Title: Job shop scheduling in Wafer Fab with random job arrivals using hybrid tabu search technique

Authors: Rosshairy Abd. Rahman; Kean Heong Lee; Syariza Abdul-Rahman; Azhar Mahdi Ibadi

Addresses: Institute of Strategic Industrial Decision Modelling (ISIDM), School of Quantitative Sciences, Universiti Utara Malaysia, Kedah, Malaysia ' Ams-Osram, Jalan Hi-tech 1, Kulim Hi-tech Park, 09000 Kulim, Kedah, Malaysia ' Institute of Strategic Industrial Decision Modelling (ISIDM), School of Quantitative Sciences, Universiti Utara Malaysia, Kedah, Malaysia ' Department of Physics, College of Science, University of Sumer, Thi-Qar, Iraq

Abstract: Job shop scheduling (JSS) plays an important role in the manufacturing sector to minimise makespan and avoid bottleneck situations. Wafer fabrication (Wafer Fab) is a costly and complex manufacturing system that demands several billion dollars investment with many repeated processes. Scheduling of Wafer Fab is vital to produce wafers and to achieve on-time delivery to customers. As such, this study proposes a hybrid tabu search model to solve JSS problem by minimising makespan while concurrently maximising the average machine utilisation. Initially, the concept of bin packing was deployed to obtain the initial solution using random greedy algorithm. Tabu search algorithm (TSA) was then employed to enhance solutions around the neighbourhood area. The findings successfully improved the schedule of jobs in Wafer Fab manufacturing, where the enhancement of 6.2% in makespan minimisation was achieved. Thus, it shows the ability to utilise the machines equally and avoid bottlenecks in Wafer Fab manufacturing.

Keywords: job shop scheduling; JSS; random greedy bin packing; Wafer Fab; tabu search algorithm; TSA; makespan; manufacturing; tabu tenure size; metaheuristics; bottleneck; utilisation.

DOI: 10.1504/IJMOR.2025.147858

International Journal of Mathematics in Operational Research, 2025 Vol.31 No.3, pp.397 - 412

Received: 17 Sep 2023
Accepted: 20 Sep 2023
Published online: 05 Aug 2025 *

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