A new crossover to solve the full truckload vehicle routing problem using genetic algorithm

Bouyahyiouy, Karim El; Bellabdaoui, Adil

doi:10.1109/gol.2016.7731675

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…Numerical results on small and medium-size instances are presented using the CPLEX solver. To solve larger instances, we developed, adapted, and applied some heuristic methods: an ant colony system (ACS) [16], a genetic algorithm (GA) [17][18], and a reactive tabu search (RTS) [19].…”

Section: Related Workmentioning

confidence: 99%

“…Li and Lu [6] Zolfagharinia and Haughton [12] Li et al [13] El Bouyahyiouy and Bellabdaoui [16] Hammami et al [20] Wang et al [9] El Bouyahyiouy and Bellabdaoui [17] Öner and Kuyzu [8] Authors Ball et al [3] Wang and Regan [4] Yang et al [10] Tjokroamidjojo et al [11] Liu et al [7] Miori [5] x The MILP model of the RSFTMDVRPTW is given as follows:…”

Section: Min Worst Tt E Milp-based Lexicographic This Studymentioning

confidence: 99%

“…Constraints ( 15) require, at a commodity level, that the unloading time be greater than the sum of the loading time and the time from the commodity's collection location to its delivery location. Constraints ( 16) and (17) impose that loading a commodity onto a truck can only start after the truck has left its departure depot. Constraints (18) and (19) ensure that a truck can only pick up the next commodity after unloading the previous one, and displacement occurs.…”

Section: Minimize F Tt mentioning

confidence: 99%

“…The experiments are conducted across adapted SFTMDVRPTW instances proposed by EL Bouyahyiouy and Bellabdaoui [17], which are generated based on three classes of Solomon's VRPTW benchmark instances [22]. ' ' means that the points are clustered, ' ' indicates that the points are random, and ' ' denotes that the points are both clustered and random.…”

Section: Milp-based Lexicographic Approach For the Rsftmdvrptwmentioning

confidence: 99%

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An MILP-based Lexicographic Approach for Robust Selective Full Truckload Vehicle Routing Problem

Bouyahyiouy,

Annouch,

Bellabdaoui

2023

IJACSA

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Full truckload (FTL) shipment is one of the largest trucking modes. It is an essential part of the transportation industry, where the carriers are required to move FTL transportation demands (orders) at a minimal cost between pairs of locations using a certain number of trucks available at the depots. The drivers who pick up and deliver these orders must return to their home depots within a given time. In practice, satisfying those orders within a given time frame (e.g., one day) could be impossible while adhering to all operational constraints. As a result, the investigated problem is distinguished by the selective aspect, in which only a subset of transportation demands is serviced. Furthermore, travel times between nodes can be uncertain and vary depending on various possible scenarios. The robustness subsequently consists of identifying a feasible solution in all scenarios. Therefore, this study introduces an MILP-based lexicographic approach to solve a robust selective full truckload vehicle routing problem (RSFTVRP). We demonstrated the proposed method's efficiency through experimental results on newly generated instances for the considered problem.

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Section: Related Workmentioning

confidence: 99%

Section: Min Worst Tt E Milp-based Lexicographic This Studymentioning

confidence: 99%

Section: Minimize F Tt mentioning

confidence: 99%

Section: Milp-based Lexicographic Approach For the Rsftmdvrptwmentioning

confidence: 99%

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An MILP-based Lexicographic Approach for Robust Selective Full Truckload Vehicle Routing Problem

Bouyahyiouy,

Annouch,

Bellabdaoui

2023

IJACSA

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“…The VRP is recognized as a highly intricate combinatorial optimization dilemma that allows scholars to investigate various algorithms [8]. Numerous research efforts have been dedicated to addressing the VRP by introducing a multitude of methodologies, including Mixed-Integer Linear Programs (MILP) implemented through CPLEX [9] [10], Branch and Bound algorithm [11] [12] [13], Tabu Search [14] [15] [16], Simulated Annealing [17], Ant Colony Optimization (ACO) [18] [19] and Genetic Algorithm (GA) [20] [21] [22]. Readers are referred to [23] and [24] to obtain a better idea for solving VRPs using a meta-heuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Maritime Passenger Transfer in Rich Vehicle Routing Problem Using a Hybrid Genetic Algorithm

Iksan,

Tufail,

Anna Apong

et al. 2024

IEEE Access

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Ship routing is a fundamental component of maritime passenger logistics, involving the careful planning of sea routes to facilitate the efficient and comfortable transportation of passengers. However, the ship routing problem presents unique challenges in the transit network design, encompassing various Vehicle Routing Problem (VRP) variants. These challenges arise from complex characteristics including multiple depots, ship types, and asymmetric distances between ports. This study aims to minimize passenger transfers in ship routing by incorporating constraints on maximum distance and travel time.To address these challenges, we propose a two-step approach: a hybrid Genetic Algorithm (GA) that incorporates the Fixed Radius Near Neighbor (FRNN) heuristic method alongside GA optimization. Moreover, a comparison was conducted between our proposed hybrid GA and both the standalone FRNN and conventional GA. The comparative analysis demonstrated that the hybrid GA outperformed both the FRNN and the standard GA in the reduction of passenger transfers and attainment of optimal fitness values. This highlights the significance of incorporating various optimization methodologies to enhance performance. Subsequent studies should investigate the mechanisms underlying the success and suitability of hybrid GA in practical maritime logistics scenarios.

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Optimal unit sizing of PV/wind/battery/sector generating system

Hariz

Aissaoui

Diany

2018

2018 4th International Conference on Optimization and Applications (ICOA)

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A new crossover to solve the full truckload vehicle routing problem using genetic algorithm

Cited by 6 publications

References 28 publications

An MILP-based Lexicographic Approach for Robust Selective Full Truckload Vehicle Routing Problem

An MILP-based Lexicographic Approach for Robust Selective Full Truckload Vehicle Routing Problem

Optimizing Maritime Passenger Transfer in Rich Vehicle Routing Problem Using a Hybrid Genetic Algorithm

Optimal unit sizing of PV/wind/battery/sector generating system

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