Capability-based machine layout with a matheuristic-based approach


EXPERT SYSTEMS WITH APPLICATIONS, vol.198, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 198
  • Publication Date: 2022
  • Doi Number: 10.1016/j.eswa.2022.116900
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Facilities planning and design, Capability-based machine layout, Matheuristic, Integer nonlinear programming, SIMULATED ANNEALING ALGORITHM, DISTRIBUTED LAYOUT, LOCAL SEARCH, OPTIMIZATION
  • Van Yüzüncü Yıl University Affiliated: Yes


Capability-based machine layout (CB-ML) problem is firstly introduced in this paper. In the conventional ma-chine layout problem, part flow matrix is generated from parts' machine routes to minimize total part flows. However, defining part flow matrix based on the machines' routes (instead of processing capability requirements of parts) restricts facility designers to utilize available flexibility in manufacturing systems. In this research, parts' processing requirements are defined in terms of Resource Elements (REs), which describe unique pro-cessing capabilities and the processing capability overlaps of machines. If part flow matrix is defined in terms of REs, it becomes possible to utilize available flexibility in a more effective manner. However, physical part flows cannot be identified directly from the RE-based flow matrices. Because, the processing requirements of manu-factured parts can be satisfied from alternative machines that contain the required REs. Therefore, RE-based part flow matrix must be mapped into the machine flow matrix, which requires defining the machine flow matrix as a decision variable. This makes the proposed CB-ML problem much more complicated than the conventional machine layout problem. We firstly developed an integer non-linear programming model for the proposed CB-ML problem. Because of its NP-completeness and nonlinear structure, a matheuristic-based solution approach is also developed. The extensive computational analysis have shown that the proposed approach is able to provide good quality solutions for the larger problem instances within reasonable computation times.