期刊
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
卷 60, 期 5, 页码 1439-1457出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2020.1859635
关键词
Ridesharing; multi-objective optimisation; cumulative prospect theory; heuristic algorithm; VNS-NSGAII; city logistics
资金
- National Natural Science Foundation of China [71521001, 71871080, 72071056, 71922009, 71690235]
- Natural Science Foundation of Anhui Province [1908085MG223, 2008085QG341]
- Fundamental Research Funds for the Central Universities [JZ2019HGTA0051, JZ2019HGBZ0131]
- Base of Introducing Talents of Discipline to Universities for Optimization and Decision-making in the Manufacturing Process of Complex Product (111 project)
- Humboldt Research Award (Germany)
This paper studies the ridesharing problem in urban transportation aiming to enlarge the ridesharing market at a limited cost. A novel multi-objective optimization model based on cumulative prospect theory is established to address the preferred travel mode of commuters. The perceived value of commuters influences their choice of travel mode.
The daily home-office commute of millions of people in crowded cities strains air quality and increases travel time, which motivates the generation of ridesharing. Ridesharing offers many benefits, such as reducing travel costs, congestion, and pollution. Commuter ridesharing is an important theme of urban transportation. This paper studies a ridesharing problem aiming at enlarging the ridesharing market at a limited cost, which enlighten the decision-making problem in city logistics. We establish a novel multi-objective optimisation model based on cumulative prospect theory (CPT) to address the preferred travel mode of commuters. The commuters' perceived value influences their choice of travel mode. Meanwhile, the perceived value changes with the commuters' experience of travel mode choice. We give the NP-hardness proof of the ridesharing scheduling problem and develop a heuristic algorithm to solve it in a small-scale scenario. For large-scale problems, a hybrid VNS-NSGAII algorithm combining variable neighbourhood search (VNS) with NSGAII (Non-dominated Sorting Genetic Algorithm II) is proposed to generate an approximate optimal Pareto front. A series of computational experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm based on the actual traffic data in Beijing, China.
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