Optimization of driver-parcel matching for crowdsourced intra-city delivery with multi-hop and detour
This paper examines the problem of driver-parcel matching for crowdsourced intra-city delivery.A delivery strategy is proposed in which retail stores are served as parcels pickup or multi-hop nodes and private car drivers drop by the way.The problem of crowdsourced parcel intra-city delivery with multi-hop and detour is formulated as a mixed integer programming(MIP)model,where the total revenue of platform is maximized.An improved adaptive large neighborhood search(ALNS)algorithm is developed to solve the model.A numerical example taking the main urban area of Dalian as an actual scenario verifies the effectiveness and applicability of the model and algorithm.The research indicates that the crowdsourced parcel intra-city delivery with multi-hop and detour can increase the total revenue of the platform,and benefit both private car drivers and retail stores.In addition,it also helps to alleviate urban road congestion and reduce environmental pollution.Sensitivity analysis results show that increasing the number of contracted private car drivers and the maximum detour coefficient within a certain range can help improve the success rate of driver-parcel matching and the total revenue of the platform.Furthermore,as the time urgency for parcel delivery weakens,the advantages of crowdsourced intra-city parcel delivery with the participation of private cars drivers and retail stores become more and more obvious.
crowdsourced intra-city deliverydriver-parcel matchingmulti-hopdetourMIP model
万方数据
维普
