Cost-Profit Trade-Off for Optimally Locating Automotive Service Firms under Uncertainty

Document Type

Article

Publication Date

2-1-2021

Abstract

This work investigates the problem of optimally locating an automotive service firm (ASF) subject to stochastic customer demands, varying setup cost and regional constraints. The goal is to minimize the transportation cost while maintaining the specified profit of the ASF. This work studies two variants of the problem: ASF location with known demand probability distributions and with partial demand information, i.e., only the support and mean of the customer demands are known. For the former, a chance-constrained program is formulated that improves an existing model, and then an equivalent deterministic nonlinear program is constructed based on our property analysis results. For the latter, a novel distribution-free model is developed. The proposed models are solved by solver LINGO. Computational results on the benchmark examples show that: i) for the first variant, the proposed approach outperforms the existing one; ii) for the second one, the proposed distribution-free model can effectively handle stochastic customer demands without complete probability distributions; and iii) the results of the distribution-free model are slightly worse than those of the deterministic nonlinear one, but the former is more cost-efficient for the practical ASF location as it is less expensive in obtaining demand information. Moreover, the proposed models and approaches are extended to address a multi-ASF location allocation under demand uncertainty.

Identifier

85081931600 (Scopus)

Publication Title

IEEE Transactions on Intelligent Transportation Systems

External Full Text Location

https://doi.org/10.1109/TITS.2019.2961929

e-ISSN

15580016

ISSN

15249050

First Page

1014

Last Page

1025

Issue

2

Volume

22

Grant

FJ2018JDZ024

Fund Ref

National Natural Science Foundation of China

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