Thesis--Auburn University, 2006. / Abstract. Vita.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 97-114).

Includes bibliographical references (p. 64-71).

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.

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This thesis addresses the scheduling and control of shop-floor production units that operate in a highly autonomous and distributed environment. The distinct feature of this environment is the heterarchical nature of the control where the scheduling function is quite independently carried out by the units. The units solve only part of the overall problem while resolving conflicts to maintain consistent global schedules. The need for communication and coordination, in such circumstances, introduces many complexities that affects the quality of the schedules produced. These include lapses of open-loop control due to uncertainty of up-to-date status information, asynchronous behaviour, and uncontrollable propagation of conflicts. / A behaviour-based approach is introduced to solve these problems. Using this approach, the organisation of the shop-floor is viewed as similar to a colony of ants or an eco-system. The units operate quite independently but continue to adapt their schedules to changes in their environment. While they may not directly negotiate to resolve conflicts, their cooperation is innate or in-built through their local adaptive actions. This individual cooperative action of the units brings about a collective behaviour that produces the desired emergent global schedules. The major focus of this research is in examining the link between the individual and collective behaviours and developing a model that realises the desired scheduling functionality at the shop level. / In order to achieve high scheduling performance (both locally and globally) a model of a unit incorporating dynamic problem decomposition, allocation algorithms and adaptation mechanisms is developed. For the latter, a reinforcement learning model is used to adapt the scheduling horizon. In fact, an important contribution if this research is the novel view we take of the problem and the manner of adaptation. In addition, a communication model for simulating the scheduling behaviours is designed using concepts of Holonic and other emerging concepts of manufacturing systems. / The model is tested for a number of scheduling problems representing a variety of production situations. Preliminary results indicate an impressive scheduling performance comparable to well-known heuristics. Further examination indicates the types of dynamic behaviour that can be expected of such a model, including the levels of unresolved conflicts, the adaptability in the face of uncertainty, consequence of alternative communication policies and the sensitivities to adaptation. / This thesis has also a strong qualitative theme in reviewing and consolidating the concepts underlying the design and operational attributes of autonomous distributed organisations of the shop-floor.

Thesis (Ph. D.)--Ohio University, June, 2000. / Title from PDF t.p.