Heuristics for the two-stage job shop scheduling problem with a bottleneck machine

2000 ◽  
Vol 123 (2) ◽  
pp. 229-240 ◽  
Author(s):  
I.G. Drobouchevitch ◽  
V.A. Strusevich
2020 ◽  
Vol 53 (6) ◽  
pp. 915-924
Author(s):  
Jianfeng Ren ◽  
Chunming Ye ◽  
Yan Li

This paper solves the job-shop scheduling problem (JSP) considering job transport, with the aim to minimize the maximum makespan, tardiness, and energy consumption. In the first stage, the improved fast elitist nondominated sorting genetic algorithm II (INSGA-II) was combined with N5 neighborhood structure and the local search strategy of nondominant relationship to generate new neighborhood solutions by exchanging the operations on the key paths. In the second stage, the ant colony algorithm based on reinforcement learning (RL-ACA) was designed to optimize the job transport task, abstract the task into polar coordinates, and further optimizes the task. The proposed two-stage algorithm was tested on small, medium, and large-scale examples. The results show that our algorithm is superior to other algorithms in solving similar problems.


2011 ◽  
Vol 21 (12) ◽  
pp. 3082-3093
Author(s):  
Zhu-Chang XIA ◽  
Fang LIU ◽  
Mao-Guo GONG ◽  
Yu-Tao QI

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