Affiliation:
1. University of Pisa Department of Mechanical, Nuclear and Production Engineering Italy
Abstract
This paper presents a genetic algorithm for generalized job-shop problem solving. The generalization includes feeding times, sequences of set-up dependent operations and jobs with different routings among workcentres including ‘multi-identical’ machines. A formulation as an optimization problem with an original chromosome coding and tailored genetic operators are proposed. The algorithm has been tested with benchmarks given in the literature, and bounds for the minimum completion time are reported in order to evaluate the performance in both generalized job-shop and flexible manufacturing system (FMS) scheduling.
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Cited by
9 articles.
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1. A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems;IEEE/CAA Journal of Automatica Sinica;2019-07
2. Integrating Process Plan and Part Routing Using Optimization via Simulation Approach;International Journal of Simulation Modelling;2019-06-15
3. An integrated ant colony optimization algorithm to solve job allocating and tool scheduling problem;Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture;2016-03-24
4. An improved meta-heuristic approach for solving identical parallel processor scheduling problem;Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture;2015-02-17
5. A review on job shop scheduling with setup times;Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture;2015-01-19