Rabih Zakaria | UTBM - Academia.edu (original) (raw)
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Universidade Federal de Santa Catarina - UFSC (Federal University of Santa Catarina)
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Papers by Rabih Zakaria
Operations Research Proceedings, 2016
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... more 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.
Journal of Modern Transportation
In this paper, we present a multiobjective approach for solving the one-way car relocation proble... more In this paper, we present a multiobjective approach for solving the one-way car relocation problem. We fix three objectives that include the number of remaining rejected demands, the number of jockeys used for the relocation operations, and the total time used by these jockeys. For this sake, we propose to apply two algorithms namely NSGA-II and an adapted memetic algorithm (MA) that we call MARPOCS which stands for memetic algorithm for the one-way carsharing system. The NSGA-II is used as a reference to compare the performance of MARPOCS. The comparison of the approximation sets obtained by both algorithms shows that the hybrid algorithm outperforms the classical NSGA-II and so solutions generated by the MARPOCS are much better than the solutions generated by NSGA-II. This observation is proved by the comparison of different quality indicators' values that are used to compare the performance of each algorithm. Results show that the MARPOCS is promising to generate very good solutions for the multiobjective car relocation problem in one-way carsharing system. It shows a good performance in exploring the search space and in finding solution with very good fitness values.
2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS), 2014
ABSTRACT In this paper, we present two approaches to solve the relocation problem in one-way cars... more ABSTRACT In this paper, we present two approaches to solve the relocation problem in one-way carsharing system. We start by formulating the problem as an Integer Linear Programming Model. Then using mobility data collected from an operational carsharing system, we built demands matrices that will be used as input data for our solver. We notice that the time needed to solve the ILP using an exact solver increases dramatically when we increase the number of employees involved in the relocation process and when the system gets bigger. To cope with this problem, we develop a greedy algorithm in order to solve the relocation problem in a faster time. Our algorithm takes one second to solve the relocation problem in worst cases; also, we evaluated the robustness of the two approaches with stochastic input data using different numbers of employees.
Operations Research Proceedings, 2016
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... more 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.
Journal of Modern Transportation
In this paper, we present a multiobjective approach for solving the one-way car relocation proble... more In this paper, we present a multiobjective approach for solving the one-way car relocation problem. We fix three objectives that include the number of remaining rejected demands, the number of jockeys used for the relocation operations, and the total time used by these jockeys. For this sake, we propose to apply two algorithms namely NSGA-II and an adapted memetic algorithm (MA) that we call MARPOCS which stands for memetic algorithm for the one-way carsharing system. The NSGA-II is used as a reference to compare the performance of MARPOCS. The comparison of the approximation sets obtained by both algorithms shows that the hybrid algorithm outperforms the classical NSGA-II and so solutions generated by the MARPOCS are much better than the solutions generated by NSGA-II. This observation is proved by the comparison of different quality indicators' values that are used to compare the performance of each algorithm. Results show that the MARPOCS is promising to generate very good solutions for the multiobjective car relocation problem in one-way carsharing system. It shows a good performance in exploring the search space and in finding solution with very good fitness values.
2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS), 2014
ABSTRACT In this paper, we present two approaches to solve the relocation problem in one-way cars... more ABSTRACT In this paper, we present two approaches to solve the relocation problem in one-way carsharing system. We start by formulating the problem as an Integer Linear Programming Model. Then using mobility data collected from an operational carsharing system, we built demands matrices that will be used as input data for our solver. We notice that the time needed to solve the ILP using an exact solver increases dramatically when we increase the number of employees involved in the relocation process and when the system gets bigger. To cope with this problem, we develop a greedy algorithm in order to solve the relocation problem in a faster time. Our algorithm takes one second to solve the relocation problem in worst cases; also, we evaluated the robustness of the two approaches with stochastic input data using different numbers of employees.