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We study lot-size policies in a serial, multi-stage, manufacturing/inventory system with two key generalizations, namely (1) random yields at each production stage and (2) an autoregressive demand process. Previous research shows that the optimal policies in models with random yields (even in models with a single installation) lack the familiar order-up-to structure and are not myopic. Thus, dynamic programming algorithms are needed to compute optimal policies and one encounters the “curse of dimensionality;” this is exacerbated here by the need to expand the size and dimension of the state space to accommodate the autoregressive demand feature. Nevertheless, although our model is more complex, we prove that there is an optimal policy with the order-up-to feature and, more importantly, that the optimal policy is myopic. This avoids the computational burden of dynamic programming. Our results depend on two assumptions concerning the stochastic yield, namely that the expected yield at a work station is proportional to the lot size, and the distribution of the deviation of the yield from its mean does not depend on the lot size. We introduce the concept of echelon-like variables to derive the structure of optimal policies; this is a generalization of the echelon variables in Clark and Scarf (1960). Furthermore, we show that the same kind of policy is optimal for several criteria: infinite-horizon discounted cost, infinite-horizon long-run average cost, and finite-horizon discounted cost (with the appropriate choice of the salvage value function).
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.
Sharing accurate and timely supply and demand information throughout a supply chain can yield significant performance improvements to all members of the supply chain. Despite the benefits, many firms are reluctant to share information with their supply chain partners due to an unequal distribution of risks, costs, and benefits among the partners. Thus, incentive mechanisms must be in place to induce communication, cooperation, and collaboration among all members of a supply chain. The issue of Information exchange/sharing has been examined by various researchers over the last 15-20 years. However, there is no research book that compiles various approaches, analyses, key implications, as well as future development of this area. This book will serve as a handbook for researchers who are interested in learning the state of the art of the line of research in this area and explore open research topics in this area. Contributors, all leading researchers, have committed to delivering 18 chapters, broken into four distinct sections covering the Value of Information Sharing, Contracting and Information, Information Signaling, and Incentives for Information Sharing.
This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. The book contains primers on operations, finance, and their interface. After that, each section contains chapters in the categories of theory, applications, case studies, and teaching resources. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing. The balance between theory, applications, and teaching materials make this book an interesting read for academics and practitioners in operations and finance who are curious about the role of new technologies. The book is an attractive choice for PhD-level courses and for self-study.
We consider the multiperiod lot-sizing problem in which the production yield (the proportion of usable goods) is variable according to a known probability distribution. A dynamic programming algorithm for an arbitrary sequence of demand requirements is presented. We review two economic order quantity (EOQ) models for the stationary demand continuous-time problem and derive an EOQ model when the production yield follows a binomial distribution and backlogging of demand is permitted. Heuristics based on the EOQ model are discussed, and a computational evaluation of these heuristics is presented. The heuristics consistently produced near optimal lot-sizing policies for problems with stationary and cyclic demands. (Author).
Companies frequently operate in an uncertain environment and many real life production planning problems imply volatility and stochastics of the customer demands. Thereby, the determination of the lot-sizes and the production periods significantly affects the profitability of a manufacturing company and the service offered to the customers. This thesis provides practice-oriented formulations and variants of dynamic lot-sizing problems in presence of restricted production resources and demand uncertainty. The demand fulfillment is regulated by service level constraints. Additionally, integrated production and remanufacturing planning under demand and return uncertainty in closed-loop supply chains is addressed. This book offers introductions to these problems and presents approximation models that can be applied under uncertainty. Comprehensive numerical studies provide managerial implications. The book is written for practitioners interested in supply chain management and production as well as for lecturers and students in business studies with a focus on supply chain management and operations management.
This book is the outcome of my research in the field of multi levellot sizing and scheduling which started in May 1993 at the Christian-Albrechts-University of Kiel (Germany). During this time I discovered more and more interesting aspects ab out this subject and I had to learn that not every promising idea can be thoroughly evaluated by one person alone. Nevertheless, I am now in the position to present some results which are supposed to be useful for future endeavors. Since April 1995 the work was done with partial support from the research project no. Dr 170/4-1 from the "Deutsche For schungsgemeinschaft" (D FG). The remaining space in this preface shaH be dedicated to those who gave me valuable support: First, let me express my deep gratitude towards my thesis ad visor Prof. Dr. Andreas Drexl. He certainly is a very outstanding advisor. Without his steady suggestions, this work would not have come that far. Despite his scarce time capacities, he never rejected proof-reading draft versions of working papers, and he was always willing to discuss new ideas - the good as weH as the bad ones. He and Prof. Dr. Gerd Hansen refereed this thesis. I am in debted to both for their assessment. I am also owing something to Dr. Knut Haase. Since we al most never had the same opinion when discussing certain lot sizing aspects, his comments and criticism gave stimulating input.
This thesis deals with timing and sizing decisions for production lots, and more precisely, with mathematical models to support optimal tim ing and sizing decisions. These models are called lotsizing models. They are characterized by the fact that production lots are determined based on a trade-offbetween production costs and customer service. Production costs can be categorized as basic production costs, which consist of material costs, labour costs, machine startup costs and over head costs, and inventory related costs, which include costs of capital tied up in inventory, insurances and taxes. Customer service is the capability of the firm to deliver to their clients the products in the quantity they ordered at the agreed upon time and place. The costs of realizing a certain service level are usuaIly very dif ficult to convert into money. They include costs of expediting, loss of customer goodwill, and loss of sales revenues resulting from the short age situation.