An Evolutionary Variable Neighborhood Descent for Addressing an Electric VRP Variant

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

doi:10.1007/978-3-030-15843-9_17

Cited by 6 publications

(3 citation statements)

References 24 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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“…This work adopts the following sequence of neighborhood structures: the swap, the insert, the 2-opt*, and the 2-opt operators. We use approximately the same order as the literature for VRP problems [19,20]. The results of our previous work [18] show that with well-chosen operators, the GVNS algorithm is an effective method to solve the single objective version of our problem.…”

Section: Local Search Procedures and Neighborhood Structuresmentioning

confidence: 99%

“…For each of these six classes, we consider the horizon H = 100 h with a latest arrival time at the depot b n+1 = 200 h and H = 200 h with b n+1 = 400 h. The opening time is a 0 = 0. The maintenance parameters were generated as described in [4]: T pm i ∼ U c [5,10], Tcm i ∼ U c [15,30], [10,20], and σ ∼ t 0i * U c [2,5]. Only the parameter β ∼ U c [2,6] is generated differently compared to the one described in [4] so that it can be closer to reality.…”

Section: Instances Descriptionmentioning

confidence: 99%

Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem

et al. 2022

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This paper addresses a problem faced by maintenance service providers: performing maintenance activities at the right time on geographically distributed machines subjected to random failures. This problem requires determining for each technician the sequence of maintenance operations to perform to minimize the total expected costs while ensuring a high level of machine availability. To date, research in this area has dealt with routing and maintenance schedules separately. This study aims to determine the optimal maintenance and routing plan simultaneously. A new bi-objective mathematical model that integrates both routing and maintenance considerations is proposed for time-based preventive maintenance. The first objective is to minimize the travel cost related to technicians’ routing. The second objective can either minimize the total preventive and corrective maintenance cost or the failure cost. New general variable neighborhood search (GVNS) and variable neighborhood descent (VND) algorithms based on the Pareto dominance concept are proposed and performed over newly generated instances. The efficiency of our approach is demonstrated through several experiments. Compared to the commercial solver and existing multi-objective VND and GVNS, these new algorithms obtain highly competitive results on both mono-objective and bi-objective variants.

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A Bi-objective Evolutionary Algorithm to Improve the Service Quality for On-Demand Mobility

Nasri

Bouziri

2022

Lecture Notes on Data Engineering and Communications Technologies

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An Evolutionary Variable Neighborhood Descent for Addressing an Electric VRP Variant

Cited by 6 publications

References 24 publications

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

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

Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem

A Bi-objective Evolutionary Algorithm to Improve the Service Quality for On-Demand Mobility

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