A Multi-Objective Variable Neighborhood Search Algorithm for Precast Production Scheduling

Authors

DOI:

https://doi.org/10.4186/ej.2020.24.6.139 Full article

Abstract

In real life, precast production schedulers face the challenges of creating a reasonable schedule to satisfy multiple conflicting objectives. Practical constraints and objectives encountered in the precast production scheduling problem (PPSP) were addressed, with the goal to minimize makespan and total earliness and tardiness penalties. A multi-objective variable neighborhood search (MOVNS) algorithm was proposed and the performance was tested on 11 problem instances. Ten of these were generated using precast concrete production information taken from the literature. One real industrial problem from a precast concrete company was considered as a case study. Extensive experiments were conducted, and the spread and distance metrics were used to evaluate the quality of the non-dominated solutions set. Statistical analysis demonstrated that the result was statistically convincing. Computational results showed that the proposed MOVNS algorithm was significantly better when compared to the other nine algorithms. Therefore, the proposed MOVNS algorithm was a very competitive method for the considered PPSP.

Keywords:

precast production scheduling, multi-objective, metaheuristic, variable neighborhood search, spread and distance

Affiliations

  • Zong Lehuang Prince of Songkla University
  • Wanatchapong Kongkaew Prince of Songkla University

Corresponding author: Wanatchapong Kongkaew, wanatchapong.k@psu.ac.th

1113 1238

Author Biographies

  • Department of Industrial Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90110, Thailand

  • Department of Industrial Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90110, Thailand

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How to Cite

[1]
Z. Lehuang and W. Kongkaew, “A Multi-Objective Variable Neighborhood Search Algorithm for Precast Production Scheduling”, Eng. J., vol. 24, no. 6, pp. 139–157, Nov. 2020, doi: 10.4186/ej.2020.24.6.139.

Citations

Published

2020-11-30

Issue

Section

Modern Engineering Technology