A hybrid genetic algorithm to solve a multi-objective Pickup and Delivery Problem

Chami, Zaher Al; Manier, Hervé; Manier, Marié-Ange; Fitouri, Cyrine

doi:10.1016/j.ifacol.2017.08.1906

Cited by 29 publications

(19 citation statements)

References 15 publications

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“…The second comparison is made between HPSO and HG which is proposed in [12]. The comparison is also made between HPSO and real pareto front/approached pareto front.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…The drawback of the proposed method is that only one non dominated solution can be obtained. Therefore, hybrid genetic algorithm (HG) was proposed in [12]. The method can find high quality solutions, but the number of non dominated solutions was also limited.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SPDPTWPD has been little studied. To our best knowledge, only two metaheuristics are proposed in [12] and in [11] to solve the problem. In this paper, a hybrid multi-objective PSO is proposed to solve this variant.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Particle Swarm Optimization for Selective Pickup and Delivery Problem

Peng

Chami

Manier

et al. 2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

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This paper studies a variant of vehicle routing problem called Selective Pickup and Delivery Problem with Time Windows and Paired Demands (SPDPTWPD). A visiting sequence of each assigned vehicle needs to be determined by respecting the imposed constraints. Like for other combinatorial problems, the optimal solution cannot be obtained in a reasonable time when the size increases. An approached method is thus chosen as an alternative to tackle this issue. The proposed method integrates particle swarm optimization (PSO) with local searches by considering the diversification of PSO and intensification of local search. To validate the method, experiments are made on the benchmarks from the literature. The experiments are divided into two parts. In the first part, a self-comparison is made to demonstrate the evolutionary capacity of PSO and the efficiency of proposed local searches. In the second part, the proposed method is compared with a genetic algorithm from the literature. The results show that the method is competitive and efficient.

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“…The second comparison is made between HPSO and HG which is proposed in [12]. The comparison is also made between HPSO and real pareto front/approached pareto front.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Particle Swarm Optimization for Selective Pickup and Delivery Problem

Peng

Chami

Manier

et al. 2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

View full text Add to dashboard Cite

show abstract

“…To address the same variant, researchers have used a hybrid Particle Swarm Optimization Algorithm [9]. Furthermore, two metaheuristic approaches were used to solve the SPDPTWPD: the first one is based on a hybrid genetic algorithm [10] and the second one combines a simulated annealing with a local search procedure [11].…”

Section: A Pickup and Delivery Problemmentioning

confidence: 99%

“…To confirm that each route begins and finishes at the depot, we used constraints (5) and (6). The respect of each vehicle capacity is guaranteed by (7),(8), (9) and (10). Constraints (11) update the starting service time at each node for each scenario while taking into account the associated traveling time.…”

Section: Exact Approach For the Rspdpmentioning

confidence: 99%

A Robust Pickup and Delivery Problem with Uncertain Travel Time

Chami

Bechara

Manier

et al. 2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

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This paper discusses a robust variant of the well known Pickup and Delivery Problem (PDP) which we called the Robust Selective PDP (RSPDP). In this problem, a fleet composed of several vehicles with a given capacity should satisfy a set of transportation requests while respecting various constraints in relation with the precedence between suppliers and customers, the capacity of vehicles, the opening and closing times of each site. Because of all these constraints, the honoring of all requests over a given period (for example, one day) is sometimes impossible so the selective aspect consists in choosing the sites to be served. This robust variant is characterized by the presence of different scenarios used to handle the uncertain traveling time for each arc. Subject to all scenarios and constraints, the goal is to find the best solution (with the higher profit) which validates all scenarios. This study introduces the mathematical formulation for this new variant. Then, an exact and a metaheuristic approaches will be used to solve it. Several tests have been done, on new generated instances for the considered problem, in order to show the efficiency of developed methods.

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