EQUIPMENT RESOURCE OPTIMIZATION BASED ON EFFICIENCY EVALUATION AND COMPARATIVE ADVANTAGE

Authors

  • Youyuan Wang Nanchang Hangkong University
  • Rui Liu Nanchang Hangkong University
  • Weiwei Qian Nanchang Hangkong University
  • Changbin Xu Jiangxi Teachers College

DOI:

https://doi.org/10.23055/ijietap.2020.27.5.6409

Keywords:

Effectiveness Evaluation, Particle Swarm Optimization, Multi-Attribute Decision Making, Equipment Resources Optimization

Abstract

In this study, a method for optimizing equipment resources based on efficiency evaluation and the relative advantage relationship is proposed. An efficiency evaluation and optimization model are also constructed, and the corresponding algorithm is designed. The effects of altering the decision-matrix canonical method and quantity discount on multi-attribute decision-making are studied by investigating the available feasible solutions. The results show that the proposed efficiency evaluation and optimization model can yield several groups of optimal feasible solutions. Additionally, combining it with a multi-attribute decision model can enable it to effectively select and rank feasible solutions while avoiding the adverse effect of different decision-specification methods on the decision-making result. The decision method and quantity discount considerably influence the multi-attribute decision-making results, implying their importance when optimizing the equipment resources with respect to a manufacturing process.

Author Biographies

Youyuan Wang, Nanchang Hangkong University

Institute of Industry and Engineering

Rui Liu, Nanchang Hangkong University

College of Aeronautical Manufacturing Engineering

Weiwei Qian, Nanchang Hangkong University

College of Aeronautical Manufacturing Engineering

Changbin Xu, Jiangxi Teachers College

College of Aviation and Tourism

Published

2021-04-29

How to Cite

Wang, Y., Liu, R., Qian, W., & Xu, C. (2021). EQUIPMENT RESOURCE OPTIMIZATION BASED ON EFFICIENCY EVALUATION AND COMPARATIVE ADVANTAGE. International Journal of Industrial Engineering: Theory, Applications and Practice, 27(5). https://doi.org/10.23055/ijietap.2020.27.5.6409

Issue

Section

Special Issue on Data-driven Computational Intelligence in Industries Application