Title: Developing a genetic-based multi-objective algorithm to optimise job shop scheduling problems

Authors: Mohammed Hussein; Abd_Elrahman Elgendy

Addresses: Mechanical Engineering Department, British University, Shorouk City, Cairo, 11837, Egypt ' Mechanical Engineering Department, Benha University, Banha, 13512, Egypt

Abstract: Dynamic job shop scheduling is one of the problems that get little attention in literature as it is known as an NP-hard combinatorial optimisation problem. Few researchers handled the mathematical model and the approaches of optimising the schedule efficiency and stability. As events such as (machine breakdown, arriving new jobs or processing time variation) are hard to be formulated in a mathematical model, this research introduces a dynamic multi-objective genetic algorithm based on partial repair reactive strategy. The reactive strategy is selected to deal with the dynamic nature of job shop by applying partial repair policy for optimising the scheduling efficiency and the schedule stability simultaneously. Experimental results show that the proposed algorithm provided better solutions than key problem solutions in dynamic job shop scheduling problems published in literature.

Keywords: dynamic job shop scheduling; repair strategy; scheduling efficiency and stability; genetic algorithm.

DOI: 10.1504/IJCENT.2018.092074

International Journal of Collaborative Enterprise, 2018 Vol.6 No.1, pp.1 - 19

Received: 05 Dec 2016
Accepted: 01 Aug 2017
Published online: 31 May 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article