Production management and logistics

Development of a Cost Optimal Predictive Maintenance Strategy

Christoph Weeber, Technical University of Munich (Master thesis)
Junior Management Science 9(3), 2024, 1805-1835

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.

Keywords: machine learning algorithm; NASA CMAPSS dataset; optimal maintenance strategy; predictive maintenance; preventive maintenance; reactive maintenance.

DOI: https://doi.org/10.5282/jums/v9i3pp1805-1835

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Cost Allocation in Vehicle Routing Problems with Time Windows

Federico Arroyo, Technical University of Munich (Master thesis)
Junior Management Science 9(1), 2024, 1241-1268

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’ capacity, customers’ 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.

Keywords: collaborative vehicle routing; cost allocation; Shapley value method; structured random sampling; time windows.

DOI: https://doi.org/10.5282/jums/v9i1pp1241-1268

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The organization of future production work – Requirements and technical solution approaches

Jan Felix Csavajda, University of Stuttgart (Bachelor thesis)
Junior Management Science 7(4), 2022, 1032-1097

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.

Keywords: Industry 4.0; production; organization; requirements; performance indicators.

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The Impact of Sustainable Supply Chain Management on Corporate Performance – An Empirical Analysis of Manufacturing and Processing Companies in Germany

Sören Schwulera, University of Göttingen (Master thesis)
Junior Management Science 7(3), 2022, 756-801

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.

Keywords: Sustainable Supply Chain Management; Unternehmensperformance; Tripple Bottom Line; Partial Least Squares Strukturgleichungsmodellierung.

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Regionality in Electricity Tariffs from the Energy Supply Companies‘ Perspective – A Qualitative Content Analysis on Regional Electricity in Germany

Jonathan Müller, Karlsruhe Institute of Technology (Bachelor thesis)
Junior Management Science 7(1), 2022, 67-102

Keywords: Regional electricity; energy sector; energy transition; German electricity network.

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Multi-Period Optimization of the Refuelling Infrastructure for Alternative Fuel Vehicles

Alexander Böhle, Karlsruhe Institute of Technology (Bachelor thesis)
Junior Management Science 6(4), 2021, 790-825

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’s 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.

Keywords: Alternative fuel vehicle; refuelling infrastructure; optimal location; multi-period; fuel station.

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Stochastic Optimization of Bioreactor Control Policies Using a Markov Decision Process Model

Quirin Stockinger, Technical University of Munich (Master thesis)
Junior Management Science 5(1), 2020, 50-80

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 purification systems under these uncertainties, a stochastic, dynamic approachisnecessary. This thesis provides such a model by extending an existing discret estate-space, infinite 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 profit 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 purification control aids decision making. However, the model assumptions pose room for further studies.

Keywords: Markov decision process; biopharmaceuticals production; fermentation uncertainty; chromatography resin; stochastic performance decay.

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Designing and Scheduling Cost-Efficient Tours by Using the Concept of Truck Platooning

Florian Stehbeck, Technical University of Munich (Master thesis)
Junior Management Science 4(4), 2019, 566-634

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’s 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.

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).

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.

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’ highly favorable character in terms of solution quality and processing time.

Keywords: autonomous transport; Truck Platooning; driving time and rest periods; cost-efficient routing & scheduling; computational efficiency.

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State-of-the-Art dynamischer Methoden zur multikriteriellen Entscheidungsunterstützung

Sebastian Schär, University of Göttingen (Bachelor thesis)
Junior Management Science 3(3), 2018, 146-165

Die Methoden der multikriteriellen Entscheidungsunterstützung (MCDA) bieten die Möglichkeit eine Vielzahl an Kriterien unterschiedlicher Natur im Zuge der Entscheidungsfindung simultan einzubeziehen. Bestimmte Entscheidungen, insbesondere im strategischen Bereich, zeichnen sich zudem durch eine hohe Komplexität aus, da die zugrundeliegenden Annahmen sowie die Auswirkungen der Entscheidung mit Unsicherheiten behaftet sind.
Das Ziel dieser Arbeit war es, durch ein strukturiertes Literaturreview herauszustellen, welche Ansätze zur Erfassung einer solchen dynamischen Entscheidungskomponente es bislang gibt.
Zur 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 anschließend nach dem zugrundeliegenden Verständnis der dynamischen Komponente, sowie deren methodischer Erfassung klassifiziert. Hierbei offenbarten sich drei Gruppen von Ansätzen Dynamik in die MCDA zu integrieren: (1) Szenario-basierte Ansätze, (2) Eine Kombination von MCDA mit Lebenszyklusmodellen (LCA), sowie (3) die direkte Einbeziehung von Dynamik in der Problemformulierung über mehrere Datensätze (DMCDA).
Ein kritischer Vergleich dieser zeigt eine fortgeschrittene Entwicklung mit vielen Anwendungsbeispielen im Forschungsstrang der Szenario-basierten Ansätze. Eine Kombination von MCDA mit LCA kommt vor allem in Nachhaltigkeitsfragen und bei der Beurteilung von Energietechnologien zum Einsatz. Das Gebiet der DMCDA-Ansätze erweist sich als vergleichsweise jüngerer Forschungsstrang mit Ansatzpunkten für zukünftige Forschungsvorhaben.

Keywords: Multikriterielle Entscheidungsunterstützung, DMCDA, uncertainty,
dynamic decision making, MADM

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