scholarly journals An improved memetic algorithm for the flexible job shop scheduling problem with transportation times

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1518-1528
Author(s):  
Guohui Zhang ◽  
Jinghe Sun ◽  
Xixi Lu ◽  
Haijun Zhang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Optimal Solutions ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Annealing Algorithm ◽  
Transportation Times ◽  
Improved Memetic Algorithm

In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability.

An Efficient Memetic Algorithm for Dynamic Flexible Job Shop Scheduling with Random Job Arrivals

2013 ◽  
Vol 5 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Liping Zhang ◽  
Xinyu Li ◽  
Long Wen ◽  
Guohui Zhang
Keyword(s):  
Local Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Dynamic Environment ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Much of the research on flexible job shop scheduling problem has ignored dynamic events in dynamic environment where there are complex constraints and a variety of unexpected disruptions. This paper proposes an efficient memetic algorithm to solve the flexible job shop scheduling problem with random job arrivals. Firstly, a periodic policy is presented to update the problem condition and generate the rescheduling point. Secondly, the efficient memetic algorithm with a new local search procedure is proposed to optimize the problem at each rescheduling point. Five kinds of neighborhood structures are presented in the local search. Moreover, the performance measures investigated respectively are: minimization of the makespan and minimization of the mean tardiness. Finally, several experiments have been designed to test and evaluated the performance of the memetic algorithm. The experimental results show that the proposed algorithm is efficient to solve the flexible job shop scheduling problem in dynamic environment.

Solving the Flexible Job Shop Scheduling Problem Based on Memetic Algorithm

2012 ◽  
Vol 544 ◽  
pp. 1-5 ◽  
Author(s):  
Guo Hui Zhang
Keyword(s):  
Local Search ◽  
Search Strategy ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Flexible job shop scheduling problem (FJSP) is a well known NP-hard combinatorial optimization problem due to its very large search space and many constraint between jobs and machines. Evolutionary algorithms are the most widely used techniques in solving FJSP. Memetic algorithm is a hybrid evolutionary algorithm that combines the local search strategy and global search strategy. In this paper, an effective memetic algorithm is proposed to solve the FJSP. In the proposed algorithm, variable neighborhood search is adopted as local search algorithm. The neighborhood functions is generated by exchanging and inserting the key operations which belong to the critical path. The optimization objective is to minimize makespan. The experimental results obtained from proposed algorithm show that the proposed algorithm is very efficient and effective for all tested problems.

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