Dr. oec. Hendrik Butemann

Models and solution methods for the long-term electricity production planning of a flexible biogas plant considering wear and tear

One of the most important measures against climate change is the shift from fossil to renewable energies. Many countries have therefore made it their goal to increase the share of renewable energies for electricity generation. In Germany, the share in 2019 was 40.2%, of which biomass accounted for 20.6%. This category includes biogas plants, which, unlike other sources of renewable energy, have the advantage of not being dependent on certain weather conditions. They are considered a flexible option for electricity generation because they can produce electricity when neither the sun is shining nor the wind is blowing. When the first biogas plants were put into operation, revenues from electricity production could be maximized by having the combined heat and power unit (CHP) associated with the biogas plant generate electricity continuously. To take advantage of the flexibility of biogas plants, German legislators introduced premiums that contained incentives to produce electricity during periods of low supply from other renewable energy sources. Since then, biogas plant operators have been able to maximize their revenues when the CHP produces electricity on demand, i.e., in start-stop mode. However, a large number of starts and stops of the CHP causes altered wear and tear and must be taken into account in the long-term planning of the electricity production of a biogas plant. The aim of this dissertation is therefore to use operations research methods to develop cyclical electricity production plans for biogas plants that take into account the wear and tear of the CHP and the timing and costs of maintenance activities in order to support biogas plant operators in maximizing their revenues. For this purpose, first a classification of electricity production planning of biogas plants into the planning tasks along the biomass-based supply chain is given. Subsequently, the basics of biogas plants are explained, which include their relevance in Germany, their way of operation, service and maintenance as well as the legal framework for their operation. The research gap, which is filled by this dissertation, results from the literature review on quantitative approaches for the operation of biogas plants. It shows that there is still no research work that sufficiently addresses the wear and tear of CHP in flexible operation and the planning of maintenance activities in connection with electricity production. Therefore, a conceptual optimization model is developed that accurately replicates the non-linear wear that occurs in reality and thus enables simultaneous planning of electricity production and maintenance activities. For better applicability with standard solvers, the model is additionally linearized. A case study based on real-world data reveals that a flexible biogas plant achieves higher total revenues than a continuously operated biogas plant under the conditions prevailing in Germany, even when maintenance costs are taken into account. The conceptual optimization model is then extended to produce a cyclical plan that biogas plant operators can apply on a weekly basis. In the following chapter, a greedy heuristic for generating a starting solution as well as a genetic algorithm and a tabu search are developed with the goal of reducing the computation time when solving the extended model. For this purpose, the basics of the individual solution methods are first explained and the input data are adapted to the problem with the help of parameter tuning. An extensive numerical study, in which the input parameters electricity prices, costs for maintenance activities, wear and tear of the CHP and biogas storage capacity are varied, compares the performance of the methods with that of the extended optimization model. In all scenarios, the tabu search determines the best result in low runtime. A summary and an outlook on further research opportunities conclude the dissertation.

Dr. oec. Alexander Kressner

OP-Saalplanung (Master Surgical Scheduling)

Der OP-Saal eines Krankenhauses ist eine der bedeutendsten Ressourcen zur Wiederherstellung der Gesundheit derjenigen Patienten, die operiert werden müssen. Zugleich ist er aber auch einer der kostenintensivsten Arbeitsplätze. Im Rahmen der OP-Saalplanung wird die verfügbare OP-Saalzeit auf die operierenden Fachgebiete und somit Patienten verteilt. Ziel ist es, eine bestmögliche Patientenversorgung bei hoher Auslastung der einzelnen OP-Säle zu realisieren. Eine große Herausforderung bei der Planung stellen hierbei die hohe Variabilität von Operationszeiten sowie das zufällige Eintreffen von Notfallpatienten dar. Werden diese Rahmenbedingungen bei der Planung nicht beachtet, kann dies in über- oder unterausgelasteten Kapazitäten resultieren und den Patientenservice erheblich beeinträchtigen. Hinzu kommt, dass der OP-Saalplan die Arbeitslast nachfolgender Abteilungen (z.B. Intensiv- oder Normalstationen) maßgeblich bestimmt. Eine gute Planung sollte diesem Sachverhalt in ausreichender Weise Rechnung tragen.

Vor diesem Hintergrund beschäftigt sich das Dissertationsprojekt mit der Entwicklung eines integrativ geprägten Ansatzes zur mittelfristigen OP-Saalplanung (Master Surgical Scheduling). Es wird auf bestehende Ansätze aufgesetzt, um diese anschließend gezielt um stochastische Aspekte zu erweitern. Hierbei kommen insbesondere Methoden des Operations Research wie stochastische Programmierung und Simulation zum Einsatz.

Das Forschungsprojekt wird durch ein Stipendium der Landesgraduiertenförderung Baden-Württemberg gefördert.

Dr. oec. Magdalene Friedemann-Scherbacher

Enumerative Konzepte bei der Lösung ganzzahliger linearer Optimierungsprobleme.

Dr. oec. Johannes Stärk

Vorproduktionsprozesse bei der Durchführung von Großprojekten in der Auftragseinzelfertigung – Planung und Steuerung der Vorfertigung.

Das Ziel dieses Dissertationsprojektes war es, einen idealtypischen, sowohl theoretisch fundierten als auch an der Praxis orientierten Vorproduktionsprozess für Auftragseinzelfertiger zu erstellen. Durch die Beachtung von Integration, Standardisierung und Parallelisierung werden Kostensenkung, Zeitersparnis und Qualitätserhöhung im gesamten Erstellungsprozess erreicht.

Dr. rer. pol. Ivan Žulj

Order picking has been identified as a crucial factor for the competitiveness of a supply chain because inadequate order picking performance causes customer dissatisfaction and high costs. This dissertation aims at designing new models and algorithms to improve order picking operations and to support managerial decisions on facing current challenges in order picking.

First, we study the standard order batching problem (OBP) to optimize the batching of customer orders with the objective of minimizing the total length of order picking tours. We present a mathematical model formulation of the problem and develop a hybrid solution approach of an adaptive large neighborhood search and a tabu search method. In numerical studies, we conduct an extensive comparison of our method to all previously published OBP methods that used standard benchmark sets to investigate their performance. Our hybrid outperforms all comparison methods with respect to average solution quality and runtime. Compared to the state-of-the-art, the hybrid shows the clearest advantages on the larger instances of the existing benchmark sets, which assume a larger number of customer orders and larger capacities of the picking device. Finally, our method is able to solve newly generated large-scale instances with up to 600 customer orders and six items per customer order with reasonable runtimes and convincing scaling behavior and robustness.

Next, we address a problem based on a practical case, which is inspired by a warehouse of a German manufacturer of household products. In this warehouse, heavy items are not allowed to be placed on top of light items during picking to prevent damage to the light items. Currently, the case company determines the sequence for retrieving the items from their storage locations by applying a simple S-shape strategy that neglects this precedence constraint. As a result, order pickers place the collected items next to each other in plastic boxes and sort the items respecting the precedence constraint at the end of the order picking process. To avoid this sorting, we propose a picker routing strategy that incorporates the precedence constraint by picking heavy items before light items, and we develop an exact solution method to evaluate the strategy. We assess the performance of our strategy on a dataset provided to us by the manufacturer. We compare our strategy to the strategy used in the warehouse of the case company, and to an exact picker routing approach that does not consider the given precedence constraint. The results clearly demonstrate the convincing performance of our strategy even if we compare our strategy to the exact solution method that neglects the precedence constraint.

Last, we investigate a new order picking problem, in which human order pickers of the traditional picker-to-parts setup are supported by automated guided vehicles (AGVs). We introduce two mathematical model formulations of the problem, and we develop a heuristic to solve the NP-hard problem. In numerical studies, we assess the solution quality of the heuristic in comparison to optimal solutions. The results demonstrate the ability of the heuristic in finding high-quality solutions within a negligible computation time. We conduct several computational experiments to investigate the effect of different numbers of AGVs and different traveling and walking speed ratios between AGVs and order pickers on the average total tardiness. The results of our experiments indicate that by adding (or removing) AGVs or by increasing (or decreasing) the AGV speed to adapt to different workloads, a large number of customer orders can be completed until the respective due date.