Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 140-146). Also available in electronic version.

Report (M. Engr.)--Virginia Polytechnic Institute and State University, 1994. / Includes bibliographical references (leaves 43-54). Also available via the Internet.

No description available.

In this thesis we present an integrated formulation for production planning, facilities layout and process selection and provide two heuristics for solving the integrated problem. The traditional solution procedures to each of the subproblems are discussed. The decisions modeled in these three problems are interrelated and, in some cases, share overlapping data requirements. The integrated formulation provided is a mixed integer program. Solving this mixed integer program optimally is a hard problem. Thus, in this thesis we only provide heuristic solutions. An optimal branch and bound algorithm is suggested. Both heuristic solutions are demonstrated on a sample problem. The results of testing eight problems is recorded. Further suggestions for improving the heuristic solutions are also provided.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

In this thesis we give two extensions to the multiple choice modular design problem. In the first case, we consider the situation that parts are purchased from different vendors. In the second case, we consider the situation that linear and separable constraints are present in our model. We propose a heuristic for solving each of the problems. Some computational results are included.

No description available.

Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

The loading problem in a Flexible Manufacturing System (FMS) lies in the allocation of operations and associated cutting tools to machines for a given set of parts subject to capacity constraints. This dissertation proposes a hierarchical approach to the machine loading problem when the workload and tool magazine capacity of each machine are restrained. This hierarchical approach reduces the maximum workload of the machines by partially grouping them. This research deals with situations where different groups of machines performing the same operation require different processing times and this problem is formulated as an integer linear problem. This work proposes a solution that is comprised of two phases. In the first phase (Phase I), demand is divided into batches and then operations are allocated to groups of machines by using a heuristic constrained by the workload and tool magazine capacity of each group. The processing time of the operation is different for each machine group, which is composed of the same identical machines; however, these machines can perform different sets of operations if tooled differently. Each machine and each group of machines has a limited time for completing an operation. Operations are allocated to groups based on their respective workload limits. In the second phase (Phase II), demand is divided into batches again and operations are assigned to machines based on their workload and tool magazine capacity defined by Longest Processing Time (LPT) and Multifit algorithms. In Phase II, like Phase I, partial grouping is more effective in balancing the workload than total grouping. In partial grouping, each machine is tooled differently, but they can assist one another in processing each individual operation. Phase I demonstrates the efficiency of allocating operations to each group. Phase II demonstrates the efficiency of allocating operations to each machine within each group. This two-phase solution enhances routing flexibility with the same or a smaller number of machines through partial grouping rather than through total grouping. This partial grouping provides a balanced solution for problems involving a large number of machines. Performance of the suggested loading heuristics is tested by means of randomly generated tests.

In this thesis, we consider a capacity allocation problem in flexible manufacturing systems. We assume limited time and tool magazine capacities on the Computer Numerically Controlled (CNC) machines. We have a set of operations that have to be assigned to the machines and each operation requires a set of tools to be processed. Our problem is to allocate the available capacity of the CNC machines to operations and their required tools. We consider two problems in this study: maximizing the total weight of operations where there are a limited number of tools of each type available and maximizing total weight minus total tooling cost where the tools can be used or purchased at a cost. We model the problems as Integer Linear Programs and show that they are NP-hard in the strong sense. For the total weight problem, we propose upper bounds, branch and bound algorithm for exact solutions and several heuristics for approximate solutions. For the bicriteria problem, we use Lagrangean relaxation technique to obtain lower and upper bounds. Our computational results have revealed that all solution approaches give satisfactory results in reasonable times.