This book has a dual purpose?serving as an advanced textbook designed to prepare doctoral students to do research on the mathematical foundations of inventory theory, and as a reference work for those already engaged in such research. All chapters conclude with exercises that either solidify or extend the concepts introduced.
The International Conference on Production Research has a good tradition: The fIrst Conference was held in Birmingham 1971 with 61 participants. With respect to the decision that the Conference should be held every second year, by this time the Conference has been held in the following countries: Birmingham (1971, UK), Copenhagen (1973, Denmark), Amhurst (1975, USA), Tokyo (1977, Japan), Amsterdam (1979, The Netherlands), Novi Sad (1981, Yugoslavia), Windsor (1983, Canada), Stuttgart (1985, Germany), and the next Conference will take place in Cincinnatti (1987, USA). The number of submitted abstracts and papers was continuously increas ing such that the Programme Committee of this actual 8th Conference on Production Research has been forced to introduce a further refereeing procedure. Each submitted abstract was presented to at least two referees. This resulted not only in a reduction of the number of presented full papers and poster contributions but, as the Programme Committee and the Editiors hope, it led also to a considerable increase in the scientifIc quality of this 8th International Conference on Production Research. The preceeding conference in Windsor, Canada, was dedicated to the topic: Production Research as a Means of Productivity Improvement. We don't believe that this statement has become untrue in the meanwhile.
In the real world, production systems are affected by external and internal uncertainties. Stochastic demand - an external uncertainty - arises mainly due to forecast errors and unknown behavior of customers in future. Internal uncertainties occur in situations where random yield, random production capacity, or stochastic processing times affect the productivity of a manufacturing system. The resulting stochastic production output is especially present in industries with modern and complex technologies as the semiconductor industry. This thesis provides model formulations and solution methods for capacitated dynamic lot sizing problems with stochastic demand and stochastic production output that can be used by practitioners within Manufacturing Resource Planning Systems (MRP), Capacitated Production Planning Systems (CPPS), and Advanced Planning Systems (APS). In all models, backordered demand is controlled with service levels. Numerical studies compare the solution methods and give managerial implications in presence of stochastic production output. This book addresses practitioners, consultants, and developers as well as students, lecturers, and researchers with focus on lot sizing, production planning, and supply chain management.
This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.
Stock management and control is a critical element to the success and overall financial well-being of an organization. Through the application of innovative practices and technology, businesses are now able to effectively monitor their operations and manage their inventory by evaluating sales patterns and customer preferences. Optimal Inventory Control and Management Techniques explores emergent research in stock management and product control within organizations. Featuring diverse perspectives on the implementation of various optimization techniques, genetic algorithms, and datamining concepts, as well as research on big data applications for inventory management, this publication is a comprehensive reference source for practitioners, educators, and researchers in the fields of logistics, operations management, and retail management.
