A Hybrid Evolutionary Algorithm for Smart Freight Delivery with Electric Modular Vehicles

Rezgui, Dhekra; Bouziri, Hend; Siala, Jouhaina Chaouachi

doi:10.1109/aiccsa.2018.8612861

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…The equations of the model are further implemented and tested on real instances in the experimental section. Indeed, although metaheuristics [43][44][45][46] and especially hybrid ones [47,48] are effective and fast for solving complex problems such as scheduling and transportation ones, we want here to have a comparative experimentation with an exact resolution [49,50] to find the optimal solution of each of the customer-oriented DARP instances foreseen. This exact resolution will be compared to the new evolutionary method which is proposed to solve the problem.…”

Section: Mathematical Formulation Of the Customer-oriented Darpmentioning

confidence: 99%

An Evolutionary Descent Algorithm for Customer-Oriented Mobility-On-Demand Problems

Nasri

Bouziri

2022

Sustainability

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This paper is addressing a new class of on-demand transport problems oriented toward customers. A mixed-integer linear programming model is proposed with new effective constraints that contribute to enhancing the quality of service. An exact resolution has been achieved, leading to lower bounds of the solution space of real cases of on-demand transport problems. To overcome the exponential computational time of the exact resolution, an evolutionary descent method is developed. It relies on a new operator for perturbing the search. The comparative results between the new method and the branch and bound show low gaps for almost all the instances tested with lower execution times. The results of the evolutionary descent method are also compared with the results of two different heuristics, namely a Tabu Search and an Evolutionary Local Search. Our evolutionary method demonstrates its effectiveness through competitive and promising results.

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Section: Mathematical Formulation Of the Customer-oriented Darpmentioning

confidence: 99%

An Evolutionary Descent Algorithm for Customer-Oriented Mobility-On-Demand Problems

Nasri

Bouziri

2022

Sustainability

Self Cite

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“…In a real routing application, a vehicle often chooses the route from all closest customers. So, the routing system can make a search from all available neighboring routes, calculate their distances, then select the customer with the shortest route as the service priority [5,[22][23][24].…”

Section: Elitist Genetic Algorithm With Improved Neighbor Routing Ini...mentioning

confidence: 99%

“…In recent years, neighborhood algorithms have been proposed and broadly used to improve the convergence of EVRP [5,[22][23][24]. In this method, the routing system searches the route from the neighbor nodes rather than random nodes, which makes the route searching converge fast and easily into the optimum [22,24]. Inspired by the idea, the paper adopts the neighbor routing (NR) idea to improve the convergence of GA.…”

Section: Introductionmentioning

confidence: 99%

The NR-EGA for the EVRP Problem with the Electric Energy Consumption Model

Zhu

Lee

2022

Energies

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Nowadays, in researches on electric vehicle routing problems, in order to improve the delivery efficiency and reduce the routing cost, many important elements are broad discussed such as the customer time window, the routing algorithm, the electric energy consumption, etc. In these, the routing algorithm is the key element to achieve a good solution. Based on this background, the paper investigates the routing algorithm, then adopts the elitist genetic algorithm and proposes an improved neighbor routing initialization method for solving the electric vehicle routing problem. In our method, the electric vehicle energy consumption is used as the main component of the routing system. The neighbor routing initialization enables the routing system to choose the close route from a suitable first customer in the initialization, which makes the routing search faster and find the global optimal route easily. The simulations on the Solomon benchmark data and the Hiland Dairy milk delivery example in Dallas, Texas, USA verifies the good performance of the method.

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“…Since every EV travels to related neighboring customers, the neighbor routing schedule can be used in GA initialization. Rezgui et al [16] call this method a greedy algorithm as it chooses the nearest customer that verifies all the constraints in the routing problem. In the neighbor routing schedule, the first customer close to the depot is the key leader for the initial routing.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Elitist Genetic Algorithm With Improved Neighbor Routing Initialization for Electric Vehicle Routing Problems

2021

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This paper applies the elitist genetic algorithm to the electric vehicle routing problem with time window. In initialization, the paper proposes an improved neighbor routing initialization method for adaptive elitist genetic algorithm. The improved neighbor routing method is used to select the nearest EV customer as the next route to be scheduled and make the route start from the suitable first customer in the initialization of the elitist GA. It makes the scheduled route begins with a neighboring directionality, which can be inherited in selection, crossover, and mutation operations. For effective convergence, new adaptive crossover probability and mutation probability are provided to make the algorithm converge faster. Experimental studies on randomly distributed customers and Solomon benchmark cases show the effective performance of the algorithm. The algorithm is demonstrated in the simulation of a U. S. Postal Service system.

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A Hybrid Evolutionary Algorithm for Smart Freight Delivery with Electric Modular Vehicles

Cited by 10 publications

References 19 publications

An Evolutionary Descent Algorithm for Customer-Oriented Mobility-On-Demand Problems

An Evolutionary Descent Algorithm for Customer-Oriented Mobility-On-Demand Problems

The NR-EGA for the EVRP Problem with the Electric Energy Consumption Model

Adaptive Elitist Genetic Algorithm With Improved Neighbor Routing Initialization for Electric Vehicle Routing Problems

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