A methodology for real-time scheduling of jobs with splitting on unrelated parallel machines Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/6m311s65n

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  • Unrelated parallel machines are machines that perform the same function but have different capacity or capability. Thus, the processing time of each job would be different on machines of different types. The scheduling environment considered is dynamic in both job release time and machine availability. Additionally, each job considered can have different weight, and due date. Split jobs are also considered in this research. The number of jobs that needs to be processed in split-modes is pre-determined and not part of the scheduling decision. Additional constraints are imposed on split jobs to ensure that the absolute difference in completion time of the split portions of a job is within a user-specified margin. These constraints are supported by the Just-In-Time manufacturing concept where inventory has to be maintained at a very low or zero level. The objective of this research is to minimize the sum of the weighted tardiness of all jobs released within the planning horizon. The research problem is modeled as a mixed (binary) integer-linear programming model and it belongs to the class of NP-hard problems. Thus, one cannot rely on using an implicit enumeration technique, such as the one based on branch-and-bound, to solve industry-size problems within a reasonable computation time. Therefore, a higher-level search heuristic, based on a concept known as tabu search, is developed to solve the problems. Four different methods based on simple and composite dispatching rules are used to generate the initial solution that is used by tabu-search as a starting point. Six different tabu-search based heuristics are developed by incorporating the different features of tabu search. The heuristics are tested on eight small problems and the quality of their solutions is compared to their optimal solutions, which are obtained by applying the branch-and-bound technique. The evaluation shows that the tabu-search based heuristics are capable of obtaining solutions of good quality within a much shorter time. The best performer among these heuristics recorded a percentage deviation of only 1.18%. The performance of the tabu-search based heuristics is compared by conducting a statistical experiment that is based on a split-plot design. Three sizes of problem structures, ranging from 9 jobs to 60 jobs and from 3 machines to 15 machines are used in the experiment. The results of the experiment reveal that in comparison to other initial-generation methods, the composite dispatching rule is capable of obtaining initial solutions that significantly accelerate the tabu search based heuristic to get to the final solution. The use of long-term memory function is proven to be advantageous in solving all problem structures. The long-term memory based on maximum-frequency strategy is recommended for solving the small problem structure, while the minimum-frequency strategy is preferred for solving medium and large problem structures. With respect to the use of tabu-list size as a parameter, the variable tabu-list size is preferred for solving the smaller problem structure, but the fixed tabu-list size is preferred as the size of the problems grows from small to medium and then large.
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