No description available.

No description available.

Production control and capacity configuration policies are critical to a manufacturing firm for effective inventory control. In the first part of this dissertation, a Dynamic Programming model and a solution algorithm are developed to obtain an optimal (near-optimal) production control policy. The solution algorithm is able to produce an extremely good policy under mild conditions, but is applicable only to problems with a limited number of products. For problems involving a large number of products, a heuristic algorithm based on a decomposition/aggregation scheme is then proposed. This algorithm overcomes the computational difficulty typically associated with Dynamic Programming problems with a large number of state dimensions. Computational test results are reported to show the performance of the policy generated by the heuristic algorithm. In the second part of the dissertation, the production lead time and operational cost performance of two capacity configurations are analyzed. Models are developed for each configuration to determine the amount of capacity which minimizes the total capacity acquisition and operational costs, including the inventory cost. Computational test results are presented to study the impact of problem characteristics on the superiority of each configuration.

Firms in several discrete parts manufacturing industries, e.g., electronics equipment, computers, telecommunications equipment etc. operate in a multi-plant environment where products are processed successively at several plants. Prior studies have ignored the interaction between different plants in a multi-plant scenario. The objective of this dissertation is to study the impact of coordination on the cost performance of a two-plant firm. / We propose a model that jointly determines production and inventory decisions so that the total cost of holding inventory and overtime, at the two plants is minimized. Our model captures the interaction between the two plants and is preferable to the uncoordinated or the sequential approach which ignores this interaction. We consider the case with limited capacity and explicitly model setup times. Strategies based on Lagrangian relaxation and Lagrangian decomposition methodologies are proposed to solve the model. / Two main findings emerge from this research. First, our results indicate that coordination could lead to improved cost performance and enhanced profits for firms. Two parameters, the setup time to processing time ratio and the capacity utilization at the two plants played a significant role in determining the cost improvements. Managerial implications relating to implementation of the coordinated model are discussed. The second important finding of this research is that Lagrangian decomposition consistently outperforms Lagrangian relaxation in terms of achieving better deviation from the optimal solution, for this problem. A Linear Programming based technique for further enhancing the convergence between the upper and lower bounds is presented. / In the quest for improved performance, multi-plant coordination represents an important strategy for firms. The contribution of the current research is in modelling some of the salient issues of this problem and exploring promising methodological directions.

Stanley B. Gershwin. / "December, 1983" / Bibliography: p. 36-37. / "DAAK11-82-K-0018"

This thesis presents a cost allocation method for deriving lower bounds on costs of feasible policies for a class of production/inventory problems. Consider the joint replenishment problem where a group of items is replenished together or individually. A sequence of reorders for any particular item will incur holding, backorder and set-up costs specific to the item, in addition whenever any item is replenished a joint cost is incurred. What is required of the total problem is the minimization of a cost function of the replenishment sequence or policy. The cost allocation method consists of decomposing the total problem into sub-problems, one for each item, by allocating the joint cost amongst the items in such a way that every item in the group receives a positive allocation or none. The result is that, for an arbitrary feasible cost allocation, the sum of the minimum costs for the subproblems is a lower bound on the cost of any feasible policy to the total problem. The results for the joint replenishment problem follows: For the constant and continuous demand case we reproduce the lower bound of Jackson, Maxwell and Muckstadt more easily than they did. For the multi-item dynamic lot-size problem, we generalize Silver-Meal and part-period balancing heuristics, and derive a cost allocation bound with little extra work. For the 'can-order' system, we use periodic policies derived from the cost allocation method and show that they are superior to the more complex (s,c,S) policies. The cost allocation method is easily generalized to pure distribution problems where joint replenishment decisions are taken at several facilities. For example, for the one-warehouse multi-retailer problem, we reproduce Roundy's bound more easily than he did. For the multi-facility joint replenishment problem (a pure distribution system with an arbitrary number of warehouses), we give a lower bound algorithm whose complexity is dr log r where d is the maximum number of facilities which replenish a particular item and r is the number of items. / Business, Sauder School of / Graduate

No description available.

No description available.

A multistage production system consists of a number of production stages that are interrelated, that is the output from one stage forms input to the next stage. There are constraints associated with each stage as well as constraints imposed by the overall system. Besides, there are multiple objectives that need to be satisfied, and in numerous cases, these objectives conflict with each other. What is required is an efficient technique to allocate and schedule resources so as to provide a balance between the conflicting objectives within the system constraints. This study is concerned with the problem of scheduling multistage production systems in food processing industry. The system and products have complex structure and relationships. This makes the system difficult to be solved analytically. Therefore, the problem is solved by developing a heuristic algorithm that considers most of the constraints. The output generated by the algorithm includes a production schedule which specifies the starting and completion times of the products in each stage and the machines where the products are to be processed. In addition, a summary of system performances including throughput times, resources' utilizations, and tardy products is reported. / Graduation date: 1992

Ngo-Tai Fong. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 158-168). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Manufacturing Systems: An Overview --- p.1 / Chapter 1.2 --- Previous Research --- p.3 / Chapter 1.3 --- Motivation --- p.5 / Chapter 1.4 --- Outline of the Thesis --- p.8 / Chapter 2 --- Preliminaries --- p.11 / Chapter 2.1 --- Problem Formulation: Deterministic Production Planning --- p.11 / Chapter 2.2 --- Markov Chain --- p.15 / Chapter 2.3 --- Problem Formulation: Stochastic Production Planning --- p.18 / Chapter 2.4 --- Some Lemmas --- p.24 / Chapter 3 --- Open-Loop Production Planning in Stochastic Flowshops --- p.26 / Chapter 3.1 --- Introduction --- p.26 / Chapter 3.2 --- Limiting Problem --- p.29 / Chapter 3.3 --- Weak-Lipschitz Continuity --- p.34 / Chapter 3.4 --- Constraint Domain Approximation --- p.41 / Chapter 3.5 --- Asymptotic Analysis: Initial States in Sε --- p.47 / Chapter 3.6 --- Asymptotic Analysis: Initial States in S \ Sε --- p.61 / Chapter 3.7 --- Concluding Remarks --- p.70 / Chapter 4 --- Feedback Production Planning in Deterministic Flowshops --- p.72 / Chapter 4.1 --- Introduction --- p.72 / Chapter 4.2 --- Assumptions --- p.75 / Chapter 4.3 --- Optimal Feedback Controls --- p.76 / Chapter 4.3.1 --- The Case c1 < c2+ --- p.78 / Chapter 4.3.2 --- The Case c1 ≥ c2+ --- p.83 / Chapter 4.4 --- Concluding Remarks --- p.88 / Chapter 5 --- Feedback Production Planning in Stochastic Flowshops --- p.90 / Chapter 5.1 --- Introduction --- p.90 / Chapter 5.2 --- Original and Limiting Problems --- p.91 / Chapter 5.3 --- Asymptotic Optimal Feedback Controls for pε --- p.97 / Chapter 5.3.1 --- The Case c1 < c2+ --- p.97 / Chapter 5.3.2 --- The Case c1 ≥ c2+ --- p.118 / Chapter 5.4 --- Concluding Remarks --- p.124 / Chapter 6 --- Computational Evaluation of Hierarchical Controls --- p.126 / Chapter 6.1 --- Introduction --- p.126 / Chapter 6.2 --- The Problem and Control Policies under Consideration --- p.128 / Chapter 6.2.1 --- The Problem --- p.128 / Chapter 6.2.2 --- Hierarchical Control (HC) --- p.131 / Chapter 6.2.3 --- Kanban Control (KC) --- p.133 / Chapter 6.2.4 --- Two-Boundary Control (TBC) --- p.137 / Chapter 6.2.5 --- "Similarities and Differences between HC, KC, and TBC" --- p.141 / Chapter 6.3 --- Computational Results --- p.142 / Chapter 6.4 --- Comparison of HC with Other Polices --- p.145 / Chapter 6.5 --- Concluding Remarks --- p.151 / Chapter 7 --- Conclusions and Future Research --- p.153 / Bibliography --- p.158