Performance Analysis Of Stochastic Flow Lines With Limited Material Supply
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Performance analysis of stochastic flow lines with limited material supply
Author: Julia Mindlina
language: en
Publisher: BoD – Books on Demand
Release Date: 2019-03-14
In order to improve the performance of production systems, companies consider the optimization of the flow line configuration. However, the material supply of the flow line exerts a strong influence on the output of the production system since material shortages impede the flow of workpieces through the flow line. Simultaneously, the configuration of the flow line determines the demand for material. Consequently, the mutual interdependence between the material supply and the flow line has to be considered in order to balance a sufficient material supply of the flow line avoiding material shortages as well as excessive material inventory and handling effort. We provide integrated approaches for the evaluation and optimization of stochastic flow lines with limited material supply. Thereby, we make use of several evaluation methods as Markov chain approaches, aggregation and decomposition approaches as well as linear programming. Further, we model open and closed queuing networks in continuous and discrete time. Hence, we present exact and approximate approaches that allow us to study the effects in several stochastic production systems with limited material supply.
Analyse und Optimierung stochastischer Fließproduktionssysteme mit begrenzter Materialverfügbarkeit unter Verwendung von Methoden des maschinellen Lernens
Author: Insa Südbeck
language: de
Publisher: BoD – Books on Demand
Release Date: 2023-01-26
An den Stationen einer Fließlinie werden häufig sekundäre Materialien zur Bearbeitung eines Werkstücks benötigt. Da die Materialverfügbarkeit direkten Einfluss auf den Durchsatz des Gesamtsystems hat, sollten integrierte Modelle zur Konfiguration der Fließlinie und der Materialversorgung genutzt werden. Insa Südbeck entwickelt zwei Ansätze zur Durchsatzbewertung von stochastischen Fließproduktionssystemen mit begrenzter Materialverfügbarkeit unter Verwendung von Methoden des maschinellen Lernens. Diese Ansätze können zur schnellen Evaluation einzelner Konfigurationen in angepassten heuristischen Optimierungsverfahren verwendet werden. Dadurch können Verhaltensweisen der Systeme beobachtet und betriebswirtschaftliche Erkenntnisse abgeleitet werden.
Generalized Bounds for Convex Multistage Stochastic Programs
Author: Daniel Kuhn
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-03-30
This work was completed during my tenure as a scientific assistant and d- toral student at the Institute for Operations Research at the University of St. Gallen. During that time, I was involved in several industry projects in the field of power management, on the occasion of which I was repeatedly c- fronted with complex decision problems under uncertainty. Although usually hard to solve, I quickly learned to appreciate the benefit of stochastic progr- ming models and developed a strong interest in their theoretical properties. Motivated both by practical questions and theoretical concerns, I became p- ticularly interested in the art of finding tight bounds on the optimal value of a given model. The present work attempts to make a contribution to this important branch of stochastic optimization theory. In particular, it aims at extending some classical bounding methods to broader problem classes of practical relevance. This book was accepted as a doctoral thesis by the University of St. Gallen in June 2004.1 am particularly indebted to Prof. Dr. Karl Frauendorfer for - pervising my work. I am grateful for his kind support in many respects and the generous freedom I received to pursue my own ideas in research. My gratitude also goes to Prof. Dr. Georg Pflug, who agreed to co-chair the dissertation committee. With pleasure I express my appreciation for his encouragement and continuing interest in my work.