Cost-based hybrid flow shop scheduling with uniform machine optimization using an improved tiki-taka algorithm

ABSTRACTCost is the foremost factor in decision-making for profit-driven organizations. However, hybrid flow shop scheduling (HFSS) research rarely prioritizes cost as its optimization objective. Existing studies primarily focus on electricity costs linked to machine utilization. This paper introduces a comprehensive cost-based HFSS model, encompassing electricity, labor, maintenance, and penalty costs. Next, the Tiki-Taka Algorithm (TTA) is improved by increasing the exploration capability to optimize the problem. The cost-based HFSS model and TTA algorithm have been tested using benchmark and case study problems. The results indicated that the TTA consistently outperforms other algorithms. It delivers the best mean fitness and better solution distribution. In industrial contexts, the TTA able to reduces costs by 2.8% to 12.0% compared to other approaches. This holistic cost-based HFSS model empowers production planners to make more informed decisions. Furthermore, the improved TTA shows promise for broader applicability in various combinatorial optimization domains.KEYWORDS: Hybrid flow shop schedulingproduction costtiki-taka algorithmcost optimization AcknowledgmentsThe authors would like to acknowledge Universiti Malaysia Pahang for funding this research under the UMP Grant RDU223017.Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe datasets generated during and/or analyzed during the current study are available as follow:(i) Computational experiment purpose: Carlier, J., & Neron, E. (2000). An Exact Method for Solving the Multi-Processor Flow-Shop. RAIRO-Oper. Res., 34(1), 1–25. https://doi.org/10.1051/ro:2000103.(ii) Practical application data: Data available on request from the authors.Supplemental materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/21681015.2023.2276108Additional informationFundingThe work was supported by the Universiti Malaysia Pahang [RDU223017].Notes on contributorsMohd Fadzil Faisae Ab RashidDr. Mohd Fadzil Faisae Ab. Rashid is currently an Associate Professor at the Faculty of Mechanical & Automotive Engineering Technology, University Malaysia Pahang. He received a Bachelor’s Degree in Mechanical (Industry) from Universiti Teknologi Malaysia in 2003, a Master of Engineering (Manufacturing) from Universiti Malaysia Pahang in 2007, and a Ph.D. in Manufacturing System Optimization from Cranfield University, the United Kingdom in 2013. His research interests are in engineering optimization, particularly focusing on manufacturing systems, metaheuristics, and discrete event simulation techniques.Muhammad Ammar Nik Mu’tasimMr. Muhammad Ammar Nik Mu’tasim is a lecturer at the Faculty of Mechanical & Automotive Engineering Technology, University Malaysia Pahang. He holds a Bachelor's degree in Mechanical Engineering from Universiti Malaysia Pahang, as well as a Master of Engineering in Mechanical Engineering from University Teknologi Malaysia and a Master of Science from SUNY Buffalo, New York, USA. His research focuses on manufacturing optimization, Computational Fluid Dynamics (CFD), and energy systems. With his passion for innovative engineering solutions, Mr. Mu’tasim continues to contribute significantly to the field of mechanical and automotive engineering.