Abstract
With the rapid development of information technology, the sharing economy has developed rapidly all over the world as a new mode of distributing business profit, among which the bike-sharing system (BSS) has become popular in many cities because of its low cost, convenience, and environmental protection. The application of the 5th generation mobile communication technology (5G) in BSS makes users to search the bikes more accurately and quickly and enables operators to spot noncompliant bike sharing as soon as possible, significantly improving the efficiency of bike-sharing management. However, one of the thorny issues for operators is the bike-sharing rebalancing problem (BRP). It is the key to improve the efficiency of rebalancing, reduce the rebalancing cost, and realize the sustainable development of BSS on how to excavate the huge amount of customer cycling data, respond quickly to customer demand, and use intelligence optimization algorithm to rebalance bikes among stations. However, most of the previous studies dealt with only one period BRP and rarely considered multiperiod issues. At the same time, most researches have focused on minimizing the total cost or time of rebalancing or customer dissatisfaction, but few have aimed at minimizing the rebalancing amount. In addition, the demand gap can reflect the real rental and returning requirements of customers over a certain period of time, which is rarely considered in solving BRP. First of all, this paper presents a multiperiod and multiobjective bike-sharing rebalancing problem (MMBRP). Secondly, a mathematical model is formulated with the objective of minimizing both the total rebalancing cost and amount. In order to solve MMBRP, an improved multiobjective backtracking search genetic algorithm (IMBSGA) is designed. Finally, the effectiveness and competitiveness of IMBSGA in solving MMBRP are verified by numerous experiments comparing with state-of-the-art algorithms.
Highlights
In the modern computer era, with the rapid development of information technology, a large number of new economic and business models have emerged
(i) Based on station classification, the paper selects some stations from nonrebalancing stations and defines them as coordination stations involved in rebalancing, which makes full use of the station resources and greatly reduces the rebalancing amount (ii) Taking into account the demand gap, the initial number of bikes at station is determined to reflect the customer demand in finer time granularity, which effectively extends the rebalance interval and greatly reduces the rebalance (iii) A multiperiod and multiobjective formulation is proposed, which can optimize the total rebalancing cost and the total rebalancing amount at the same time (iv) An improved multiobjective backtracking search genetic algorithm is presented to solve multiperiod and multiobjective bike-sharing rebalancing problem (MMBRP)
Citi Bike is the first bike-sharing program in New York, which was founded in September 2011 after winning a bid of the bike-sharing program organized by the New York Department of Transportation
Summary
In the modern computer era, with the rapid development of information technology, a large number of new economic and business models have emerged. We introduce a multiobjective mathematic model which minimizes the total rebalancing cost and amount, simultaneously To solve this problem, we used an improved multiobjective backtracking search genetic algorithm (IMBSGA), which include an initial number of bike setting method based on periods of demand gap and a coordination station selection and rebalancing amount determining strategy. (i) Based on station classification, the paper selects some stations from nonrebalancing stations and defines them as coordination stations involved in rebalancing, which makes full use of the station resources and greatly reduces the rebalancing amount (ii) Taking into account the demand gap, the initial number of bikes at station is determined to reflect the customer demand in finer time granularity, which effectively extends the rebalance interval and greatly reduces the rebalance (iii) A multiperiod and multiobjective formulation is proposed, which can optimize the total rebalancing cost and the total rebalancing amount at the same time (iv) An improved multiobjective backtracking search genetic algorithm is presented to solve MMBRP.
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