Quality is an important measurement within a semiconductor manufactory. Due to the fact that yield is directly affected by quality of the manufacturing process, in this paper a quality based scheduling approach will be presented which compares different methods like dispatching, MIP and CP, regarding different objectives. To test the different used methods a benchmark model of a semiconductor manufactory is build up. Here a lithography work center is used in detail where the rest of the fabrication is only build up as a delay station. With this model the repeatability for the example of a lithography step is investigated. Thereby in this investigation it is assumed, that each lithography tool has an offset which is transferred to the structure. Now the quality of a product should be best, if the offset from one layer to the next layer is minimized.

This thesis presents the mixed integer bilevel programming problems where some optimality conditions and solution algorithms are derived. Bilevel programming problems are optimization problems which are partly constrained by another optimization problem. The theoretical part of this dissertation is mainly based on the investigation of optimality conditions of mixed integer bilevel program. Taking into account both approaches (optimistic and pessimistic) which have been developed in the literature to deal with this type of problem, we derive some conditions for the existence of solutions. After that, we are able to discuss local optimality conditions using tools of variational analysis for each different approach. Moreover, bilevel optimization problems with semidefinite programming in the lower level are considered in order to formulate more optimality conditions for the mixed integer bilevel program. We end the thesis by developing some algorithms based on the theory presented

The main aim of this work is to provide decision makers suitable approaches for solving two crucial planning problems in the railway industry: the locomotive assignment problem and the crew scheduling problem with attendance rates. On the one hand, the focus is on practical usability and the necessary integration and consideration of real-life requirements in the planning process. On the other hand, solution approaches are to be developed, which can provide solutions of sufficiently good quality within a reasonable time by taking all these requirements into account.

This thesis presents the mixed integer bilevel programming problems where some optimality conditions and solution algorithms are derived. Bilevel programming problems are optimization problems which are partly constrained by another optimization problem. The theoretical part of this dissertation is mainly based on the investigation of optimality conditions of mixed integer bilevel program. Taking into account both approaches (optimistic and pessimistic) which have been developed in the literature to deal with this type of problem, we derive some conditions for the existence of solutions. After that, we are able to discuss local optimality conditions using tools of variational analysis for each different approach. Moreover, bilevel optimization problems with semidefinite programming in the lower level are considered in order to formulate more optimality conditions for the mixed integer bilevel program. We end the thesis by developing some algorithms based on the theory presented

Historically, electricity supply was heavily centralized and was provided by conventional thermal power plants such as coal-fired, gas, or nuclear power plants. The share of conventional power generation is being increasingly replaced by power generation from renewable sources. In Europe, the share of electricity generation from fossil fuels fell from 49% in 2011 to 37% in 2020, whereas the share from renewables increased from 22% to 38% during the same timeframe. Renewable generation is expected to rise by 10% annually to almost triple the current renewable capacity by 2030. The accelerating adoption of renewables changes the character of the electricity infrastructure from a centralized energy supply to a highly decentralized one, such that generation is moving closer to the point of demand. This change brings numerous challenges with it. This work focuses on challenges in operational planning of distributed energy resources from the perspective of so-called aggregators that are increasingly participating in energy markets. Aggregators combine different energy resources, i.e., electricity producers and consumers, and operate them as a distributed power plant. However, the planning of the energy resources is still coordinated collectively in a centralized manner by the aggregator. This work aims to develop a framework to schedule energy resources from the perspective of an aggregator to cover a large variety of technical assets and to simultaneously consider market interactions such as bid acceptance and rejection possibilities. The inevitable and accelerating proliferation of renewable energy resources brings with it -- as a consequence of its intermittency -- a growing need in control reserve and storage technologies. Hence, a focus is placed on control reserve, energy storage, and integrated scheduling and bidding, as well as their trade-offs, to answer the following research questions: 1) What is the current state of control reserve formulations and how can they be improved? Specifically regarding reserve under consideration of limitations with respect to the rate of change of power output, maximum power output, and energy capacity. 2) What are the effects of using different control reserve formulations? 3) Which trade-offs exist in the operation of storage plants in a market environment? 4) Is it possible to derive a rigorous, tractable mathematical model to simultaneously determine scheduling and bidding decisions? 5) Which trade-offs exist between scheduling and bidding decisions and what are their effects? 6) To what extent is it possible to solve energy resource scheduling models faster while retaining sufficiently high solution quality? / In der Vergangenheit war die Stromerzeugung stark zentralisiert und wurde durch konventionelle Kraftwerke wie Kohle-, Gas- oder Kernkraftwerke bereitgestellt. Der Anteil der konventionellen Stromerzeugung wird zunehmend durch die Stromerzeugung aus erneuerbaren Quellen ersetzt. In Europa sank der Anteil der Stromerzeugung aus fossilen Brennstoffen von 49% im Jahr 2011 auf 37% im Jahr 2020, während der Anteil der erneuerbaren Energien im gleichen Zeitraum von 22% auf 38% anstieg. Es wird erwartet, dass die Stromerzeugung aus erneuerbaren Energien jährlich um 10 % steigt und sich die derzeitige Kapazität bis 2030 fast verdreifacht. Die zunehmende Einführung erneuerbarer Energien verändert den Charakter der Elektrizitätsinfrastruktur von einer zentralisierten zu einer stark dezentralisierten Energieversorgung, so dass die Erzeugung näher an den Ort des Bedarfs rückt. Dieser Wandel bringt zahlreiche Herausforderungen mit sich. Diese Arbeit konzentriert sich auf die Herausforderungen bei der Betriebsplanung dezentraler Energieanlagen aus der Perspektive sogenannter Aggregatoren, die zunehmend an den Energiemärkten teilnehmen. Aggregatoren fassen verschiedene Energieanlagen, d.h. Stromerzeuger und -verbraucher, zusammen und betreiben sie als dezentrales Kraftwerk. Die Planung der Energieressourcen wird jedoch weiterhin zentral durch den Aggregator koordiniert. Diese Arbeit zielt darauf ab, ein Framework für die Planung von Energieressourcen aus der Sicht eines Aggregators zu entwickeln, um eine große Vielfalt an technischen Anlagen abzudecken und gleichzeitig Marktinteraktionen wie Gebotsannahme- und Ablehnungsmöglichkeiten zu berücksichtigen. Der unvermeidliche und zunehmende Ausbau von erneuerbaren Energieressourcen bringt -- als Folge ihrer Unstetigkeit -- einen wachsenden Bedarf an Regelleistung- und Speichertechnologien mit sich. Daher liegt der Schwerpunkt auf Regelleistung, Energiespeicherung und integrierter Anlagen- und Gebotsplanung sowie deren Trade-offs, um die folgenden Forschungsfragen zu beantworten: 1) Was ist der aktuelle Stand von Regelleistungsmodellen und wie können diese verbessert werden? Insbesondere im Hinblick auf Regelleistung unter Berücksichtigung von Einschränkungen hinsichtlich der Änderungsrate der Leistungsabgabe, der maximalen Leistungsabgabe und der Energiekapazität. 2) Welche Auswirkungen hat die Verwendung unterschiedlicher Regelleistungsmodelle? 3) Welche Zielkonflikte bestehen beim Betrieb von Speicheranlagen in einem Marktumfeld? 4) Ist es möglich, ein rigoroses, praktikables mathematisches Modell zur gleichzeitigen Bestimmung von Anlagen- und Gebotsplanung aufzustellen? 5) Welche Zielkonflikte bestehen zwischen Anlagen- und Gebotsplanung und welche Auswirkungen haben sie? 6) Inwieweit ist es möglich, Modelle zur Planung von Energieressourcen schneller zu lösen und dabei eine ausreichend hohe Lösungsqualität beizubehalten?

No description available.