Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm

A hybrid genetic algorithm based on Pareto was proposed and applied to the flexible job shop scheduling problem (FJSP) with bi-objective, and the bi-objective FJSP optimization model was built, where the make-span and the production cost were concerned. The algorithm embeds Pareto ranking strategy into Pareto competition method, and the niche technology and four kinds of crossover operations are used in order to promote solution diversity. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. In this paper, the influence of the proportion of workers and machines on the scheduling result is researched on the basis of the hybrid genetic algorithm and the result is in accord with other researchers. In conclusion, the algorithm proposed in this paper is available and efficient.

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A New Hybrid Optimization Algorithm and its Application in Job Shop Scheduling

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.55-57.1789 ◽

2011 ◽

Vol 55-57 ◽

pp. 1789-1793

Author(s):

Xian Zhou Cao ◽

Zhen He Yang

Keyword(s):

Genetic Algorithm ◽

Production Cost ◽

Job Shop ◽

Job Shop Scheduling ◽

Hybrid Genetic Algorithm ◽

Hybrid Optimization ◽

Job Shop Scheduling Problem ◽

Shop Scheduling ◽

Hybrid Optimization Algorithm ◽

Active Schedule

In this paper, a dual-resource constrained job shop scheduling problem was studied by designing a hybrid genetic algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA). GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one. The combination of GA and SA utilizes the advantages of the two algorithms and overcomes their disadvantages. The operation-based encoding and an active schedule decoding method were employed. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. The results of numerical simulations, which are compared with those of other well-known algorithms, show better performance of the proposed algorithm.

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Extended Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problem

International Journal of Science and Engineering Applications ◽

10.7753/ijsea1002.1001 ◽

2021 ◽

Vol 10 (02) ◽

pp. 017-020

Author(s):

Sumaia E. Eshim ◽

Mohammed M. Hamed

Keyword(s):

Genetic Algorithm ◽

Hybrid Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Hybrid Genetic Algorithm ◽

Benchmark Problems ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Solution Quality ◽

Shop Scheduling

In this paper, a hybrid genetic algorithm (HGA) to solve the job shop scheduling problem (JSSP) to minimize the makespan is presented. In the HGA, heuristic rules are integrated with genetic algorithm (GA) to improve the solution quality. The purpose of this research is to investigate from the convergence of a hybrid algorithm in achieving a good solution for new benchmark problems with different sizes. The results are compared with other approaches. Computational results show that a hybrid algorithm is capable to achieve good solution for different size problems.

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