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.

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.

Increasing customer requirements, product variety, and market competition demand for service and cost improvements by model based inventory control in supply chains. The book presents approaches for safety stock determination in manufacturing and logistics networks. Most of the existing literature provides methods for very specific types of supply networks. The approach presented in this book follows a material flow philosophy that allows for several extensions of the basic models and therefore offers a wide applicability within decision support systems. Models for several types of problems and network structures are presented and analyzed to develop efficient optimization algorithms and heuristics.

The book considers the lot-sizing and scheduling problem for flexible flow line production facilities. Flexible flow lines are flow lines with parallel machines on some or all production stages. They can be found in a vast number of industries. A three-phased solution approach is presented that solves the integrated lot-sizing and scheduling problem in a hierarchical manner. The approach is able to handle several important features relevant in industrial practice, such as back-orders and setup carry-over. The developed solution procedures solve practically sized problems in a relatively short amount of time. One of the procedures is based on a novel mixed integer programming (MIP) model, which employs integer variables instead of binary variables. This makes it possible to find (near-)optimal solutions using standard algorithms such as CPLEX. Another procedure uses two nested Genetic Algorithms. An application of the framework in the semiconductor industry is given.

In logistics systems, the issue of planning stability has attracted increased attention and interest in recent years. This is mainly due to an increasing integration of planning systems both within and across companies in supply chain management. The propagation of adjustments in planning systems first acquired wide attention when MRP systems were employed as standard planning tools for material coordination. Within a rolling horizon framework the MRP application produced considerable planning instability which origins from uncertainties in the planner's exogenous environment as well as from endogenous sources. This book presents an analytical investigation that gives deep insight into the influence of different kind of inventory control rules on the stability of material planning systems under stochastic demand in a rolling horizon environment.

The book deals with collaborative planning, an approach to supply chain planning which aims to coordinate planning tasks of independent supply chain partners while respecting their local decision authority. The major contribution of this work is to provide a process model concerned with the decision making and negotiation aspects of collaborative planning. Taking technological means for data exchange as a given, it sketches a detailed picture of a collaborative planning process at the medium-term level of master planning, assuming that mathematical programming models are used by all partners. Building on the planning process, it indicates how financial outcomes and partner incentives are affected by the negotiations of supply quantities between buyers and suppliers, and demonstrates how supply contract terms can be adapted in order to establish a win-win situation for all partners.

Classical econometrics - which plunges its roots in economic theory with simultaneous equations models (SEM) as offshoots - and time series econometrics - which stems from economic data with vector autoregr- sive (VAR) models as offsprings - scour, like the Janus's facing heads, the flowing of economic variables so as to bring to the fore their autonomous and non-autonomous dynamics. It is up to the so-called final form of a dy namic SEM, on the one hand, and to the so-called representation theorems of (unit-root) VAR models, on the other, to provide informative closed form expressions for the trajectories, or time paths, of the economic vari ables of interest. Should we look at the issues just put forward from a mathematical standpoint, the emblematic models of both classical and time series econometrics would turn out to be difference equation systems with ad hoc characteristics, whose solutions are attained via a final form or a represen tation theorem approach. The final form solution - algebraic technicalities apart - arises in the wake of classical difference equation theory, display ing besides a transitory autonomous component, an exogenous one along with a stochastic nuisance term. This follows from a properly defined ma trix function inversion admitting a Taylor expansion in the lag operator be cause of the assumptions regarding the roots of a determinant equation pe culiar to SEM specifications.