Comparison of lot streaming division methodologies for multi-objective hybrid flowshop scheduling problem by considering limited waiting time


Gürsoy Yılmaz B., Yılmaz Ö. F., Yeni F. B.

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, vol.1, pp.1-42, 2024 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 1
  • Publication Date: 2024
  • Doi Number: 10.3934/jimo.2024058
  • Journal Name: JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, MathSciNet, zbMATH
  • Page Numbers: pp.1-42
  • Karadeniz Technical University Affiliated: Yes

Abstract

In this paper, a multi-objective hybrid flowshop scheduling problem (HFSP) with limited waiting time and machine capability constraints is addressed. Given its importance, the implementation of lot streaming division methodologies with the problem is investigated through a design of experiment (DoE) setting based on real data extracted from a leading tire manufacturer in Gebze, Turkey. By doing so, specific characteristics of the addressed HFSP can be further explored to provide insights into its complexity and suggest recommendations for improving the operational efficiency of such systems resembling it. Based on the problem specifications and constraints, a novel generic multi-objective optimization model with objectives including the makespan, the average flow time, and the total workload imbalance is formulated. Since the studied problem is NP-hard in the strong sense, several algorithms based on the non-dominated sorting genetic algorithm-II (NSGA-II) are proposed according to the division methodologies, i.e., consistent sublots and equal sublots. Since the main aim of this problem is to further analyze the implementation of lot streaming on the HFSP problem, the developed algorithms are compared with each other to gain remarkable insights into the problem. Four different comparison metrics are employed to assess the solution quality of the proposed algorithms in terms of intensification and diversification aspects. Computational results demonstrate that employing the consistent sublot methodology leads to significant improvements in all metrics compared to the equal sublot methodology.