{"id":34121,"date":"2020-03-03T13:30:36","date_gmt":"2020-03-03T13:30:36","guid":{"rendered":"https:\/\/jums.academy\/production-management-and-logistics\/"},"modified":"2025-09-03T15:04:56","modified_gmt":"2025-09-03T15:04:56","slug":"production-management-and-logistics","status":"publish","type":"page","link":"https:\/\/jums.academy\/en\/production-management-and-logistics\/","title":{"rendered":"Production management and logistics"},"content":{"rendered":"<p><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-background-position:left top;--awb-border-sizes-top:0px;--awb-border-sizes-bottom:0px;--awb-border-sizes-left:0px;--awb-border-sizes-right:0px;--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-padding-top:20px;--awb-padding-bottom:20px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#000000;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-unboxed\" id=\"accordion-34121-1\"><div class=\"fusion-panel panel-default panel-df1970a1196920bad fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_df1970a1196920bad\"><a aria-expanded=\"false\" aria-controls=\"df1970a1196920bad\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-1\" data-target=\"#df1970a1196920bad\" href=\"#df1970a1196920bad\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">All published articles sorted by issues<\/span><\/a><\/h4><\/div><div id=\"df1970a1196920bad\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_df1970a1196920bad\"><div class=\"panel-body toggle-content fusion-clearfix\">\n<ul style=\"list-style-type: none;\">\n<!--li><a href=\"https:\/\/jums.academy\/en\/v10i4-3\/\">Junior Management Science, Volume 11, Issue 1, March 2026<\/a><\/li-->\n<li><a href=\"https:\/\/jums.academy\/en\/v10i4\/\">Junior Management Science, Volume 10, Issue 4, December 2025<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v10i3\/\">Junior Management Science, Volume 10, Issue 3, September 2025<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v10i2\/\">Junior Management Science, Volume 10, Issue 2, June 2025<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v10i1\/\">Junior Management Science, Volume 10, Issue 1, March 2025<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v9i4\/\">Junior Management Science, Volume 9, Issue 4, December 2024<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v9i3\/\">Junior Management Science, Volume 9, Issue 3, September 2024<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v9i2\/\">Junior Management Science, Volume 9, Issue 2, June 2024<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v9i1\/\">Junior Management Science, Volume 9, Issue 1, March 2024<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v8i4\/\">Junior Management Science, Volume 8, Issue 4, December 2023<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v8i3\/\">Junior Management Science, Volume 8, Issue 3, September 2023<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v8i2\/\">Junior Management Science, Volume 8, Issue 2, June 2023<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v8i1\/\">Junior Management Science, Volume 8, Issue 1, March 2023<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i5\/\">Junior Management Science, Volume 7, Issue 5, December 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i4\/\">Junior Management Science, Volume 7, Issue 4, September 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i3\/\">Junior Management Science, Volume 7, Issue 3, July 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i2\/\">Junior Management Science, Volume 7, Issue 2, June 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v7i1\/\">Junior Management Science, Volume 7, Issue 1, March 2022<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v6i4\/\">Junior Management Science, Volume 6, Issue 4, December 2021<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v6i3\/\">Junior Management Science, Volume 6, Issue 3, September 2021<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v6i2\/\">Junior Management Science, Volume 6, Issue 2, June 2021<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v6i1-2\/\">Junior Management Science, Volume 6, Issue 1, March 2021<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v5i4\/\">Junior Management Science, Volume 5, Issue 4, December 2020<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v5i3\/\">Junior Management Science, Volume 5, Issue 3, September 2020<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v5i2\/\">Junior Management Science, Volume 5, Issue 2, June 2020<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v5i1\/\">Junior Management Science, Volume 5, Issue 1, March 2020<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v4i4\/\">Junior Management Science, Volume 4, Issue 4, December 2019<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v4i3\/\">Junior Management Science, Volume 4, Issue 3, September 2019<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v4i2\/\">Junior Management Science, Volume 4, Issue 2, June 2019<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v4i1\/\">Junior Management Science, Volume 4, Issue 1, March 2019<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v3i4\/\">Junior Management Science, Volume 3, Issue 4, December 2018<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v3i3\/\">Junior Management Science, Volume 3, Issue 3, September 2018<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v3i2\/\">Junior Management Science, Volume 3, Issue 2, June 2018<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v3i1\/\">Junior Management Science, Volume 3, Issue 1, March 2018<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v2i3\/\">Junior Management Science, Volume 2, Issue 3, December 2017<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v2i2\/\">Junior Management Science, Volume 2, Issue 2, September 2017<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v2i1\/\">Junior Management Science, Volume 2, Issue 1, June 2017<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v1i2\/\">Junior Management Science, Volume 1, Issue 2, December 2016<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/en\/v1i1\/\">Junior Management Science, Volume 1, Issue 1, June 2016<\/a><\/li>\n<\/ul>\n<\/div><\/div><\/div><\/div><\/div>\n<div class=\"fusion-title title fusion-title-1 sep-underline sep-solid fusion-title-text fusion-title-size-five\" style=\"--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-sep-color:#000000;\"><h5 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;--fontSize:18;--minFontSize:18;line-height:1.38;\"><\/h5><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-1 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#000000;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#ffffff;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-unboxed\" id=\"accordion-34121-2\"><div class=\"fusion-panel panel-default panel-5303557bbfc411118 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_5303557bbfc411118\"><a aria-expanded=\"false\" aria-controls=\"5303557bbfc411118\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-2\" data-target=\"#5303557bbfc411118\" href=\"#5303557bbfc411118\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">All published articles sorted by areas of business<\/span><\/a><\/h4><\/div><div id=\"5303557bbfc411118\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_5303557bbfc411118\"><div class=\"panel-body toggle-content fusion-clearfix\">\n<ul style=\"list-style-type: none;\">\n<li><a href=\"https:\/\/jums.academy\/banken \">Banken und Finanzierung<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/steuerlehre \">Betriebswirtschaftliche Steuerlehre<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/controlling \">Controlling <\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/nachhaltigkeit \">Ethik und Nachhaltigkeit in der BWL<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/rechnungswesen\">Externes Rechnungswesen und Wirtschaftspr\u00fcfung<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/familienunternehmen\/\">Familienunternehmen und Unternehmerfamilien<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/marketing \">Marketing <\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/personalwesen \">Personalwesen und Leadership<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/logistik \">Produktionswirtschaft und Logistik<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/strategie\">Strategie und Organisation<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/technologie\">Technologie, Innovation und Entrepreneurship<\/a><\/li>\n<li><a href=\"https:\/\/jums.academy\/wirtschaftsinformatik\">Wirtschaftsinformatik<\/a><\/li>\n<\/ul>\n<\/div><\/div><\/div><\/div><\/div>\n<div class=\"fusion-title title fusion-title-2 sep-underline sep-solid fusion-title-text fusion-title-size-five\" style=\"--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-sep-color:#000000;\"><h5 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;--fontSize:18;--minFontSize:18;line-height:1.38;\"><\/h5><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-2 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-builder-row fusion-builder-row-inner fusion-row\"><div class=\"fusion-layout-column fusion_builder_column_inner fusion-builder-nested-column-0 fusion_builder_column_inner_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-clearfix\"><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-1\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-3 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-builder-row fusion-builder-row-inner fusion-row\"><div class=\"fusion-layout-column fusion_builder_column_inner fusion-builder-nested-column-1 fusion_builder_column_inner_3_4 3_4 fusion-three-fourth fusion-column-first\" style=\"--awb-bg-size:cover;width:75%;width:calc(75% - ( ( 4% ) * 0.75 ) );margin-right: 4%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-column-wrapper-legacy\"><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;margin-top:7px;width:100%;\"><div class=\"fusion-separator-border sep-double\" style=\"--awb-height:20px;--awb-amount:20px;--awb-sep-color:#ffffff;border-color:#ffffff;border-top-width:0px;border-bottom-width:0px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-text fusion-text-2\"><h2>Production management and logistics<\/h2>\n<\/div><div class=\"fusion-text fusion-text-3\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column_inner fusion-builder-nested-column-2 fusion_builder_column_inner_1_4 1_4 fusion-one-fourth fusion-column-last\" style=\"--awb-bg-size:cover;width:25%;width:calc(25% - ( ( 4% ) * 0.25 ) );\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-column-wrapper-legacy\"><div class=\"fusion-fa-align-center\"><i class=\"fb-icon-element-1 fb-icon-element fontawesome-icon icon-fachbereicheProduktionswirtschaft-Logistik_schwarz circle-no\" style=\"--awb-iconcolor:#000000;--awb-iconcolor-hover:#000000;--awb-font-size:90px;\"><\/i><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-4 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last fusion-column-no-min-height\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div style=\"width: 2px; height: 15px;\"><\/div><div class=\"fusion-title title fusion-title-3 sep-underline sep-solid fusion-title-text fusion-title-size-five\" style=\"--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-sep-color:#000000;\"><h5 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;--fontSize:18;--minFontSize:18;line-height:1.38;\"><\/h5><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><\/div><\/div><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-2 nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-margin-bottom:100px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-5 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-4\"><p><a name=\"A9\"><\/a><\/p>\n<\/p>\n<h5 style=\"text-align: left;\">Numerical Studies for the Scheduling of Continuous Annealing Lines<\/h5>\n<\/p>\n<p>Hagen Alexander H\u00f6nerloh, Leibniz University Hannover (Bachelor thesis)<\/p>\n<p>Junior Management Science 10(3), 2025, 781-809<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-3\"><div class=\"fusion-panel panel-default panel-55f3f86045daf7a83 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_55f3f86045daf7a83\"><a aria-expanded=\"false\" aria-controls=\"55f3f86045daf7a83\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-3\" data-target=\"#55f3f86045daf7a83\" href=\"#55f3f86045daf7a83\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"55f3f86045daf7a83\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_55f3f86045daf7a83\"><div class=\"panel-body toggle-content fusion-clearfix\">The continuous annealing of flat steel improves its properties for applications such as automotive manufacturing. Scheduling these processes on Parallel Heterogeneous Annealing Lines (PHALs) is complex due to diverse coil properties, incompatible process modes, and due date constraints. Introducing stringers to address incompatibilities between steel sheets raises costs, energy use, and CO2 emissions, highlighting the need for optimized scheduling. This thesis implements a mathematical model in Python using the Gurobi solver to optimize PHAL scheduling by minimizing stringer usage while meeting tardiness constraints. The model is extended to include coil-specific release dates and expanded to address trade-offs between stringer use, tardiness, and due date deviations, including earliness. A computational study evaluates the model under various scenarios, examining the effects of coil heterogeneity, urgency, process flexibility, and stringer processing times. Results show that optimized schedules reduce stringer use and delays, particularly under high process flexibility. These findings demonstrate the potential of optimization to improve efficiency and sustainability in steel production while guiding future research in dynamic scheduling approaches.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-5\"><p><em>Keywords: continuous annealing lines; Gurobi solver; scheduling optimization; steel industry; stringer minimization.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-6\"><p><em>DOI: <a href=\"https:\/\/www.doi.org\/10.5282\/jums\/v10i3pp781-809\">https:\/\/www.doi.org\/10.5282\/jums\/v10i3pp781-809<\/a><\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-1 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2025\/09\/BA_Hoenerloh.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Article<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-2 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2025\/09\/BA_Hoenerloh_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-3 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/www.doi.org\/10.5282\/jums\/v10i3pp781-809\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite Article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-6 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-7\"><p><a name=\"A8\"><\/a><\/p>\n<\/p>\n<h5 style=\"text-align: left;\">Modeling the Impact of Emission Credit Systems on Automotive Product Portfolios: A Mathematical Analysis of Policy Effects in Europe, China, and the U.S. Under Different Demand Scenarios<\/h5>\n<\/p>\n<p>Zewei Shi, Technical University of Munich (Master thesis)<\/p>\n<p>Junior Management Science 10(3), 2025, 748-780<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-4\"><div class=\"fusion-panel panel-default panel-89efceb522da8e01f fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_89efceb522da8e01f\"><a aria-expanded=\"false\" aria-controls=\"89efceb522da8e01f\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-4\" data-target=\"#89efceb522da8e01f\" href=\"#89efceb522da8e01f\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"89efceb522da8e01f\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_89efceb522da8e01f\"><div class=\"panel-body toggle-content fusion-clearfix\">In the midst of the global climate crisis, governments worldwide have implemented a range of emission policies aimed at encouraging more production of the environmentally friendly vehicle. However, the exact impact of these policies on automakers\u2019 production portfolios and profitability remains uncertain and challenging to anticipate. This paper presents a comprehensive analysis of three major emission regulation policies enacted by the European Union (EU), China, and the United States (U.S.), evaluating their influence on car manufacturers. Leveraging a mathematical model, this paper adopt the perspective of individual manufacturers seeking to maximize revenue, delving into the intricacies of these policies. Furthermore, this article conduct sensitivity and factorial analyses to assess the impact of policy parameters. The findings reveal that all three major emission policies contribute to an increase in the production of low-emission vehicles. However, China\u2019s policy has the least impact on manufacturers\u2019 profits and relies more on market demand to reduce the average carbon fleet emissions compared to the policies in the EU and the U.S. In conclusion, this paper underscores that different policy systems yield varying profit outcomes for manufacturers, necessitating adjustments to production portfolios for sustained profitability and the significance of mathematical models in aiding manufacturers\u2019 understanding of evolving policies and making informed predictions in a dynamic regulatory landscape.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-8\"><p><em>Keywords: automotive production; green transition; international emission policies; regulatory impact; sustainability.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-9\"><p><em>DOI: <a href=\"https:\/\/www.doi.org\/10.5282\/jums\/v10i3pp748-780\">https:\/\/www.doi.org\/10.5282\/jums\/v10i3pp748-780<\/a><\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-4 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2025\/09\/MA_Shi.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Article<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-5 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2025\/09\/MA_Shi_Appendix.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-6 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/www.doi.org\/10.5282\/jums\/v10i3pp748-780\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite Article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-7 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-10\"><p><a name=\"A8\"><\/a><\/p>\n<\/p>\n<h5 style=\"text-align: left;\">Waiting Time Estimation for Ride-Hailing Fleets Using Graph Neural Networks<\/h5>\n<\/p>\n<p>Hashmatullah Sadid, Technical University of Munich (Master thesis)<\/p>\n<p>Junior Management Science 10(2), 2025, 462-490<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-5\"><div class=\"fusion-panel panel-default panel-7290d3ccff27749c2 fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_7290d3ccff27749c2\"><a aria-expanded=\"false\" aria-controls=\"7290d3ccff27749c2\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-5\" data-target=\"#7290d3ccff27749c2\" href=\"#7290d3ccff27749c2\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"7290d3ccff27749c2\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_7290d3ccff27749c2\"><div class=\"panel-body toggle-content fusion-clearfix\">Ride-hailing services are part of intermodal transport systems, allowing passengers to use various transport modes for their trip. The optimal choice for a request in the intermodal system depends on the passenger\u2019s waiting time for the ride-hailing service. Estimating this waiting time is crucial for efficient system operation. The prediction of waiting time depends on the spatial dependency of the transport network and traffic flow elements. Graph neural network (GNN) approaches have gained attention for capturing spatial dependencies in various applications, though less attention has been given to ride-hailing waiting time prediction. The aim of this master thesis is to implement a GNN-based method to predict waiting time for ride-hailing requests in the network. Simulation-based waiting time data is used for model training and validation. MATSim is chosen for generating waiting time data under different demand and supply scenarios. Graph Convolutional Network (GCN) and Gated Attention Network (GAT) are used as prediction models. Regression and MLP methods are used as baselines to compare model performance. Results show GCN outperforms regression by 15%, while GAT performs 14% better than regression.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-11\"><p><em>Keywords: graph convolutional network; ride-hailing service; waiting time estimation.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-12\"><p><em>DOI: <a href=\"https:\/\/www.doi.org\/10.5282\/jums\/v10i2pp462-490\">https:\/\/www.doi.org\/10.5282\/jums\/v10i2pp462-490<\/a><\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-7 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2025\/06\/MA_Sadid.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Article<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-8 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/www.doi.org\/10.5282\/jums\/v10i2pp462-490\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-8 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-13\"><p><a name=\"A9\"><\/a><\/p>\n<\/p>\n<h5 style=\"text-align: left;\">Development of a Cost Optimal Predictive Maintenance Strategy<\/h5>\n<p>Christoph Weeber, Technical University of Munich (Master thesis)<br \/>Junior Management Science 9(3), 2024, 1805-1835<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-6\"><div class=\"fusion-panel panel-default panel-a20b2d015f21839ca fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_a20b2d015f21839ca\"><a aria-expanded=\"false\" aria-controls=\"a20b2d015f21839ca\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-6\" data-target=\"#a20b2d015f21839ca\" href=\"#a20b2d015f21839ca\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"a20b2d015f21839ca\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_a20b2d015f21839ca\"><div class=\"panel-body toggle-content fusion-clearfix\">Maintenance costs account for a significant share of operating expenses. Selecting the optimal maintenance strategy for each application is crucial to optimize operational processes and minimize MRO spending. In recent years, Machine Learning has become popular for analyzing large amounts of data and improving decision-making in various industries. This yields great potential in the field of Predictive Maintenance. In this thesis, a methodology to determine and compare the average maintenance costs per cycle for Reactive, Preventive, and Predictive Maintenance, as well as a Reference Case is developed. This cost comparison methodology is then applied to a realistic example of a fleet of ten aircraft. Unlike previous research, this thesis combines all aspects in one approach, from Machine Learning algorithm selection and RUL prediction, to the maintenance cost comparison based on a fleet of aircraft. The NASA CMAPSS jet engine dataset is used as an example. Results suggest that maintenance costs per cycle for Predictive Maintenance are 36.0 % lower than for Preventive Maintenance and 88.3 % lower compared to Reactive Maintenance. In general, this thesis serves as a guideline that highlights the necessary steps to determine the cost-optimal maintenance strategy for an application.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-14\"><p><em>Keywords: machine learning algorithm; NASA CMAPSS dataset; optimal maintenance strategy; predictive maintenance; preventive maintenance; reactive maintenance.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-15\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v9i3pp1805-1835\">https:\/\/doi.org\/10.5282\/jums\/v9i3pp1805-1835<\/a><\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-9 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2024\/09\/MA_Weeber.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Article<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-10 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2024\/09\/A_Weeber.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-11 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v9i3pp1805-1835\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-9 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-16\"><p><a name=\"A6\"><\/a><\/p>\n<\/p>\n<h5 style=\"text-align: left;\">Cost Allocation in Vehicle Routing Problems with Time Windows<\/h5>\n<p>Federico Arroyo, Technical University of Munich (Master thesis)<br \/>Junior Management Science 9(1), 2024, 1241-1268<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-7\"><div class=\"fusion-panel panel-default panel-e4b3906a83cc0eb3f fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_e4b3906a83cc0eb3f\"><a aria-expanded=\"false\" aria-controls=\"e4b3906a83cc0eb3f\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-7\" data-target=\"#e4b3906a83cc0eb3f\" href=\"#e4b3906a83cc0eb3f\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read Abstract<\/span><\/a><\/h4><\/div><div id=\"e4b3906a83cc0eb3f\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_e4b3906a83cc0eb3f\"><div class=\"panel-body toggle-content fusion-clearfix\">The estimation of costs allocated to each customer when serving them in a collaborative logistic operation is a complex problem whose solution is computationally very expensive. In this work the case of central horizontal collaboration for vehicle routing problems with time windows and a central depot is studied. An approximation to the Shapley value method via structured random sampling is used to calculate the cost associated with customers in Solomon instances. Such costs are regressed to a linear model with a set of defined features. The results show that cost can be predicted with considerable accuracy with few features. Moreover, the extent to which vehicles\u2019 capacity, customers\u2019 demand and distance, the degree of customer clustering and time window horizons affect cost and potential savings from carriers in collaboration is assessed. Additionally, individual regression models of different set of instances show how various pricing strategies for customers can be fitted to their classification when grouping them.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-17\"><p><em>Keywords: collaborative vehicle routing; cost allocation; Shapley value method; structured random sampling; time windows.<\/em><\/p>\n<\/div><div class=\"fusion-text fusion-text-18\"><p><em>DOI: <a href=\"https:\/\/doi.org\/10.5282\/jums\/v9i1pp1241-1268\">https:\/\/doi.org\/10.5282\/jums\/v9i1pp1241-1268<\/a><\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-12 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2024\/03\/MA_Arroyo.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Article<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-13 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2024\/03\/A_Arroyo.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read Appendix<\/span><\/a><\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-14 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/doi.org\/10.5282\/jums\/v9i1pp1241-1268\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Cite article<\/span><\/a><\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-10 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-19\"><p><a name=\"A7\"><\/a><\/p>\n<h5 style=\"text-align: left;\">The organization of future production work \u2013 Requirements and technical solution approaches<\/h5>\n<p>Jan Felix Csavajda, University of Stuttgart (Bachelor thesis)<br \/>\nJunior Management Science 7(4), 2022, 1032-1097<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-8\"><div class=\"fusion-panel panel-default panel-d3d607fd773bb611c fusion-toggle-no-divider\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_d3d607fd773bb611c\"><a aria-expanded=\"false\" aria-controls=\"d3d607fd773bb611c\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-8\" data-target=\"#d3d607fd773bb611c\" href=\"#d3d607fd773bb611c\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read abstract<\/span><\/a><\/h4><\/div><div id=\"d3d607fd773bb611c\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_d3d607fd773bb611c\"><div class=\"panel-body toggle-content fusion-clearfix\">\n<p>With a view to the industrial production of the future and Industry 4.0, the focus mostly lies on technology, while organization and the role of humans are less considered. The aim of this paper is to determine organizational requirements and performance indicators for future production. A quantitative empirical study is used to evaluate the relevance from a practical perspective. In addition, a collection of technological solutions illustrates the exemplary practical implementation of the organizational requirements. As a result, an ideal-typical organization of future production is presented. A central finding is that so far little attention is drawn to the areas of organization and humans in the context of Industry 4.0. Modern work and leadership concepts, consequent employee qualification, Lean Management 4.0, improved coordination, connectivity and transparency as well as the use of performance indicators are essential. For the successful implementation of technical Industry 4.0 solutions, the primary establishment of a basic organizational framework is mandatory. This work clarifies which concepts should be in the foreground in the future, also in order to secure competitiveness.<\/p>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-20\"><p><em>Keywords: Industry 4.0; production; organization; requirements; performance indicators.<\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-15 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2022\/09\/BA_Csavajda.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read article<\/span><\/a><\/div><div class=\"fusion-text fusion-text-21\" style=\"--awb-line-height:0.1;\"><p style=\"text-align: right; margin-top: 25px;\"><a href=\"https:\/\/jums.academy\/en\/j-csavajda\/\">Go to article page<\/a><\/p>\n<\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-11 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-22\"><p><a name=\"A8\"><\/a><\/p>\n<h5 style=\"text-align: left;\">The Impact of Sustainable Supply Chain Management on Corporate Performance \u2013 An Empirical Analysis of Manufacturing and Processing Companies in Germany<\/h5>\n<p>S\u00f6ren Schwulera, University of G\u00f6ttingen (Master thesis)<br \/>\nJunior Management Science 7(3), 2022, 756-801<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-9\"><div class=\"fusion-panel panel-default panel-6b25d0ddc7448da7b fusion-toggle-no-divider\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_6b25d0ddc7448da7b\"><a aria-expanded=\"false\" aria-controls=\"6b25d0ddc7448da7b\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-9\" data-target=\"#6b25d0ddc7448da7b\" href=\"#6b25d0ddc7448da7b\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read abstract<\/span><\/a><\/h4><\/div><div id=\"6b25d0ddc7448da7b\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_6b25d0ddc7448da7b\"><div class=\"panel-body toggle-content fusion-clearfix\">\n<p>Companies implement Sustainable Supply Chain Management (SSCM) practices to remain competitiveness not only on the economic, but also on the environmental and social levels of the Tripple Bottom Line (TBL). The aim of this paper was to empirically investigate the impact of SSCM practices on the economic, environmental, and the social level of corporate performance of manufacturing and processing companies. In order to achieve this goal, a theoretical research model was set up based on relevant literature with four internal and four external SSCM practices, each of them was expected to have a positive effect on all levels of corporate performance. After an online survey of the 500 biggest manufacturing and processing companies in Germany measured by turnover, 61 questionnaires were evaluated using partial least squares structural equation modelling. In total, 10 of the 28 expected positive effects of internal and external SSCM practices on the three levels of corporate performance could be confirmed. This paper provides a theoretical research model for further studies and supports manager in companies in case of implementation of SSCM practices.<\/p>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-23\"><p><em>Keywords: Sustainable Supply Chain Management; Unternehmensperformance; Tripple Bottom Line; Partial Least Squares Strukturgleichungsmodellierung.<\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-16 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2022\/07\/MA_Schwulera.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read article<\/span><\/a><\/div><div class=\"fusion-text fusion-text-24\" style=\"--awb-line-height:0.1;\"><p style=\"text-align: right; margin-top: 25px;\"><a href=\"https:\/\/jums.academy\/en\/s-schwulera\/\">Go to article page<\/a><\/p>\n<\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-12 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-25\"><p><a name=\"A3\"><\/a><\/p>\n<h5 style=\"text-align: left;\">Regionality in Electricity Tariffs from the Energy Supply Companies\u2018 Perspective \u2013 A Qualitative Content Analysis on Regional Electricity in Germany<\/h5>\n<p>Jonathan M\u00fcller, Karlsruhe Institute of Technology (Bachelor thesis)<br \/>\nJunior Management Science 7(1), 2022, 67-102<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-10\"><div class=\"fusion-panel panel-default panel-1e9b035c92a211938 fusion-toggle-no-divider\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_1e9b035c92a211938\"><a aria-expanded=\"false\" aria-controls=\"1e9b035c92a211938\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-10\" data-target=\"#1e9b035c92a211938\" href=\"#1e9b035c92a211938\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read abstract<\/span><\/a><\/h4><\/div><div id=\"1e9b035c92a211938\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_1e9b035c92a211938\"><div class=\"panel-body toggle-content fusion-clearfix\">\n<div id=\"docs-chrome\" class=\"docs-material companion-enabled\" tabindex=\"0\" role=\"banner\" aria-label=\"Men\u00fcleiste\">\n<div id=\"docs-additional-bars\">\n<div id=\"formula-bar-name-box-wrapper\" class=\"name-box-enabled formula-bar-with-name-box-wrapper\">\n<div id=\"formula-bar\" class=\"formula-bar\">\n<div id=\"t-formula-bar-input-container\">\n<div dir=\"ltr\">\n<div id=\"t-formula-bar-input\" spellcheck=\"false\">\n<div class=\"cell-input\" dir=\"ltr\" tabindex=\"0\" role=\"combobox\" contenteditable=\"true\" aria-autocomplete=\"list\">The new Registry of Guarantees of Regional Origin (GRO), established on 01.01.2019, enables electricity producers to give electricity deliveries proof of regionality, which in turn can be used by electricity suppliers as proof of regionality to end customers. However, still unknown are the advantages and disadvantages the concept of regional electricity has from the energy supplier\u2019s perspective. To address this research gap seventeen expert interviews with energy supply companies of different sizes throughout Germany were conducted. The interviews shed light on the concept of regional electricity from different perspectives, taking into account both the advantages and the respective points of criticism, mainly from the interviewed experts\u2019 point of view. The work places special attention on the sales side as well as on the purchasing side of regional electricity in Germany. Most of the experts had a very positive attitude towards regional electricity and are already implementing it or plan to do in the near future. However, there is a wish for adjustment of the regulations otherwise it cannot expand from being a niche product. This includes a request for financial improvement to increase its competitiveness thereby serving as an acceptance tool for the energy policy turnaround.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-26\"><p><em>Keywords: Regional electricity; energy sector; energy transition; German electricity network.<\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-17 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2022\/03\/BA_Mueller.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read article<\/span><\/a><\/div><div class=\"fusion-text fusion-text-27\" style=\"--awb-line-height:0.1;\"><p style=\"text-align: right; margin-top: 25px;\"><a href=\"https:\/\/jums.academy\/en\/j-mueller\/\">Go to article page<\/a><\/p>\n<\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-13 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-28\"><p><a name=\"A4\"><\/a><\/p>\n<h5 style=\"text-align: left;\"><strong>Multi-Period Optimization of the Refuelling Infrastructure for Alternative Fuel Vehicles<\/strong><\/h5>\n<p>Alexander B\u00f6hle, Karlsruhe Institute of Technology (Bachelor thesis)<br \/>\nJunior Management Science 6(4), 2021, 790-825<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-11\"><div class=\"fusion-panel panel-default panel-4754be01c6b994c0c fusion-toggle-no-divider\" style=\"--awb-title-color:#333333;--awb-content-color:#333333;\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_4754be01c6b994c0c\"><a aria-expanded=\"false\" aria-controls=\"4754be01c6b994c0c\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-11\" data-target=\"#4754be01c6b994c0c\" href=\"#4754be01c6b994c0c\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read abstract<\/span><\/a><\/h4><\/div><div id=\"4754be01c6b994c0c\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_4754be01c6b994c0c\"><div class=\"panel-body toggle-content fusion-clearfix\">\n<p>Alternative fuel vehicles (AFV) are gaining increasing attention as a mean to reduce greenhouse gas (GHG) emissions. One of the most critical barriers to the widespread adoption of AFVs is the lack of sufficient refuelling infrastructure. Although it is expected, that an adequate number of alternative fuel stations (AFS) will eventually be constructed, due to the high resource intensity of infrastructure development, an optimal step-by-step construction plan is needed. For such a plan to be actionable, it is necessary, that the underlying model considers realistic station sizes and budgetary limitations. This bachelor thesis addresses this issue by introducing a new formulation of the flow-refuelling location model, that combines multi-periodicity and node capacity restrictions (MP-NC FRLM). For this purpose, the models of Capar and Kluschke have been extended, and the pre-generation process of sets and variables has been improved. The thesis furthermore adapts and applies the two evaluation concepts Value of the Multi-Period Solution (VMPS) and Value of Multi-Period Planning (VMPP) to assess the model\u2019s relative additional benefit over static counterparts. Besides, several hypotheses about potential drivers of the two evaluation concepts VMPS and VMPP have been made within the scope of a numerical experiment, to help central planners identify situations, where the additional complexity of a dynamic model would be worthwhile. While the MP-NC FRLM has proven to provide additional benefit over static counterparts, it comes at the cost of a higher solving time. The main contributor to the higher solving is hereby the incorporation of a time module.<\/p>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-29\"><p><em>Keywords: Alternative fuel vehicle; refuelling infrastructure; optimal location; multi-period; fuel station.<\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-18 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2021\/12\/BA_Boehle.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read article<\/span><\/a><\/div><div class=\"fusion-text fusion-text-30\" style=\"--awb-line-height:0.1;\"><p style=\"text-align: right; margin-top: 25px;\"><a href=\"https:\/\/jums.academy\/en\/a-boehle\/\">Go to article page<\/a><\/p>\n<\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-14 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-31\"><p><a name=\"A4\"><\/a><\/p>\n<h5 style=\"text-align: left;\"><strong>Stochastic Optimization of Bioreactor Control Policies Using a Markov Decision Process Model<br \/>\n<\/strong><\/h5>\n<p>Quirin Stockinger, Technical University of Munich (Master thesis)<br \/>\nJunior Management Science 5(1), 2020, 50-80<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-12\"><div class=\"fusion-panel panel-default panel-7814e493ae7e07c63 fusion-toggle-no-divider\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_7814e493ae7e07c63\"><a aria-expanded=\"false\" aria-controls=\"7814e493ae7e07c63\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-12\" data-target=\"#7814e493ae7e07c63\" href=\"#7814e493ae7e07c63\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read abstract<\/span><\/a><\/h4><\/div><div id=\"7814e493ae7e07c63\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_7814e493ae7e07c63\"><div class=\"panel-body toggle-content fusion-clearfix\">Biopharmaceuticals are the fastest-growing segment of the pharmaceutical industry. Their manufacture is complicated by the uncertainty exhibited therein. Scholars have studied the planning and operation of such production systems under some uncertainties, but the simultaneous consideration of fermentation and resin yield uncertainty is lacking so-far. To study the optimal operation of biopharmaceutical production and puri\ufb01cation systems under these uncertainties, a stochastic, dynamic approachisnecessary. This thesis provides such a model by extending an existing discret estate-space, in\ufb01nite horizon Markov decision process model of upstream fermentation. Tissue Plasminogen Activator fermentation and chromatography was implemented. This example was used to discuss the optimal policy for operating different fermentation setups. The average per-cycle operating pro\ufb01t of a serial setup was 1,272 $; the parallel setup produced negative average rewards. Managerial insights were derived from a comparison to a basic, titer maximizing policy and process sensitivities. In conclusion, the integrated stochastic optimization of biopharma production and puri\ufb01cation control aids decision making. However, the model assumptions pose room for further studies.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-32\"><p><em>Keywords: Markov decision process; biopharmaceuticals production; fermentation uncertainty; chromatography resin; stochastic performance decay. <\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-19 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2020\/03\/MA_Stockinger-1.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read article<\/span><\/a><\/div><div class=\"fusion-text fusion-text-33\" style=\"--awb-line-height:0.1;\"><p style=\"text-align: right; margin-top: 25px;\"><a href=\"https:\/\/jums.academy\/en\/q-stockinger\/\">Go to article page<\/a><\/p>\n<\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-15 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-34\"><p><a name=\"A5\"><\/a><\/p>\n<h5 style=\"text-align: left;\"><strong>Designing and Scheduling Cost-Efficient Tours by Using the Concept of Truck Platooning<\/strong><\/h5>\n<p>Florian Stehbeck, Technical University of Munich (Master thesis)<br \/>\nJunior Management Science 4(4), 2019, 566-634<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-13\"><div class=\"fusion-panel panel-default panel-b345e27e40acafa38 fusion-toggle-no-divider\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_b345e27e40acafa38\"><a aria-expanded=\"false\" aria-controls=\"b345e27e40acafa38\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-13\" data-target=\"#b345e27e40acafa38\" href=\"#b345e27e40acafa38\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read abstract<\/span><\/a><\/h4><\/div><div id=\"b345e27e40acafa38\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_b345e27e40acafa38\"><div class=\"panel-body toggle-content fusion-clearfix\">Truck Platooning is a promising new technology to reduce the fuel consumption by around 15% via the exploitation of a preceding and digitally connected truck\u2019s slipstream. However, the cost-efficient coordination of such platoons under consideration of mandatory EU driving time restrictions turns out to be a highly complex task.<\/p>\n<p>For this purpose, we provide a comprehensive literature review and formulate the exact EU-Truck Platooning Problem (EU-TPP) as an Integer Linear Program (ILP) which also features a hypothetical task-relieving effect for following drivers in a convoy. In order to increase the computational efficiency, we introduce an auxiliary constraint and two hierarchical planning-based matheuristic approaches: the Shortest Path Heuristic (SPH) and the Platoon Routing Heuristic (PRH).<\/p>\n<p>Besides a qualitative sensitivity analysis, we perform an extensive numerical study to investigate the impact of different critical influence factors on platooning, being of major political and economic interest.<\/p>\n<p>Our experiments with the EU-TPP suggest remarkable fuel cost savings of up to 10.83% without a 50% task relief, while its inclusion leads to additional personnel cost savings of up to even 31.86% at best with maximally 12 trucks to be coordinated in a recreated part of the European highway network. Moreover, we prove our matheuristics\u2019 highly favorable character in terms of solution quality and processing time.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-35\"><p><em>Keywords: autonomous transport; Truck Platooning; driving time and rest periods; cost-efficient routing &amp; scheduling; computational efficiency.<\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-20 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2020\/02\/MA_Stehbeck.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read article<\/span><\/a><\/div><div class=\"fusion-text fusion-text-36\"><p style=\"text-align: right;\"><a href=\"https:\/\/jums.academy\/en\/florian-stehbeck\/\">Go to article page<\/a><\/p>\n<\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-16 fusion_builder_column_1_1 1_1 fusion-one-full fusion-column-first fusion-column-last\" style=\"--awb-bg-size:cover;\"><div class=\"fusion-column-wrapper fusion-flex-column-wrapper-legacy\"><div class=\"fusion-text fusion-text-37\"><p><a name=\"A6\"><\/a><\/p>\n<h5 style=\"text-align: left;\"><strong>State-of-the-Art dynamischer Methoden zur multikriteriellen Entscheidungsunterst\u00fctzung<\/strong><\/h5>\n<p>Sebastian Sch\u00e4r, University of G\u00f6ttingen (Bachelor thesis)<br \/>\nJunior Management Science 3(3), 2018, 146-165<\/p>\n<\/div><div class=\"accordian fusion-accordian\" style=\"--awb-border-size:1px;--awb-icon-size:13px;--awb-content-font-size:16px;--awb-icon-alignment:left;--awb-hover-color:#f9f9f9;--awb-border-color:#cccccc;--awb-background-color:#ffffff;--awb-divider-color:#e0dede;--awb-divider-hover-color:#e0dede;--awb-icon-color:#ffffff;--awb-title-color:#333333;--awb-content-color:#333333;--awb-icon-box-color:#333333;--awb-toggle-hover-accent-color:#447c4d;--awb-title-font-family:&quot;Roboto Slab&quot;;--awb-title-font-weight:300;--awb-title-font-style:normal;--awb-title-font-size:16px;--awb-content-font-family:&quot;Roboto Slab&quot;;--awb-content-font-style:normal;--awb-content-font-weight:400;\"><div class=\"panel-group fusion-toggle-icon-boxed\" id=\"accordion-34121-14\"><div class=\"fusion-panel panel-default panel-7c584748006378c03 fusion-toggle-no-divider\"><div class=\"panel-heading\"><h4 class=\"panel-title toggle\" id=\"toggle_7c584748006378c03\"><a aria-expanded=\"false\" aria-controls=\"7c584748006378c03\" role=\"button\" data-toggle=\"collapse\" data-parent=\"#accordion-34121-14\" data-target=\"#7c584748006378c03\" href=\"#7c584748006378c03\"><span class=\"fusion-toggle-icon-wrapper\" aria-hidden=\"true\"><i class=\"fa-fusion-box active-icon awb-icon-minus\" aria-hidden=\"true\"><\/i><i class=\"fa-fusion-box inactive-icon awb-icon-plus\" aria-hidden=\"true\"><\/i><\/span><span class=\"fusion-toggle-heading\">Read abstract<\/span><\/a><\/h4><\/div><div id=\"7c584748006378c03\" class=\"panel-collapse collapse \" aria-labelledby=\"toggle_7c584748006378c03\"><div class=\"panel-body toggle-content fusion-clearfix\">Die Methoden der multikriteriellen Entscheidungsunterst\u00fctzung (MCDA) bieten die M\u00f6glichkeit eine Vielzahl an Kriterien unterschiedlicher Natur im Zuge der Entscheidungsfindung simultan einzubeziehen. Bestimmte Entscheidungen, insbesondere im strategischen Bereich, zeichnen sich zudem durch eine hohe Komplexit\u00e4t aus, da die zugrundeliegenden Annahmen sowie die Auswirkungen der Entscheidung mit Unsicherheiten behaftet sind.<br \/>\nDas Ziel dieser Arbeit war es, durch ein strukturiertes Literaturreview herauszustellen, welche Ans\u00e4tze zur Erfassung einer solchen dynamischen Entscheidungskomponente es bislang gibt.<br \/>\nZur Identifikation relevanter Literatur wurden themenrelevante, wissenschaftliche Verlage wie ELSEVIER, sowie die EBSCO Datenbank genutzt. Auch Dissertationen, Konferenzberichte sowie vorherige Reviewartikel wurden inkludiert. Insgesamt wurden 60 Zeitschriftenartikel aus 31 verschiedenen Zeitschriften, 6 Konferenz-Paper, 11 Buchquellen und eine Dissertation gefunden. Die Literatur wurde anschlie\u00dfend nach dem zugrundeliegenden Verst\u00e4ndnis der dynamischen Komponente, sowie deren methodischer Erfassung klassifiziert. Hierbei offenbarten sich drei Gruppen von Ans\u00e4tzen Dynamik in die MCDA zu integrieren: (1) Szenario-basierte Ans\u00e4tze, (2) Eine Kombination von MCDA mit Lebenszyklusmodellen (LCA), sowie (3) die direkte Einbeziehung von Dynamik in der Problemformulierung \u00fcber mehrere Datens\u00e4tze (DMCDA).<br \/>\nEin kritischer Vergleich dieser zeigt eine fortgeschrittene Entwicklung mit vielen Anwendungsbeispielen im Forschungsstrang der Szenario-basierten Ans\u00e4tze. Eine Kombination von MCDA mit LCA kommt vor allem in Nachhaltigkeitsfragen und bei der Beurteilung von Energietechnologien zum Einsatz. Das Gebiet der DMCDA-Ans\u00e4tze erweist sich als vergleichsweise j\u00fcngerer Forschungsstrang mit Ansatzpunkten f\u00fcr zuk\u00fcnftige Forschungsvorhaben.<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-text fusion-text-38\"><p><em>Keywords: Multikriterielle Entscheidungsunterst\u00fctzung, DMCDA, uncertainty,<br \/>\ndynamic decision making, MADM<\/em><\/p>\n<\/div><div class=\"fusion-alignright\"><a class=\"fusion-button button-flat fusion-button-default-size button-custom fusion-button-default button-21 fusion-button-default-span fusion-button-default-type\" style=\"--button_accent_color:#ffffff;--button_accent_hover_color:#ffffff;--button_border_hover_color:#96c346;--button_gradient_top_color:#447c4d;--button_gradient_bottom_color:#447c4d;--button_gradient_top_color_hover:#96c346;--button_gradient_bottom_color_hover:#96c346;\" target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\/\/jums.academy\/wp-content\/uploads\/2018\/10\/BA_Schaer.pdf\"><span class=\"fusion-button-text awb-button__text awb-button__text--default\">Read article<\/span><\/a><\/div><div class=\"fusion-text fusion-text-39\"><p style=\"text-align: right;\"><a href=\"https:\/\/jums.academy\/en\/s-schaer\/\">Go to article page<\/a><\/p>\n<\/div><div style=\"width: 2px; height: 20px;\"><\/div><div class=\"fusion-sep-clear\"><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"margin-left: auto;margin-right: auto;width:100%;\"><div class=\"fusion-separator-border sep-single sep-solid\" style=\"--awb-height:20px;--awb-amount:20px;border-color:#e0dede;border-top-width:1px;\"><\/div><\/div><div class=\"fusion-sep-clear\"><\/div><div style=\"width: 2px; height: 160px;\"><\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div>\n<\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":4889,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"class_list":["post-34121","page","type-page","status-publish","hentry"],"jetpack_shortlink":"https:\/\/wp.me\/P7lBbr-8Sl","jetpack-related-posts":[],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/pages\/34121","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/users\/4889"}],"replies":[{"embeddable":true,"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/comments?post=34121"}],"version-history":[{"count":14,"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/pages\/34121\/revisions"}],"predecessor-version":[{"id":63280,"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/pages\/34121\/revisions\/63280"}],"wp:attachment":[{"href":"https:\/\/jums.academy\/en\/wp-json\/wp\/v2\/media?parent=34121"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}