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Parallel machine scheduling with dynamic resource allocation via a master–slave genetic algorithm
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DOI：10.1002/tee.22625.png)
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Abstract
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Scheduling problems with resource allocation have become of increasing interest in the academic and industrial areas in recent years. This study investigates parallel machine scheduling with dynamic resource allocation under the objective of minimizing the makespan, where the processing of jobs needs an additional resource that can be assigned and reassigned among all jobs, and the processing time of jobs is a function of the amount of allocated resources. Four decisions must be made in a schedule, namely job assignment, job sequence, resource allocation and jobs' starting time. In order to solve it efficiently, a master–slave genetic algorithm is proposed to determine the decisions of job assignment, job sequence and resource allocation, and a greedy heuristic rule is designed to determine the jobs' starting time. To examine the performance of the proposed algorithm, simulation experiments are carried out on a set of instances, and similar existing algorithms and the standard genetic algorithm are chosen as for comparison. The experimental results show that the proposed algorithm is a promising optimizer in solving the investigated problem. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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