The bi-objective multimodal car-sharing problem

被引:3
|
作者
Enzi, Miriam [1 ,2 ]
Parragh, Sophie N. [1 ]
Puchinger, Jakob [3 ,4 ]
机构
[1] Johannes Kepler Univ Linz, Inst Prod & Logist Management, Linz, Austria
[2] AIT Austrian Inst Technol, Ctr Energy, Vienna, Austria
[3] Univ Paris Saclay, Cent Supelec, Lab Genie Industriel, Gif Sur Yvette, France
[4] Inst Rech Technol SystemX, Palaiseau, France
关键词
Car-sharing; Mobility; Transportation; Bi-objective; Branch and cut; TRAVELING SALESMAN PROBLEM; VEHICLE-ROUTING PROBLEM; SCHEDULING PROBLEM; TRADE-OFF; TRANSPORTATION; TIME; SEARCH; ALGORITHMS; EMISSIONS; LOCATION;
D O I
10.1007/s00291-021-00631-2
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
The aim of the bi-objective multimodal car-sharing problem (BiO-MMCP) is to determine the optimal mode of transport assignment for trips and to schedule the routes of available cars and users whilst minimizing cost and maximizing user satisfaction. We investigate the BiO-MMCP from a user-centred point of view. As user satisfaction is a crucial aspect in shared mobility systems, we consider user preferences in a second objective. Users may choose and rank their preferred modes of transport for different times of the day. In this way, we account for, e.g., different traffic conditions throughout the planning horizon. We study different variants of the problem. In the base problem, the sequence of tasks a user has to fulfil is fixed in advance and travel times as well as preferences are constant over the planning horizon. In variant 2, time-dependent travel times and preferences are introduced. In variant 3, we examine the challenges when allowing additional routing decisions. Variant 4 integrates variants 2 and 3. For this last variant, we develop a branch-andcut algorithm which is embedded in two bi-objective frameworks, namely the epsilon-constraint method and a weighting binary search method. Computational experiments show that the branch-and cut algorithm outperforms the MIP formulation and we discuss changing solutions along the Pareto frontier.
引用
收藏
页码:307 / 348
页数:42
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