Highlights We incorporate logit price-dependent demand function into the user-based SBRP. We develop an outer approximation method to obtain the global ε -optimal solution. The performance of the developed outer-approximation method is demonstrated. We reveal the effects of parameters toward the relocation strategies. We develop an efficient bi-level VNS algorithm for large instances.

    Abstract A static bike rebalancing problem with optimal user incentives is investigated. The problem is formulated as a mixed-integer nonlinear and nonconvex programming model to minimize the total cost, including the travel costs, unbalanced penalties, and incentive costs. We reformulate the mixed-integer program and develop a new outer-approximation method to obtain its global ε -optimal solutions. We also propose a bi-level variable neighborhood search algorithm to solve large problems. The results tested on small examples reveal problem properties and the performance of the outer-approximation method. The results tested on large examples show that the bi-level algorithm can provide high-quality solutions with short computational times.


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    Title :

    The static bike rebalancing problem with optimal user incentives


    Contributors:
    Li, Yanfeng (author) / Liu, Yang (author)


    Publication date :

    2020-12-22




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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