Title: Modified ant colony algorithm for job shop scheduling problem

Authors: Ye Li; Ning Wang; Kun Xu

Addresses: School of Maritime Economics and Management, Dalian Maritime University, Dalian, China ' School of Maritime Economics and Management, Dalian Maritime University, Dalian, China ' School of Maritime Economics and Management, Dalian Maritime University, Dalian, China

Abstract: In this work, we proposed a modified ant colony algorithm (ACA) for job shop scheduling problem (JSSP) with make-span, and constraints such as machine selection, time lags, and holding times, process, and sequence are taken into account. The two-stage setup of the pheromone update mechanism allows for a combination of local and global pheromone updates. In the first stage, the pheromone is updated locally for each completed process, and after the set iteration conditions have been met, the second stage is entered. To overcome the initial reliance on pheromones in the ACA, the pheromones are initialised using a genetic algorithm (GA). The optimal convergence ratio is obtained through the design of a genetic operator based on the procedure principle to accelerate the convergence effect of the whole algorithm and improve the global searching ability of ACA. Taking an engine company as an example, several simulation experiments are carried out for GA, ACA, and modified ant colony algorithm (MACA) based on the standard dataset to verify the effectiveness of proposed algorithms.

Keywords: job shop scheduling problem; JSSP; ant colony algorithm; ACA; genetic algorithm; modified ant colony algorithm; MACA; optimal convergence ratio.

DOI: 10.1504/IJISE.2024.138026

International Journal of Industrial and Systems Engineering, 2024 Vol.46 No.4, pp.475 - 508

Received: 20 Dec 2021
Accepted: 29 May 2022
Published online: 17 Apr 2024 *

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