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- 저자 : Bingshan Ma (검색결과 2 건)
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The Location Routing Problem of the Car-Sharing System with Autonomous Electric Vehicles
Bingshan Ma,Dawei Hu,Xue Wu 대한토목학회 2021 KSCE JOURNAL OF CIVIL ENGINEERING Vol.25 No.8
Car-sharing, electrification, and autonomous driving are greatly revolutionizing the future of the transportation system. This study proposes a location routing problem for the car-sharing system with autonomous electric vehicles to determine optimal station location and vehicle routing, where each station is both a depot and a charging station. A mathematical model is formulated and then extended to three variants, while simultaneously considering different recharging and service options. The proposed mixed-integer nonlinear models are separately solved by general algebraic modeling system (GAMS) and genetic algorithm (GA), and the efficiency of the GA is demonstrated. The comparative experimental results of instances are presented, and the benefits of allowing partial recharge are obtained. More significant savings in cost can be achieved if partial service is simultaneously allowed. Furthermore, the trade-off between the operator’s interests and the interests of users, as well as the operator’s immediate profits and future profits, are explored through sensitivity analysis.
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The Two Echelon Open Vehicle Routing Problem: Optimization of Crowdshipping Based Parcel Delivery
Xue Wu,Dawei Hu,Bingshan Ma,Ruisen Jiang 대한토목학회 2022 KSCE Journal of Civil Engineering Vol.26 No.9
Crowdshipping is a revolutionary concept of the sharing economy. In this study, two carriers are used to perform the following expedition: the truck starts from the depot to complete part of the deliveries, shares part of the load with the crowdshipper at the relay point, and the private driver selected as the crowdshipper continues from that point onward. This study proposes the two-echelon open vehicle routing problem with crowdshipping (2EOVRP-CS) and formulates a mathematical model to determine the crowdshipper, parcel relay location, truck route, and crowdsource route. A tangible nested genetic algorithm (NGA) is proposed, and its efficiency is demonstrated by comparison with CPLEX and genetic algorithm (GA). A real case study is investigated in Xi'an city to test the applicability of the proposed model. The results show that using crowdshipping instead of truck delivery alone can save approximately 14% of the total cost and 26% of truck vehicle miles traveled (VMT). Moreover, several sensitivity analyses are performed. The results show that crowdshipping is sensitive to the detour limit and the time value of carriers. For the detour limit, after the acceptable detour distance increases by 8%, the total cost can be reduced by up to 5.94%.
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