Setting Inventory Levels in a Bike Sharing Network

Bike sharing systems (BSSs) allow customers to rent bicycles at automatic rental stations distributed throughout a city, use them for a short period of time, and return them to any station. One of the major issues that BSS operators must address is nonhomogeneous asymmetric demand processes. These demand processes create an inherent imbalance, thus leading to shortages either of bicycles when users are attempting to rent them and of vacant lockers when users are attempting to return them. The predominant approach taken by operators to cope with this difficulty is to reposition bicycles to rebalance the inventory levels at the different stations. Most repositioning studies assume that a target inventory level or range of inventory levels is known for each station. In this paper, the authors focus on determining the correct target level for repositioning according to a well-defined objective. This is a challenging task because of the nature of the user behavior that creates the interactions among the inventory levels at different stations. For example, if bicycles are not available at the user’s origin, the user may abandon the system, use other means of transportation, or look for available bicycles at a neighboring station. If, in another case, a locker is not available at a user’s destination, then that user is obliged to find a station with available space to return the bicycle to the system. Thus, an empty/full station can create a spillover of demand to nearby stations. In addition, stations are related by origin–destination pairing. In this paper, the authors take this effect into consideration for the first time when setting target inventory levels and develop a robust guided local search algorithm for that purpose. The authors show that neglecting the interactions among stations leads to inferior decision making.

Language

  • English

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Filing Info

  • Accession Number: 01695257
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Feb 13 2019 10:44AM