The bi-objective multimodal car-sharing problem
| Autor: | Jakob Puchinger, Sophie N. Parragh, Miriam Enzi |
|---|---|
| Přispěvatelé: | Austrian Institute of Technology [Vienna] (AIT), Johannes Kepler University Linz [Linz] (JKU), IRT SystemX (IRT SystemX), Laboratoire Génie Industriel (LGI), CentraleSupélec-Université Paris-Saclay |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Mathematical optimization Schedule Binary search algorithm Computer Science - Artificial Intelligence Computer science 0211 other engineering and technologies Time horizon 02 engineering and technology Management Science and Operations Research 0502 economics and business 11. Sustainability FOS: Mathematics Mathematics - Optimization and Control 050210 logistics & transportation Sequence 021103 operations research 05 social sciences Rank (computer programming) Pareto principle [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] Constraint (information theory) Artificial Intelligence (cs.AI) Optimization and Control (math.OC) Business Management and Accounting (miscellaneous) Routing (electronic design automation) |
| Zdroj: | OR Spectrum OR Spectrum, Springer Verlag, 2021, ⟨10.1007/s00291-021-00631-2⟩ |
| ISSN: | 0171-6468 1436-6304 |
| DOI: | 10.1007/s00291-021-00631-2⟩ |
| Popis: | 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-and-cut algorithm which is embedded in two bi-objective frameworks, namely the $$\epsilon $$ ϵ -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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink