Flexible job-shop scheduling is significant for different manufacturing industries nowadays. Moreover, consideration of transportation time during scheduling makes it more practical and useful. The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem (MOFJSP) considering transportation time.
A hybrid genetic algorithm (GA) approach is integrated with simulated annealing to solve the MOFJSP considering transportation time, and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.
The performance of the proposed algorithm is tested on different MOFJSP taken from literature. Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution, especially when the number of jobs and the flexibility of the machine increase.
Most of existing studies have not considered the transportation time during scheduling of jobs. The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs. Meanwhile, GA is one of primary algorithms extensively used to address MOFJSP in literature. However, to solve the MOFJSP, the original GA has a possibility to get a premature convergence and it has a slow convergence speed. To overcome these problems, a new hybrid GA is developed in this paper.
This work is financially supported by National Social Science Foundation of China under the project of 18BGL003.
Huang, X. and Yang, L. (2019), "A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time", International Journal of Intelligent Computing and Cybernetics, Vol. 12 No. 2, pp. 154-174. https://doi.org/10.1108/IJICC-10-2018-0136
Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited