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This paper treats a two-echelon inventory system. The higher echelon is a single location reffered to as the depot, which places orders for supply of a single com modity. The lower echelon consists of several points, called the retailers, which are supplied by shipments from the depot, and at which random demands for the item occur. Stocks are reviewed and decisions are made periodically. Orders and/or shipments may each require a fixed lead time before reaching their respective desti nations. Section II gives a short literature review of distribution research. Section III introduces the multi-echelon distribution system together with the underlying as sumptions and gives a description of how this problem can be viewed as a Markovian Decision Process. Section IV discusses the concept of cost modifications in a distribution context. Section V presents the test-examples together with their optimal solutions and also gives the characteristic properties of these optimal solutions. These properties then will be used in section VI to give adapted ver sions of various heuristics which were used in assembly experiments previously and which will be tested against the test-examples.
The multi-stage production system is viewed as a production process in which component parts have to be obtained by manufacturing or by purchasing then assembled into subassemblies, assemblies, and finally into the finished good. At the aggregate level a new formulation for the aggregate planning model is given, in order to bring computational feasibility to situations in which older formulations went beyond the capabilities of current linear programming codes. The resulting aggregate model is a large scale system that lends itself to solution by column generation. A dynamic programming algorithm for the generation of columns is developed. Next, at the disaggregation level, the problem of computing optimal lot sizes in multi-stage systems is addressed and solved. Since exact solution procedures are found to be either very expensive or computationally infeasible, a heuristic approach is adopted and results are reported. For more complex situations, in which parts are common to several end products or where there is independent demand for parts, even the heuristics become infeasible; therefore it is suggested that myopic lot sizing policies be used.
​Due to a varying product demand (changing product mix) and different production speeds, bottlenecks may shift between the stages. In that case, a simultaneous lot-sizing and scheduling of these stages is recommendable. Hence, an improved version of the General Lot-Sizing and Scheduling Problem for Multiple production Stages (GLSPMS) was developed. Moreover, several reformulation techniques were applied to this model to solve it exactly. Besides, a new meta-heuristic which combines the principles of Variable Neighborhood Decomposition Search (VNDS) and Exchange was implemented to find good solutions, even for a real-world problem case. Finally, further model extensions, e.g., for scarce setup resources, were proposed.
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The effective use of business intelligence, communication, and productivity applications for supply chain management are essential for the achievement and analysis of information sharing. Management Innovations for Intelligent Supply Chains provides comprehensive coverage in the latest research and developments of supply chain management. This reference collection provides research, methodologies, and frameworks of the incorporation of information systems to better support supply chain management.
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.