Given the growing importance of bike-sharing systems nowadays, in this paper we suggest an alternative approach to mitigate the most crucial problem related to them: the imbalance of bicycles between zones owing to one-way trips. In particular, we focus on the emerging free-floating systems, where bikes can be delivered or picked-up almost everywhere in the network and not just at dedicated docking stations. We propose a new comprehensive dynamic bike redistribution methodology that starts from the prediction of the number and position of bikes over a system operating area and ends with a relocation Decision Support System. The relocation process is activated at constant gap times in order to carry out dynamic bike redistribution, mainly aimed at achieving a high degree of user satisfaction and keeping the vehicle repositioning costs as low as possible. An application to a test case study, together with a detailed sensitivity analysis, shows the effectiveness of the suggested novel methodology for the real-time management of the free-floating bike-sharing systems.

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A modeling framework for the dynamic management of free-floating bike-sharing systems

Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis.

A branch-and-bound algorithm for solving the static rebalancing problem in bicycle-sharing systems

In this paper, a bike repositioning problem with multiple depots, multiple visits, and multiple heterogeneous vehicles for the free-floating bike-sharing system (FFBSS) is studied. Two types of nodes (i.e., easily and hardly access nodes) with different penalties are defined to represent different convenience levels of getting bikes from the FFBSS. The objective of the repositioning is to minimize the weighted sum of the inconvenience level of getting bikes from the system and the total unmet demand and the total operational time. To solve this problem, an enhanced version of chemical reaction optimization (CRO) is developed. A loading and unloading quantity adjustment procedure with the consideration of the node characteristics, including the type of node and its current state (i.e., in a balanced, surplus, or deficit state) is proposed and incorporated into this version to improve its solution quality. A concept of the nearby-node set is also proposed to narrow the search space. Numerical results are presented and indicate that compared to the traditional CRO and CPLEX, the enhanced CRO improves solution quality and has potential to tackle the repositioning problem for larger, longer repositioning duration, and more vehicle instances. The results also demonstrate the effectiveness of the proposed adjustment procedure.

/pdf/a-static-free-floating-bike-repositioning-problem-with-hpc2f2ydxo.pdf

A static free-floating bike repositioning problem with multiple heterogeneous vehicles, multiple depots, and multiple visits

This study investigates a bike repositioning problem (BRP) that determines the routes of the repositioning vehicles and the loading and unloading quantities at each bike station to firstly minimize the positive deviation from the tolerance of total demand dissatisfaction (TDD) and then service time. The total demand dissatisfaction of a bike-sharing system in this study is defined as the sum of the difference between the bike deficiency and unloading quantity of each station in the system. Two service times are considered: the total service time and the maximum route duration of the fleet. To reduce the computation time to solve the loading and unloading sub-problem of the BRP, this study examines a novel set of loading and unloading strategies and further proves them to be optimal for a given route. This set of strategies is then embedded into an enhanced artificial bee colony algorithm to solve the BRP. The numerical results demonstrate that a larger fleet size may not lead to a lower total service time but can effectively lead to a lower maximum route duration at optimality. The results also illustrate the trade-offs between the two service times, between total demand dissatisfaction and total service time, and between the number of operating vehicles provided and the TDD. Moreover, the results demonstrate that the optimal values of the two service times can increase with the TDD and introducing an upper bound on one service time can reduce the optimal value of the other service time.

/pdf/exact-loading-and-unloading-strategies-for-the-static-multi-3s6js0pgx8.pdf

Exact loading and unloading strategies for the static multi-vehicle bike repositioning problem

https://hal.archives-ouvertes.fr/hal-02868931/document

A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

Bicycle sharing systems represent a new urban mode of transportation in developed countries. The exploitation of such systems implies numerous operational challenges and one of them is to ensure users that they will be able to find a bicycle or to leave it at each station. Therefore, the rebalancing system is necessary for maintaining a prefixed number of bicycles at each station in order to improve bicycle utilisation. The main contribution of this paper is the development of an original approach based on the integration of a stochastic Petri net model and a genetic algorithm for performance optimisation of such complex systems. As an application, the proposed method is applied to Cristolib, a real self-service bicycle system of Creteil city, France. [Received 5 November 2013; Revised 20 May 2014; Accepted 12 July 2014]

An integrated Petri net and GA-based approach for performance optimisation of bicycle sharing systems

Public bicycle-sharing systems have been implemented in many big cities around the world to face many public transport problems. The exploitation and the management of such transportation systems imply crucial operational challenges. The balancing of stations is the most crucial question for their operational efficiency and economic viability. In this paper, we study the balancing problem of stations with multiple vehicles by considering the static case. A mathematical formulation of the problem is proposed, and two lower bounds based on Eastman’s bound and SPT rule are developed. Moreover, we proposed four upper bounds based on a genetic algorithm, a greedy search algorithm and two hybrid methods that integrate a genetic algorithm, a local search method and a branch-and-bound algorithm. The developed lower and upper bounds are tested and compared on a large set of instances.

Ahmed Abdelmoumene Kadri

Papers

A branch-and-bound algorithm for solving the static rebalancing problem in bicycle-sharing systems

A multi-stage stochastic integer programming approach for locating electric vehicle charging stations

An integrated Petri net and GA-based approach for performance optimisation of bicycle sharing systems

Lower and upper bounds for scheduling multiple balancing vehicles in bicycle-sharing systems

A memetic algorithm for solving the multiple vehicles routing problem in bicycle sharing systems