Highlights We incorporate logit price-dependent demand function into the user-based SBRP. We develop an outer approximation method to obtain the global -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 -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.
The static bike rebalancing problem with optimal user incentives
2020-12-22
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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