On-line dynamic station redeployments in bike-sharing systems

Bike-sharing has seen great development during recent years, both in Europe and globally. However, these systems are far from perfect. The uncertainty of the customer demand often leads to an unbalanced distribution of bicycles over the time and space (congestion and/or starvation), resulting both in a loss of customers and a poor customer experience. In order to improve those aspects, we propose a dynamic bike-sharing system, which combines the standard fixed base stations with movable stations (using trucks), which will able to be dynamically re-allocated according to the upcoming forecasted customer demand during the day in real-time. The purpose of this paper is to investigate whether using moveable stations in designing the bike-sharing system has a significant positive effect on the system performance. To that end, we contribute an on-line stochastic optimization formulation to address the redeployment of the moveable stations during the day, to better match the upcoming customer demand. Finally, we demonstrate the utility of our approach with numerical experiments using data provided by bike-sharing companies

A metaheuristic approach for the repositioning problem in bike sharing systems (bss): a study case in Toluca, México

The impact of Bike Sharing Systems (BSS) in the world had experienced such success that nowadays most iconic cities in the world have adopted its own system. The particular characteristics of the user’s mobility in every city have not allowed developing a generalized procedure to operate the systems. Moreover, the lack of symmetry in the mobility patterns, and the dynamic users’ behavior lead to eventually “unbalance” the system, this is, to a lack of bikes at stations, and therefore bikes have to be repositioned to stations where effective demand is present, and there is no unified or scientifically supported methodology. In this paper we deal with a study case in Toluca city (Huizi system), in which the entity in charge of current operational activities wants to design a procedure scientifically based to perform repositioning daily activities at the minimum operational cost guarantying the availability of bikes for the users (service level). Due to operational requirements, this bi-objective problem was formulated using a dynamic scope and stated as a combinatorial optimization model and finally solved using a multi-objective evolutionary algorithm

A* search algorithm for an optimal investment problem in vehicle-sharing systems

We study an optimal investment problem that arises in the context of the vehicle-sharing system. Given a set of locations to build stations, we need to determine i) the sequence of stations to be built and the number of vehicles to acquire in order to obtain the target state where all stations are built, and ii) the number of vehicles to acquire and their allocation in order to maximize the total profit returned by operating the system when some or all stations are open. The profitability associated with operating open stations, measured over a specific time period, is represented as a linear optimization problem applied to a collection of open stations. With operating capital, the owner of the system can open new stations. This property introduces a set-dependent aspect to the duration required for opening a new station, and the optimal investment problem can be viewed as a variant of the Traveling Salesman Problem (TSP) with set-dependent cost. We propose an A* search algorithm to address this particular variant of the TSP. Computational experiments highlight the benefits of the proposed algorithm in comparison to the widely recognized Dijkstra algorithm and propose future research to explore new possibilities and applications for both exact and approximate A* algorithms.Comment: Full version of the conference paper which is accepted to be appear in the proceeding of the The 12th International Conference on Computational Data and Social Networks - SCONET202

An integrated optimization-simulation framework for vehicle and personnel relocations of electric car-sharing systems

Car-sharing is a new concept which enables users to rent cars for short period of times. Car-sharing systems can be classified as one-way or round-trip according to restrictions imposed for pick-up and drop-off locations. In more restricted round-trip systems, users have to return vehicles where they have picked them up, whereas one-way systems allow users to return vehicles to different drop-off stations. In addition, one-way systems have two types. If the car-sharing system operates with designated parking locations it is characterized as non-floating. If the users are allowed to drop-off vehicles with defined borders then the system is called free-floating. In this research we are dealing with operational decisions of one-way non-floating electric car sharing systems with reservations and dynamic relocations, i.e. relocation can be done anytime during the operations. Our previous work in this area is addressing issues related to strategic and operational problem , and simulation of relocation operations. In this paper we are introducing the following new modelling concepts: (i) station clustering and (ii) integration of optimization with simulation for the operational problem

A profitability comparison between a “one-way” car sharing service and a "modified one-way" car sharing service

The research looks at comparing the different performance offered by two types of one-way car sharing services. In particular, we compare the “traditional” service in which users can return vehicles to a pre-determined permitted parking space to the “modified” service in which the decision of where to return the vehicle to is made at the end of its usage and vehicles can be returned also outside the permitted parking areas. The comparison is based on common and given demand/offer assumptions. The mathematical modelling uses state of the art algorithms that allow us to determine for both types of service the optimal number of personnel to re-position the vehicles in order to maximise profit. In particular, the attractiveness of the two services herewith compared, has been analysed both in terms of overall profitability as well as in terms of maximum number of users. The results show and quantify how the “modified” service, whilst allowing a greater degree of flexibility to users in terms of return locations, causes lower economic returns for the service company and lowers the number of users that can be served. Finally, the model allows us to calculate the required tariff increase necessary to transform a “traditional” service into a “modified” service assuming an inelastic demand curve as well as constant profits for the service company

Optimizing Strategic Allocation of Vehicles for One-Way Car-sharing Systems Under Demand Uncertainty

Car-sharing offers an environmentally sustainable, socially responsible and economically feasible mobility form in which a fleet of shared-use vehicles in a number of locations can be accessed and used by many people on as-needed basis at an hourly or mileage rate. To ensure its sustainability, car-sharing operators must be able to effectively manage dynamic and uncertain demands, and make the best decisions on strategic vehicle allocation and operational vehicle reallocation both in time and space to improve their profits while keeping costs under control. This paper develops a stochastic optimization method to optimize strategic allocation of vehicles for one-way car-sharing systems under demand uncertainty. A multi-stage stochastic linear programming model is developed and solved for use in the context of car-sharing. A seven-stage experimental network study is conducted. Numerical results and computational insights are discussed

Towards smart open dynamic fleets

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-33509-4_32Nowadays, vehicles of modern fleets are endowed with advanced devices that allow the operators of a control center to have global knowledge about fleet status, including existing incidents. Fleet management systems support real-time decision making at the control center so as to maximize fleet perform‐ ance. In this paper, setting out from our experience in dynamic coordination of fleet management systems, we focus on fleets that are open, dynamic and highly autonomous. Furthermore, we propose how to cope with the scalability problem as the number of vehicles grows. We present our proposed architecture for open fleet management systems and use the case of taxi services as example of our proposal.Work partially supported by Spanish Government through the projects iHAS (grant TIN2012-36586-C03) and SURF (grant TIN2015-65515-C4-X-R), the Autonomous Region of Madrid through grant S2013/ICE-3019 (“MOSI-AGIL-CM”, cofunded by EU Structural Funds FSE and FEDER) and URJC-Santander (30VCPIGI15).Billhardt, H.; Fernández, A.; Lujak, M.; Ossowski, S.; Julian Inglada, VJ.; Paz, JFD.; Hernández, JZ. (2016). Towards smart open dynamic fleets. En Multi-Agent Systems and Agreement Technologies. Springer. 410-424. https://doi.org/10.1007/978-3-319-33509-4_32S41042