Bi-objective autonomous vehicle repositioning problem with travel time uncertainty

We study the problem of repositioning autonomous vehicles in a shared mobility system in order to simultaneously minimize the unsatisfied demand and the total operating cost. We first present a mixed integer linear programming formulation for the deterministic version of the problem. We extend this...

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Veröffentlicht in:4OR Jg. 18; H. 4; S. 477 - 505
Hauptverfasser: Charkhgard, Hadi, Takalloo, Mahdi, Haider, Zulqarnain
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020
Springer Nature B.V
Schlagworte:
Algorithms
Business and Management
Industrial and Production Engineering
Integer programming
Linear programming
Mixed integer
Operations research
Operations Research/Decision Theory
Optimization
Research Paper
Travel time
Uncertainty
Bi-objective integer linear programming
90C11
Chanced-constraint programming
90C29
90C15
Autonomous vehicle repositioning problem
90B06
ISSN:1619-4500, 1614-2411
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Zusammenfassung:We study the problem of repositioning autonomous vehicles in a shared mobility system in order to simultaneously minimize the unsatisfied demand and the total operating cost. We first present a mixed integer linear programming formulation for the deterministic version of the problem. We extend this formulation to make it easier to work with in the non-deterministic setting. We then show how the travel time uncertainty can be incorporated into this extended deterministic formulation using chance-constraint programming. Finally, two new reformulations for the proposed chance-constraint program are developed. We show a critical result that the size of one of the reformulations (in terms of the number of variables and constraints) does not depend on the number of scenarios, and so it outperforms the other reformulation. Both reformulations are bi-objective mixed integer linear programs with a finite number of nondominated points and so they can be solved directly by algorithms such as the balanced box method (Boland et al. in INFORMS J Comput 27(4):735–754, 2015). A computational study demonstrates the efficacy of the proposed reformulations.
Bibliographie:ObjectType-Article-1
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ISSN:1619-4500
1614-2411
DOI:10.1007/s10288-019-00429-7