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Inhaltsangabe:Introduction: As the world population is growing continuously and emerging markets are expanding, natural recourses are being used even more intensively. Because of the scarcity of natural resources, industry faces a changing business environment. Due to government regulations, companies nowadays must handle not only in terms of efficiency, but also of sustainable development and new market opportunities. Thus, with the progression of the logistics sector in recent years, supply chain management and especially the concept of reverse logistics have become more important for both, industry and science. By utilizing reverse logistics, companies aim at maximizing their product revenue while reducing the costs of product returns. Accordingly, implementing an effective concept of reverse logistics, while manufacturing environmentally friendly products, has become a strategic issue. In order to meet the requirements, companies are confronted with the problem of reducing the uncertainties regarding the quality, quantity and timing of the product returns. In this context, a high level of uncertainty leads to a strong increase in complexity compared to the traditional forward supply chains. Using modern computer aided modelling techniques such as system dynamics, helps to counteract this complexity since they not only enable a better understanding of the dynamic behaviour of such complex systems but also allow an improved estimation of the impact of a changing environment and management decisions. This thesis contributes towards an improvement of the strategic decision making process in the field of reverse logistics by providing a generic simulation model which can be used to analyse the influence of different environmental and economical policies with respect to prevailing market conditions. To achieve this objective, the following approach is proposed: In Chapter 2, the theoretical foundation of reverse logistics is characterized forming the framework for the subsequent analytical approach concerning the appropriate model development. For this purpose, first, an overview of the state of the art concerning the processes and influencing factors within the field of reverse logistics is provided. This is achieved by describing the theoretical background of the topic, including a characterization of the impact of individual reverse logistic activities on each other and on their environment. Afterwards, current challenges and trends when [...]
As legislations have become stricter and the competition on markets is getting stronger, companies facing return flows strive for the implementation of efficient and cost-effective reverse logistic procedures. At the same time, when managing reverse logistics, they are not only confronted with a high degree of uncertainties concerning the quality, quantity and timing or the product returns, but also with a dynamically changing environment. Various aspects, such the increasing amount of return flows, shorter repair and lead times as well as increasing disposal costs, affect the reverse logistic system and need to be managed proficiently. Additionally, handling product returns requires supportive computer aided modelling tools that are capable of handling the dynamic and complex characteristics of the reverse logistic system and allow an improved estimation of the impact of a changing environment and management decisions. For the purpose of this study, the system dynamics modelling approach has been identified as particularly suitable for illustrating the system in question with a special focus on understanding the dynamic behaviour over time. A generic system dynamics model has been exemplarily created and simulated using the program iThink. The model comprises end-to-end processes of the main reverse logistic activities related to customer returns and has been used for studying the strategic design and optimization of the reverse logistic system. In order to consider relevant uncertainties as well as environmental concerns and economic efficiency, representative policies have been applied where, inter alia, with the help of the graphical illustration of the processes, effective strategies could be implemented. A general evaluation of the system dynamics methodology has revealed the significant advantages of using supportive modelling techniques for strategic decision making. Particularly for complex systems that change over time, such as reverse logistics, applying appropriate computer aided modelling tools in order to anticipate the overall effect on processes caused by varying surroundings has proven essential. An effective utilization of system dynamics may significantly reduce the forecasting and planning risks within individual frameworks, such as capacity planning. Moreover, the generic approach allows the application of the model to any other industry that is characterized by uncertain capacity utilization and varying technical, economical and legal conditions.
In a context of global competition, the optimization of logistics systems is inescapable. Logistics Systems: Design and Optimization falls within this perspective and presents twelve chapters that well illustrate the variety and the complexity of logistics activities. Each chapter is written by recognized researchers who have been commissioned to survey a specific topic or emerging area of logistics. The first chapter, by Riopel, Langevin, and Campbell, develops a framework for the entire book. It classifies logistics decisions and highlights the relevant linkages to logistics decisions. The intricacy of these linkages demonstrates how thoroughly the decisions are interrelated and underscores the complexity of managing logistics activities. Each of the chapters focus on quantitative methods for the design and optimization of logistics systems.
Monthly. Papers presented at recent meeting held all over the world by scientific, technical, engineering and medical groups. Sources are meeting programs and abstract publications, as well as questionnaires. Arranged under 17 subject sections, 7 of direct interest to the life scientist. Full programs of meetings listed under sections. Entry gives citation number, paper title, name, mailing address, and any ordering number assigned. Quarterly and annual indexes to subjects, authors, and programs (not available in monthly issues).
Due to the scarcity of reliable data, the existing literature on default risk still displays an imbalance between theoretical and empirical contributions. Consequently, the focus of this book is on empirical work. Within an intensity based modelling framework a broad range of promising specifications is tested using corporate bond data. The book provides one of the most comprehensive empirical studies in the field, from Kalman filtration of affine term structure models to the use of Efficient Method of Moments estimation of dynamic term structure models in a default risky context. Filling another gap in empirical research, the book devotes special attention to the identification factors that can explain credit default swap premia.
5th Werner Kern Award for Productivity Research 2005 Kanban control systems bear a great potential to significantly improve operations. A company may reap the full benefits of kanban control only after determining an optimal or near-optimal system configuration. To do that, methods are needed to evaluate the performance and operating costs of individual system configurations. We propose an innovative construction-kit approach that enables us to build stochastic analytical models of a large class of single- and multi-product kanban systems. The presented construction-kit approach may be extended and augmented in various directions.
Economic application of nonlinear dynamics, microscopic agent-based modelling, and the use of artificial intelligence techniques as learning devices of boundedly rational actors are among the most exciting interdisciplinary ventures of economic theory over the past decade. This volume provides us with a most fascinating series of examples on "complexity in action" exemplifying the scope and explanatory power of these innovative approaches.
This book investigates convex multistage stochastic programs whose objective and constraint functions exhibit a generalized nonconvex dependence on the random parameters. Although the classical Jensen and Edmundson-Madansky type bounds or their extensions are generally not available for such problems, tight bounds can systematically be constructed under mild regularity conditions. A distinct primal-dual symmetry property is revealed when the proposed bounding method is applied to linear stochastic programs. Exemplary applications are studied to assess the performance of the theoretical concepts in situations of practical relevance. It is shown how market power, lognormal stochastic processes, and risk-aversion can be properly handled in a stochastic programming framework. Numerical experiments show that the relative gap between the bounds can typically be reduced to a few percent at reasonable problem dimensions.
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.
Economic, marketing, and legislative considerations are increasingly leading companies to take back and recover their products after use. From a logistics perspective, these initiatives give rise to new goods flows from the user back to the producer. The management of these goods flows opposite to the traditional supply chain flows is addressed in the recently emerged field of Reverse Logistics. This monograph considers quantitative models that support decision making in Reverse Logistics. To this end, several recent case studies are reviewed. Moreover, first hand insight from a study on used electronic equipment is reported on. On this basis, logistics issues arising in the management of "reverse" goods flows are identified. Moreover, differences between Reverse Logistics and more traditional logistics contexts are highlighted. Finally, attention is paid to capturing the characteristics of Reverse Logistics in appropriate quantitative models.