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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Vydáno v:	4OR Ročník 18; číslo 4; s. 477 - 505
Hlavní autoři:	Charkhgard, Hadi, Takalloo, Mahdi, Haider, Zulqarnain
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020 Springer Nature B.V
Témata:	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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Abstract	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.
AbstractList	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. 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.
Author	Haider, Zulqarnain Charkhgard, Hadi Takalloo, Mahdi
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Cites_doi	10.1109/TITS.2014.2303986 10.3141/2454-10 10.1287/ijoc.2015.0657 10.1016/j.tre.2008.02.008 10.3846/16484142.2017.1347827 10.1109/MITS.2013.2267810 10.1007/s10107-008-0247-4 10.1287/mnsc.6.1.73 10.1016/j.ejor.2007.05.055 10.1016/j.tra.2015.04.003 10.1007/s10479-013-1492-3 10.1016/j.tre.2011.06.003 10.1287/mnsc.2013.1802 10.1016/j.cor.2012.02.021 10.1287/trsc.26.3.161 10.1007/s10957-010-9754-6 10.3141/2567-01 10.1007/s10957-009-9523-6 10.1177/0361198106198600115 10.1287/opre.1120.1136 10.3141/2143-19 10.3141/1992-10 10.1016/j.tre.2015.08.008 10.1109/TITS.2015.2513071 10.1109/TITS.2014.2304358 10.1016/j.tre.2016.05.006
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Snippet	We study the problem of repositioning autonomous vehicles in a shared mobility system in order to simultaneously minimize the unsatisfied demand and the total...
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SubjectTerms	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
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Title	Bi-objective autonomous vehicle repositioning problem with travel time uncertainty
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