.png){kind=icon}
.png){kind=icon}
Return
A Hybrid Nested Partitions Algorithm for Banking Facility Location Problems
.png){kind=icon}
PRE
AI
DOI：10.1109/TASE.2010.2043430.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The facility location problem has been studied in many industries including banking network, chain stores, and wireless network. Maximal covering location problem (MCLP) is a general model for this type of problems. Motivated by a real-world banking facility optimization project, we propose an enhanced MCLP model which captures the important features of this practical problem, namely, varied costs and revenues, multitype facilities, and flexible coverage functions. To solve this practical problem, we apply an existing hybrid nested partitions algorithm to the large-scale situation. We further use heuristic-based extensions to generate feasible solutions more efficiently. In addition, the upper bound of this problem is introduced to study the quality of solutions. Numerical results demonstrate the effectiveness and efficiency of our approach. Note to Practitioners-This paper is motivated by a practical banking facility location problem. The problem is how to choose the facilities (bank branches) location in order to maximize the facility network's profits. It is a large-scale optimization problem in the real world. We formulate this problem with an extended MCLP model and apply a hybrid nested partitions algorithm. Our approach is efficient since it combines the mathematical programming and problem specific heuristic information. Practitioners who want to use this approach should pay attention to the utility of problem structure and model formulation. This approach is also applicable to other location problems, such as the retail chain stores, gas stations, city public facilities, and so on.
Keywords:
Banking facility
maximal covering location problem
mixed integer programming
nested partitions algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.4
Papers:
4.9K
Citations:
1.6W
View more organizations