Facility location and pricing problem: discretized mill price and exact algorithms
The joint optimization of facility location and service charge arises in many industrial and business contexts. This paper investigates a facility location and mill pricing problem (FLMPr), where a company aims to maximize its profit by locating service facilities and setting appropriate service cha...
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sg-ntu-dr.10356-1690622023-06-28T00:48:50Z Facility location and pricing problem: discretized mill price and exact algorithms Lin, Yun Hui Tian, Qingyun School of Civil and Environmental Engineering Engineering::Civil engineering Facilities Planning And Design Location The joint optimization of facility location and service charge arises in many industrial and business contexts. This paper investigates a facility location and mill pricing problem (FLMPr), where a company aims to maximize its profit by locating service facilities and setting appropriate service charges to customers. For each facility, the number of pricing levels are finite, and the company will select exactly one level for each facility if it is open. We visualize the problem from a fully decentralized perspective, i.e., each customer acts as an independent decision-maker. Under mill pricing, customers visiting the same facility encounter the same service charge. The problem is formulated as a bilevel program, in which the company makes location and pricing decisions at the upper level, and customers decide whether to seek the service from a facility at the lower level. To solve FLMPr, we leverage three types of closest assignment constraints to reformulate the problem as mixed-integer linear programs (MILPs), which can be directly solved by modern solvers. However, this approach suffers from a time-consuming solver compiling process and cannot handle large-scale instances effectively. This observation motivates us to design a branch-and-cut algorithm by exploring the bilevel structure and deriving a feasibility cut to efficiently eliminate bilevel infeasible solutions. Our extensive experiments reveal that the proposed algorithm can solve large-scale FLMPr satisfactorily and outperforms the MILP approach by a large margin. Finally, we conduct sensitivity analysis and draw interesting observations. 2023-06-28T00:48:50Z 2023-06-28T00:48:50Z 2023 Journal Article Lin, Y. H. & Tian, Q. (2023). Facility location and pricing problem: discretized mill price and exact algorithms. European Journal of Operational Research, 308(2), 568-580. https://dx.doi.org/10.1016/j.ejor.2022.11.052 0377-2217 https://hdl.handle.net/10356/169062 10.1016/j.ejor.2022.11.052 2-s2.0-85147008796 2 308 568 580 en European Journal of Operational Research © 2022 Elsevier B.V. All rights reserved. |
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Engineering::Civil engineering Facilities Planning And Design Location Lin, Yun Hui Tian, Qingyun Facility location and pricing problem: discretized mill price and exact algorithms |
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The joint optimization of facility location and service charge arises in many industrial and business contexts. This paper investigates a facility location and mill pricing problem (FLMPr), where a company aims to maximize its profit by locating service facilities and setting appropriate service charges to customers. For each facility, the number of pricing levels are finite, and the company will select exactly one level for each facility if it is open. We visualize the problem from a fully decentralized perspective, i.e., each customer acts as an independent decision-maker. Under mill pricing, customers visiting the same facility encounter the same service charge. The problem is formulated as a bilevel program, in which the company makes location and pricing decisions at the upper level, and customers decide whether to seek the service from a facility at the lower level. To solve FLMPr, we leverage three types of closest assignment constraints to reformulate the problem as mixed-integer linear programs (MILPs), which can be directly solved by modern solvers. However, this approach suffers from a time-consuming solver compiling process and cannot handle large-scale instances effectively. This observation motivates us to design a branch-and-cut algorithm by exploring the bilevel structure and deriving a feasibility cut to efficiently eliminate bilevel infeasible solutions. Our extensive experiments reveal that the proposed algorithm can solve large-scale FLMPr satisfactorily and outperforms the MILP approach by a large margin. Finally, we conduct sensitivity analysis and draw interesting observations. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Lin, Yun Hui Tian, Qingyun |
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Lin, Yun Hui Tian, Qingyun |
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Lin, Yun Hui |
title |
Facility location and pricing problem: discretized mill price and exact algorithms |
title_short |
Facility location and pricing problem: discretized mill price and exact algorithms |
title_full |
Facility location and pricing problem: discretized mill price and exact algorithms |
title_fullStr |
Facility location and pricing problem: discretized mill price and exact algorithms |
title_full_unstemmed |
Facility location and pricing problem: discretized mill price and exact algorithms |
title_sort |
facility location and pricing problem: discretized mill price and exact algorithms |
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2023 |
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https://hdl.handle.net/10356/169062 |
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1772827461356093440 |