Abstract
Bike-sharing systems, which are used in many cities worldwide, need to maintain a balance between the availability of bicycles and the availability of unoccupied bicycle slots. This paper presents an investigation of the net flow of each bike-sharing station in Jersey City. The data was recorded at 1-minute intervals. The sum of the initial bicycle number and the minimum net flow value was determined to be the demand for static rebalancing, and this led to the proposal of a bike-sharing demand prediction method based on autoregressive integrated moving average models. Considering that the existence of bicycles in a state of disrepair may adversely affect demand prediction and routine planning, we present an integer linear programming formulation to model bike-sharing static rebalancing. The proposed formulation takes into account the problem introduced by the need to collect bicycles in need of repair. A hybrid Discrete Particle Swarm Optimization (DPSO) algorithm was proposed to solve the model, which incorporates a reduced variable neighborhood search (RVNS) functionality together with DPSO to improve the global optimization performance. The effectiveness of the algorithms was verified by a detailed numerical example.
Highlights
Bike-sharing systems are designed to solve “the first and the last mile problem” and to provide a connection between multitransit modes
Discrete Particle Swarm Optimization (DPSO) is applicable to solve this kind of problem
In order to test the performance of the hybridization algorithm of DPSO with Variable Neighborhood Search (VNS), Goksal et al made an overall comparison with Reactive Tabu Search (RTS), Large Neighborhood Search (LNS), Particle Swarm Optimization (PSO), Ant Colony System (ACS), Parallel Iterative Local Search (PILS), and Adaptive Memory Methodology (AMM)
Summary
Bike-sharing systems are designed to solve “the first and the last mile problem” and to provide a connection between multitransit modes. Contrary to the traditional vehicle routing problem with simultaneous pickup and delivery (VRPSPD), the number of bicycles in need of repair collected from some stations cannot be used to respond to requests of delivery stations. (2) To the best of our knowledge, our work is the first to formulate the bike-sharing static rebalancing problem by considering the need to collect bicycles requiring repair. The existence of these bicycles increases the complexity of the static rebalancing problem. We build new integer linear programming formulations for the bike-sharing static rebalancing problem by considering the need to collect bicycles requiring repair.
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