This thesis investigates the effects of dynamic rescheduling strategies of jobs at multiple revisited workstations called hubs on the performance of Flexible Manufacturing Systems (FMS). The objective of rescheduling jobs at workstations is to improve various aspects of the production flow and manufacturing productivity. Compared to fixed queue scheduling rules, dynamic changes in queue scheduling rules for hubs at certain intervention times, are shown in some cases to reduce total job flow time (maximum completion time of all jobs in a fixed total "Makespan") and average flow time (average completion time) simultaneously. The development of dynamic hub scheduling rules including intervention time specification to improve job flow measures establish the basis for an Expert System rules base for dynamic scheduling. The rationale for dynamic queue scheduling rules are developed from analysing the machine idle time structure for a simple single hub system operated under fixed queue scheduling rules. The intervention and rescheduling procedure is applied to increasingly complex and concrete FMS cases use a flexible simulation model including animation of the production facility. Specifically, this thesis provides a methodology for developing and evaluating the rescheduling rules with respect to the trade-off between Makespan and average completion time for a mixed number of jobs in an FMS defined by hub and non-hub workstations.

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A cellular manufacturing system (CMS) is a manufacturing structure organized based on the group technology (GT) concept. The main advantages of the CMSs include the low material handling costs, short setup times and reduced work in process. This study addresses the machine/part grouping and group scheduling (i.e., part/part family scheduling) problems, the two key issues in the CMS design and planning. The machine/part grouping problems can be classified into binary and comprehensive grouping problems depending on whether or not the processing times and the machine capacities are considered. The binary grouping problem arises if the part demands are unknown when the CMS is being developed. If the part demand can be forecast accurately, both the processing times and machine capacities have to be included in the analysis. This gives rise to comprehensive grouping. Both the binary and comprehensive grouping have been proved to be NP-complete problems which cannot be solved in polynomial time. Considering the large number of parts and machines involved in the industrial design problem, efficient solution methods are highly desirable. In this study, a novel neural network structure, Ortho-Synapse Hopfield Network (OSHN), has been designed to solve the binary grouping problem. Due to its significantly reduced number of synapses and unique structure, the OSHN is very computationally efficient and training-free. An objective-guided search approach has been developed to lead the OSHN search process to tune the network parameters and escape the local optima. To solve the comprehensive grouping problem, two approaches are proposed. The first one is a simulated annealing (SA) method based on a generalized grouping efficiency index. The SA method is used jointly with the OSHN algorithm to improve the computational efficiency. The second method is a modified OSHN algorithm. The objective of the modified OSHN is to maximize the generalized grouping efficiency subject to machine capacities. Our computational results compare favorably with solutions obtained in the literature. The group scheduling problem has also been proven to be NP-hard. Furthermore, due to the limited time available for scheduling decisions, computational efficiency is more critical. To this end, a combined tabu search/simulated annealing (tabu-SA) approach is developed to solve the group scheduling problem. The main advantage of this approach is that the simulated annealing search can be accompanied by a short term memory to avoid cycling and thus improve solution quality and computational efficiency. This has been tested and demonstrated in our computational experience.

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