Abstract
In this paper, a new approach has been suggested for solving the multi-objective job shop scheduling problem, in which, simple priority rules are used dynamically, according to the varied state of the scheduling environment. The rules assign priority to the jobs that waiting in queues based on their features and/or the scheduling environment. Since the real scheduling environments are generally dynamic, it is better to use different rules during the scheduling according to the state of the shop floor at each decision time. Based on this approach, a new algorithm is designed, which uses different rules over the scheduling time. This approach can be easily applied to solve the real scheduling problems of the manufacturing systems. The algorithm has been compared with some classic rules from the literature. The results show that the proposed approach is more effective than using a fixed priority rule.
Highlights
Job shop scheduling problem (JSSP) is a well-known NP-hard problem that has been researched widely in the recent decades and it is important because of its applied aspects
Many methods of the optimization, artificial intelligence (AI) and machine learning are applied for solving the JSSP
The heuristic-based and hybrid methods that take advantage of the several techniques like the optimization and AI methods are very useful for solving the large-scale scheduling problems
Summary
Job shop scheduling problem (JSSP) is a well-known NP-hard problem that has been researched widely in the recent decades and it is important because of its applied aspects. Some of the scheduling problems in the manufacturing systems can be modeled as a JSSP. Many methods of the optimization, artificial intelligence (AI) and machine learning are applied for solving the JSSP. The heuristic-based and hybrid methods that take advantage of the several techniques like the optimization and AI methods are very useful for solving the large-scale scheduling problems. Priority rules (PRs) are among the widely used and practical methods for solving the complex scheduling problems in the manufacturing systems, which are generally dynamic and contains many random variables. PRs are able to find fair solutions for the real-time scheduling problems in a polynomial time
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