Ant foraging behavior for job shop problem
Ant Colony Optimization (ACO) is a new algorithm approach, inspired by the foraging behavior of real ants. It has frequently been applied to many optimization problems and one such problem is in solving the job shop problem (JSP). The JSP is a finite set of jobs processed on a finite set of machine...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
EDP Sciences
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/73288/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969872694&doi=10.1051%2fmatecconf%2f20165201005&partnerID=40&md5=1eede959d2d78b3e75dab889a8c7a0a3 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | Ant Colony Optimization (ACO) is a new algorithm approach, inspired by the foraging behavior of real ants. It has frequently been applied to many optimization problems and one such problem is in solving the job shop problem (JSP). The JSP is a finite set of jobs processed on a finite set of machine where once a job initiates processing on a given machine, it must complete processing and uninterrupted. In solving the Job Shop Scheduling problem, the process is measure by the amount of time required in completing a job known as a makespan and minimizing the makespan is the main objective of this study. In this paper, we developed an ACO algorithm to minimize the makespan. A real set of problems from a metal company in Johor bahru, producing 20 parts with jobs involving the process of clinching, tapping and power press respectively. The result from this study shows that the proposed ACO heuristics managed to produce a god result in a short time. |
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