In:
Journal Européen des Systèmes Automatisés, International Information and Engineering Technology Association, Vol. 53, No. 6 ( 2020-12-23), p. 915-924
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1269-6935
,
2116-7087
DOI:
10.18280/jesa.530617
Language:
Unknown
Publisher:
International Information and Engineering Technology Association
Publication Date:
2020
detail.hit.zdb_id:
2390481-1
Permalink
