An Equitable Fuzzy Approach for Facility Delocation: A Case Study of Banks Merging

Document Type : Article

Authors

1 Department of Industrial Management, Allameh Tabatabai University, Tehran, Iran

2 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

3 - The Reliability, Risk, and Maintenance Research Laboratory (RRMR Lab), Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario, Canada - Distributed Systems and Multimedia Processing Laboratory (DSMP lab), Department of Computer Science, Ryerson University, Toronto, Ontario, Canada

Abstract

This paper aims to provide an equitable approach for the delocation via merging different bank branches. Due to the profit loss, some banks have resisted this change, so we developed a n/equity approach to modeling this issue to convince bank owners and employees. The proposed model is a mixed-integer programming model to have an equitable approach to fuzzy constraints based on the weighted sum of the remaining branches to the total number of branches of each type of bank. Moreover, this equitable approach was also used to avoid unemployment of the closed branches staff. Considering the harsh employment conditions and the turmoil of employees after the branched delocation, maximizing the retention of closed branch employees is considered the model's objective function. The result showed that using fuzzy constraints, equity can be well modeled. Moreover, increasing the equity coefficient reduces the number of facilities remaining in the system, and as a consequence, the desired efficiency (i.e., personnel retention) is reduced. So, we can reach the non-dominated answers. Finally, the results showed that reducing the minimum distance between facilities will allow more facilities to remain in the system and retain more staff.

Keywords



Articles in Press, Accepted Manuscript
Available Online from 13 February 2023
  • Receive Date: 20 November 2021
  • Revise Date: 07 February 2022
  • Accept Date: 13 February 2023