A Fuzzy Shifting Bottleneck Hybridised with Genetic Algorithm for Real-world Job Shop Scheduling
نویسندگان
چکیده
This paper describes a fuzzy Shifting Bottleneck Procedure hybridised with genetic algorithm for a real-world job shop scheduling problem. Fuzzy sets are used to model uncertain processing times of jobs and their release dates and due dates. In each iteration, the Shifting Bottleneck Procedure selects a machine, which is critical for the stated objective, namely minimisation of the makespan (the completion time of the last job to be processed). A genetic algorithm is developed to propose a sequence of jobs’ operations to be processed on that machine. Experiments are carried out to compare results obtained by the shifting bottleneck procedure hybridised with genetic algorithm and hybridised with a dispatching rule, proving the efficiency of the former approach.
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