Non-Crossover and Multi-Mutation Based Genetic Algorithm for Flexible Job-Shop Scheduling Problem

Zhang, Zhongshan; Chen, Yuning; Yue-jin, Tan; Yan, Jungang

doi:10.1587/transfun.e99.a.1856

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…In the case of approximate solutions, many scholars have proposed many methods in recent years. Genetic Algorithm [5,6], estimation of distribution algorithm, discrete differential evolution algorithm, linear programming, dynamic programming, branch definition and other algorithm [7][8][9][10][11], have been widely used in the JSSP.…”

Section: Introductionmentioning

confidence: 99%

An efficient SAT algorithm for complex job-shop scheduling

Huang¹,

Zhou²

2018

Proceedings of the 2018 8th International Conference on Manufacturing Science and Engineering (ICMSE 2018)

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The job-shop scheduling problem (JSSP) is a typical scheduling problem, which belongs to NP-hard problem. This paper tries to solve the complex JSSP by translating it into Boolean Satisfiability Problem. Even though the representation of JSSP in SAT is not a new issue, the solution to the complex JSSP is still a difficult problem because of processing time too long. In this paper, we optimized the SAT encoding method, thus reducing the number of clauses and their processing efficiency in the solver. We used the Ft20 and ABZ8 problem to experiment, the results show that the optimized coding method can greatly improve the processing efficiency. YN1 are indeed optimal. In addition, they improved the upper bound of YN2 and lower bounds of ABZ8, YN2, YN3 and YN4. Popov [14] proposed using satisfiability algorithms to solve the task-resource scheduling problems in cloud computing system. Cruz-Chávez and Rivera-Lopez [15] gave the application of a local search algorithm for a logical representation of the JSSP problem. Hamadi and Youssef [15] proposed a new clause learning method, which is the important for modern SAT solvers. Micheli and Mishchenko, A extended the SAT formulation to find a minimum-size network under delay constraints [17]. Other scholars have done a lot of work in the field [18].To the exact solution, or the complexity is large, or the optimization performance is poor, it is difficult to high-quality solution complex problems [13]. In this paper, taking into account the SAT algorithm to solve complex JSSP problem takes a long time and other issues, we tries to optimize the SAT encoding method, reducing the number of clauses and their running efficiency in the solver, so as to achieve a more efficient goal of solving complex problems. In section 2, we consider the problem of finding an optimal feasible schedule. In particular, we show the relationship between the graph and JSSP. In section 3, we represent the optimized SAT coding method.

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