Optimizing the Location of Virtual-Shopping-Experience Stores Based on the Minimum Impact on Urban Traffic

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Abstract
In order to enhance consumers’ experience of online shopping and to reduce their unnecessary car trips for offline shopping, a new mode, namely, establishing the virtual-shopping-experience store, is proposed in this paper. A bi-level programming model is then built with the aim of optimizing the location of the virtual-shopping-experience stores. The upper-level submodel is utilized to optimize the location of the experience stores, as well as the selection of virtual-reality (VR) devices purchased by the stores, by maximizing the social welfare generated from reducing the car trips for offline shopping after the establishment of the virtual-shopping-experience stores. The lower-level submodel is a binary Logit model, one which calculates the probability of consumers’ choices between online and offline shopping according to the locations of the experience stores output by the upper-level submodel. A genetic algorithm is adopted to solve the model. To validate the accuracy of the model, as well as that of the algorithm, case studies are carried out based on the real data collected in Dalian and Ningbo (two cities in China). The case study result demonstrates that the establishment of virtual-shopping-experience stores would contribute to reducing the frequency of car trips for offline shopping, as well as the distance of car trips for offline shopping and the time spent in car trips for offline shopping.
Funding Information
  • Fundamental Research Funds for the Provincial Universities of Zhejiang (SJLY2021001, 71704089, LQ17G030003, L21CGL003)
  • National Natural Science Foundation of China (SJLY2021001, 71704089, LQ17G030003, L21CGL003)
  • Natural Science Foundation of Zhejiang Province of China (SJLY2021001, 71704089, LQ17G030003, L21CGL003)
  • Social Science Planning Fund Project of Liaoning Province (SJLY2021001, 71704089, LQ17G030003, L21CGL003)
  • K.C. Wong Magna Fund at Ningbo University (SJLY2021001, 71704089, LQ17G030003, L21CGL003)

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