Study On Rebalancing Problem Of Urban Shared Bicycle

In recent years,bike sharing systems(BSSs)have been developing rapidly in many cities to encourage people cycling and using this environmentally friendly transport mode to make their trips.However,there are some big challenges in operating a BSS and one of them is that people are often unable to find a bike or to park it at some stations.However,reallocating the bikes in a BSS is the most primary and commonly used way to solve these challenges.In a BSS,the numbers of bikes required at some stations are often insufficient to satisfy the corresponding demand.Meanwhile,broken bikes are regularly found in a BSS.In order to balance a BSS,the operator needs to transfer bikes from bike surplus stations to bike deficient stations to satisfy cycling demand and transport broken bikes from stations to designated locations.This relocation problem is called a bike rebalancing problem.In order to incentivize users to participate in bike rebalancing activities,we introduce a user participation mechanism and propose a bike rebalancing problem with "lucky bike"strategy and a bike recycling problem with "collection reward" strategy.First,we propose a "lucky bike" strategy to attract users to relocate the excess bikes from bike surplus stations to bike deficient stations.In bike surplus station,some excess bikes are set as lucky bikes which offer a relocation task and a corresponding incentive reward for users.Users can select any lucky bike and then obtain the corresponding incentive reward after relocating the selected lucky bike to its destination.Meanwhile,the rest of the excess bikes that do not offer any incentive reward are common bikes and need to be relocated by a fleet of rebalancing trucks.The proposed problem is formulated as a mixed-integer linear programming and a combined hybrid genetic algorithm is proposed to solve this problem.The numerical experiment results imply that,when a station is moderately imbalanced,the rebalancing strategy aims to offer users to relocate lucky bikes to replace some(even all)truck activities.Numerical results also indicate when the unit incentive reward decreases,more excess bikes in bike surplus stations are set as lucky bikes and the imbalance in the network decreases.Second,we proposed a "collection reward" strategy to attract users to relocate the broken bikes to the collection stations.In this strategy,some stations are set as collection stations.A user can obtain an incentive reward after relocating a broken bike to a collection station Meanwhile,a fleet of trucks are deployed to transport broken bikes from these collection stations to the maintenance station.The proposed problem is formulated as a mixed-integer linear programming and a combined hybrid genetic algorithm is proposed to solve the problem The numerical experiment results imply that the number of broken bikes can result in an increase in the total operation cost.Therefore,the backlog of broken bikes should be prevented The numerical results also show that an increase in the available capacity can lead to a decrease in the total operation cost.In order to decrease the total cost,more collection tasks should be released when the available capacity of the collection stations is largerIt is known that there are types of bikes in a BSS,which may have an important effect on rebalancing strategies.In this paper,we extend the shared bikes from a single type to multiple types.Note that,this extension is an increase in the diversity of shared bikes,such as the balance between different types of bikes and substitution strategy.Considering the characteristics of multi-type bikes,we propose a truck-based bike rebalancing problem with broken bikes and a multiple type bike rebalancing problemFirst,in order to effectively utilize the truck capacity and reduce the total operation cost,we consider to collection broken bikes in the process of relocating usable bikes.The proposed problem is formulated as a mixed-integer linear programming and a combined hybrid tabu search is proposed to solve the problem.The numerical results show that,for different stations,an increase in the collection penalty may result in an improvement of collection priority.For a station with large allocation and collection demands,an increase in the collection penalty may lead to an increase in the collection number of broken bikesSecond,considering that users may rent different types of bikes,we propose a substitution strategy of different types of bikes in bike rebalancing problem.The proposed problem is formulated as a mixed-integer linear programming and a combined hybrid tabu search is proposed to solve the problem.The numerical experiment results show when there is a large deviation between the inventory and the expected demand of a type of bike in a station,an increase in the penalty of this type of bike is an effective way to balance the type of bike in the staiton.The experiment results also show that the substitution strategy is an effective way to reduce the total cost.