Article: An efficient approach for solving a job shop scheduling problem with resources constraints: a case study iCIM 3000 Journal: International Journal of Operational Research (IJOR) 2023 Vol.46 No.1 pp.73 - 92 Abstract: In this work, we are interested in a job shop scheduling problem (JSSP) with resources availability constraints. The aim consists in scheduling a set of N jobs on M machines. To be processed in the system, each job needs an number of consumable resources that are available in a limited quantity. Solving such a problem means finding better jobs sequencing in order to minimise the maximum execution time. We suggest two different methods to solve the above-mentioned problem. We firstly propose a set of four heuristics based on priority rules. Then, we make call to genetic algorithm. Using a real job shop manufacturing system data, a large-scale experiment was performed in order to analyse the performance of the proposed methods. The studied system is called iCIM 3000. The simulation results reveal that the new proposed heuristics are better than genetic algorithms and achieve to good solutions in shorter time. Inderscience Publishers - linking academia, business and industry through research

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Title: An efficient approach for solving a job shop scheduling problem with resources constraints: a case study iCIM 3000

Authors: Abdelkader Hadri; Aimade Eddine Bougloula; Fayçal Belkaid; Hacene Smadi

Addresses: Department of Industrial Engineering, University of Batna 02, 53, route de Constantine, Fesdis Batna 05078, Algeria; Department of Electrical and Electronic Engineering, University of Tlemcen, PB 230, Tlemcen, 13000, Algeria ' Department of Industrial Engineering, University of Batna 02, 53, route de Constantine, Fesdis Batna 05078, Algeria ' Manufacturing Engineering Laboratory of Tlemcen (MELT), Department of Electrical and Electronic Engineering, University of Tlemcen, PB 230, Tlemcen, 13000, Algeria ' Laboratory of Automation and Manufacturing (LAP), University of Batna 02, 53, route de Constantine, Fesdis Batna 05078, Algeria

Abstract: In this work, we are interested in a job shop scheduling problem (JSSP) with resources availability constraints. The aim consists in scheduling a set of N jobs on M machines. To be processed in the system, each job needs an number of consumable resources that are available in a limited quantity. Solving such a problem means finding better jobs sequencing in order to minimise the maximum execution time. We suggest two different methods to solve the above-mentioned problem. We firstly propose a set of four heuristics based on priority rules. Then, we make call to genetic algorithm. Using a real job shop manufacturing system data, a large-scale experiment was performed in order to analyse the performance of the proposed methods. The studied system is called iCIM 3000. The simulation results reveal that the new proposed heuristics are better than genetic algorithms and achieve to good solutions in shorter time.

Keywords: job shop; non-renewable resources; heuristics; genetic algorithms; iCIM 3000.

DOI: 10.1504/IJOR.2023.128581

International Journal of Operational Research, 2023 Vol.46 No.1, pp.73 - 92

Received: 08 Nov 2019
Accepted: 20 Feb 2020
Published online: 26 Jan 2023 *

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Title: An efficient approach for solving a job shop scheduling problem with resources constraints: a case study iCIM 3000

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