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.

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.

A specialized lot size model for procurement of direct material items is applied in a specific manufacturing firm. Simulation studies are utilized for determining the effectiveness of the lot size model for use in a multiproduct MRP production system having a specific product structure. Measures of effectiveness with respect to cost categories including shortage frequency, material costs and holding costs will be obtained based on a range of demand patterns and levels. These studies focus on five end items representative of the population of brands manufactured at the company under consideration. The results of the study provide a methodological basis for system wide implementation of model based material ordering policy. / M.S.

This thesis examines the spare parts mission scaling problem within the land environment of the Canadian Forces. A revision was done to the recently proposed Land Automated Scaling System, thus providing a readily implementable version of this model. This revised model determines the kit of spare parts for a first or second line unit to carry in order to maximize the operational availability of the deployed weapons systems, subject to a volume constraint. Bayesian methods and actual part demand data are used to revise the demand distribution to more accurately reflect the distribution of the number of parts required during a mission, taking into account the environmental conditions and usage mode of the equipment. The model is easy to use, requiring readily available and easily accessible input data. In addition, it can be operated on a Base minicomputer, thus allowing it to be used by the ordinance engineering and supply officers at the unit/formation level to produce and revise their parts scales as their situation requires.

Title also in Chinese. / Summary in Chinese. / Thesis (M.B.A.)--Chinese University of Hong Kong. / Bibliography: leaves 94-95.

Liu, Xing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 80-87). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.10 / Chapter 3 --- The 2-Promotional Level Model --- p.15 / Chapter 3.1 --- Average Profit Function --- p.17 / Chapter 3.2 --- Auxiliary Profit Function --- p.19 / Chapter 3.3 --- Characterizing the Auxiliary Function --- p.21 / Chapter 3.4 --- Display Optimization --- p.22 / Chapter 3.5 --- Ordering Policy Optimization --- p.27 / Chapter 3.6 --- "An Algorithm for Finding an Optimal (r, Q, d, D) Policy" --- p.28 / Chapter 3.7 --- Optimality Verification --- p.30 / Chapter 4 --- Multiple Promotional Level Model --- p.36 / Chapter 4.1 --- Analysis --- p.38 / Chapter 4.2 --- "An Algorithm for Finding an Optimal (r,Q,dn, .,d1,D1,...,Dn)Policy" --- p.46 / Chapter 5 --- Extension to Random Demand Size --- p.49 / Chapter 6 --- Numerical Examples --- p.63 / Chapter 7 --- Concluding Remarks --- p.75 / Bibliography --- p.80

To Chi Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 50-52). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Structure of thesis --- p.3 / Chapter 2 --- Literature Review --- p.4 / Chapter 3 --- Problem description and formulation --- p.7 / Chapter 3.1 --- Notation --- p.9 / Chapter 3.2 --- Cost Structure --- p.11 / Chapter 3.3 --- Assumptions --- p.12 / Chapter 3.4 --- Problem Formulation --- p.14 / Chapter 4 --- Stations with deterministic demand --- p.15 / Chapter 4.1 --- Greedy Algorithm --- p.15 / Chapter 4.2 --- Example --- p.16 / Chapter 4.3 --- Properties --- p.17 / Chapter 5 --- Stations with stochastic demand --- p.21 / Chapter 5.1 --- Decision planned before arrival --- p.26 / Chapter 5.2 --- Decision made after vehicle arrival --- p.29 / Chapter 6 --- Numerical example --- p.38 / Chapter 6.1 --- Comparing decision made before and after arrival of sta- tion --- p.39 / Chapter 6.2 --- Relation between K and li --- p.40 / Chapter 6.3 --- Relation between unit penalty / cost value with K . . . --- p.40 / Chapter 7 --- Conclusion --- p.47