Multiobjective car relocation problem in one-way carsharing system (original) (raw)
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A Multiobjective Memetic Algorithm for Solving the Carsharing Problem
In this article, we present a method combining a genetic approach with a local search for multiobjective problems. It is an extension of algorithms for the single objective case, with specific mechanisms used to build the Pareto set. The performance of the proposed algorithm is illustrated by experimental results based on a real problem with three objectives. The problem is issued from electric carsharing service with a car manufacturer partner. Compared to the Multiobjective Pareto Local Search well known in the scientific literature, the proposed model aims to improve: the solutions quality and the set diversity.
Mathematical Model for the Study of Relocation Strategies in One-way Carsharing Systems
Transportation Research Procedia, 2015
Carsharing is today considered as an ecological and innovative solution to improve urban mobility. The one-way version, where vehicles can be drop-off in any station, brings however some challenging open questions. The system has to be design as part of the global transportation one and vehicle relocation operations must be included to get the higher level of service.
Optimization of a one-way carsharing system with relocation operations
2014
Carsharing systems have gathered increasing attention and are now recognised as innovative and ecological solutions to transportation issues. Especially, one-way carsharing systems offer a high level of service because they exempt users from the obligation to return the vehicle at the same station where it was borrowed. Unfortunately, this flexibility comes with design complexity. This paper addresses a part of those design issues and deals with fleet dimensioning including vehicle relocation operations. We propose mathematical programming oriented approach and we introduce a simple linear model based on integer flow variables. This model is organized around three optimization criteria: maximizing satisfied carsharing demands while minimizing the fleet of vehicles and the relocation operations. From random generated benchmarks, we study the scalability of our method and we show that computation times remain acceptable for representative problem complexities. Index Terms-one-way carsharing, operational research, carsharing demand generation, transport optimization, mathematical modelling.
Insights on Car Relocation Operations in One-Way Carsharing Systems
International Journal of Advanced Computer Science and Applications
One-way carsharing system is a mobility service that offers short-time car rental service for its users in an urban area. This kind of service is attractive since users can pick up a car from a station and return it to any other station unlike round-trip carsharing systems where users have to return the car to the same station of departure. Nevertheless, uneven users' demands for cars and for parking places throughout the day poses a challenge on the carsharing operator to rebalance the cars in stations to satisfy the maximum number of users' requests. We refer to a rebalancing operation by car relocation. These operations increase the cost of operating the carsharing system. As a result, optimizing these operations is crucial in order to reduce the cost of the operator. In this paper, the problem is modeled as an Integer Linear Programming model (ILP). Then we present three different car relocation policies that we implement in a greedy search algorithm. The comparison between the three policies shows that car relocation operations that do not consider future demands do not effectively decrease rejected demands. On the contrary, they can generate more rejected demands. Results prove that solutions provided by our greedy algorithm when using a good policy, are competitive with CPLEX solutions. Furthermore, adding stochastic modification on the input data proves that the results of the two presented approaches are highly affected by the input demand even after adding threshold values constraints.
IEEE Transactions on Intelligent Transportation Systems, 2000
One-way carsharing systems allow travelers to pick up a car at one station and return it to a different station, thereby causing vehicle imbalances across the stations. In this paper, a way to mitigate that imbalance is discussed, which is relocating vehicles between stations. For this purpose, two methods are presented, i.e., a new mathematical model to optimize the relocation operations that maximize the profitability of a carsharing service and a simulation model to study different real-time relocation policies. Both methods were applied to networks of stations in Lisbon, Portugal. Results show that relocating vehicles, using any of the methods developed, can produce significant increases in profit. For instance, in the case where the carsharing system provides maximum coverage of the city area, the imbalances in the network resulted in an operating loss of C1160/day when no relocation operations were performed. When relocation policies were applied, however, the simulation results indicate that profits of C854/day could be achieved, even with increased costs due to relocations. Using the mathematical model, the results are even better, with a reached profit of C3865.7/day. This improvement was achieved through reductions in the number of vehicles needed to satisfy the demand and the number of parking spaces needed at stations. These results demonstrate the importance of relocation operations for profitably providing a network of stations in one-way carsharing systems that covers the entire city, thus reaching a higher number of users.
Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
Applied Sciences
In a round-trip carsharing system, stations must be located in such a way that allow for maximum user coverage with the least walking distance as well as offer certain degrees of flexibility for returning. Therefore, a balance must be stricken between these factors. Providing a satisfactory system can be translated into an optimization problem and belongs to an NP-hard class. In this article, a novel optimization model for the round-trip carsharing fleet placement problem, called Fleet Placement Problem (FPP), is proposed. The optimization in this work is multiobjective and its NP-hard nature is proven. Three different optimization algorithms: PolySCIP (exact method), heuristics, and NSGA-II (metaheuristic) are investigated. This work adopts three real instances for the study, instead of their abstracts where they are most commonly used. They are two instance:, in the city of Luxembourg (smaller and larger) and a much larger instance in the city of Munich. Results from each algorith...
2016
One-way electric vehicle car-sharing systems are receiving increasing attention due to their mobility, environmental, and societal benefits. One of the major issues faced by the operators of these systems is the optimization of the relocation operations of personnel and vehicles. These relocation operations are essential in order to ensure that vehicles are available for use at the right place at the right time. Vehicle availability is a key indicator expressing the level of service offered to customers. However, the relocation operations, that ensure this availability, constitute a major cost component for the provision of these services. In this paper the authors are developing, solving, and applying, in a real world context, an integrated multi-objective mixed integer linear programming (MMILP) optimization and discrete event simulation framework to optimize operational decisions for vehicle and personnel relocation. The authors are using a clustering procedure to cope with the d...
Simulation and optimization of one-way car-sharing systems with variant relocation policies
2015
Car-sharing is a transportation service consisting of vehicles distributed over an urban area that any driver registered to the system can use. This paper focuses on one-way electric car-sharing systems. The success of such systems relies strongly on operations management and attractive rental conditions. Immediate availability and possibility of reservation in advance are key points. This induces strong constraints for the operator especially when some stations attract more trips as a destination than as an origin and vice versa. These imbalances must be corrected by performing vehicle relocations in a smart way to maximize vehicle availability and minimize operator’s costs. In order to understand the demand patterns and explore relocation possibilities, an event-based simulator is built in C#. We develop a new relocation strategy to minimize the demand loss due to vehicle unavailability. Implemented in parallel to rentals, it relies on the regular update of the relocation plans ba...
One-Way Carsharing: Solving the Relocation Problem
, Kek et al. (10), Kek et al. (11), and Fan et al. (12), have considered the management of carsharing. The traditional carsharing system is used only for round-trips (i.e., a journey to a destination and return), which limits the number of potential users. One-way carsharing does not require users to return their vehicles to their point of origin in the system. Operators offer different degrees of flexibility to their customers. In some cases, users return the vehicles to a specified station, while in others they can leave the car whenever and wherever they want. One-way carsharing does create the need, however, to redistribute vehicles to the right stations to meet user demand. Otherwise, there would either be a concentration or a shortage of vehicles at popular destinations and origins, and the system would not be able to fully satisfy the demand, which would lead to a reduction in customers. In previous studies on flexible destinations in carsharing, two main approaches to relocation were taken: user-based and operator-based relocation algorithms. In these systems, the number of vehicles in each station should be periodically checked according to the lower and upper thresholds to identify the need to move vehicles among stations. In operator-based relocation techniques, staff members of the carsharing company relocate the vehicles, whereas, in user-based relocation methods, this task shifts to the customers. Thus far, several relocation techniques have been proposed in the literature. In a static relocation approach, Barth and Todd relocated vehicles on the basis of immediate needs at a particular station. A minimum threshold was used before a relocation event was generated for a particular station, and the station with an excess of vehicles maintained a minimum threshold before it could give up vehicles in a relocation event (9). Later, Barth and Todd studied the relocation prediction with a supposition of knowledge of user destinations (13). In a following study, Barth et al. proposed two user-based relocation methods, which successfully reduced the required number of relocations. These methods were trip splitting (i.e., users drive separate vehicles when they travel from a station with an oversupply of vehicles to one with a shortage), and trip joining (i.e., users share a ride in a single vehicle when they want to travel from a station with a shortage of vehicles to one with an oversupply) (14). In an operator-based study, Kek et al. presented a methodology for a flexible time and destination system. Two relocation techniques were used, namely, shortest time (i.e., vehicles were moved in the shortest possible time), and inventory balancing (i.e., a station with a shortage of vehicles was filled with vehicles from a station with an oversupply) (10). In another study, an algorithmic approach was proposed in which the algorithm returned a simple probabilistic policy to distribute cars from each node according to a fixed probability distribution. An attempt was made to try and find the right balance between relocation of a car and the wait for a customer to appear and take care of it instead. An operation research model was developed to maximize the carsharing scheme's long-term average revenue (15). In user-based Carsharing services allow users to benefit from the advantages of a private car without the costs of owning one. One-way systems provide users with a higher level of service than traditional carsharing systems in terms of flexibility because users do not need to return to the station of origin. Moreover, the added option to leave the vehicle at any free parking area, which is not necessarily a station, increases the flexibility offered by the one-way system. Introduction of such improvements to the carsharing system, however, leads to a vehicle relocation problem, which should be addressed carefully to avoid concentration of vehicles in certain areas. This paper reports on a study of this issue with the use of discrete event systems (DESs), which allowed an easy representation of the complex dynamics of the carsharing system. A user-based methodology was proposed on the basis of an optimal relocation policy in a rolling horizon framework. This methodology not only offers greater flexibility to users, it also maximizes operator benefits by reducing the number of required staff to relocate vehicles among the stations and determines the minimum number of vehicles needed to satisfy system demand. The DES model was applied to a case study to evaluate the proposed approach. The results showed a significant decrease in the rejection rate from the worst scenario (no relocation) to the best (relocation of all vehicles by their users). The paper concludes with suggestions for additional research and improvements to this study.