Combining variable neighborhood search and machine learning to solve the vehicle routing problem with crowd-shipping

2022 ◽  
Author(s):  
Luigi Di Puglia Pugliese ◽  
Daniele Ferone ◽  
Paola Festa ◽  
Francesca Guerriero ◽  
Giusy Macrina
Keyword(s):  
Machine Learning ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Routing Problem

scholarly journals Two metaheuristic approaches for solving the multi-compartment vehicle routing problem

2018 ◽  
Vol 20 (4) ◽  
pp. 2085-2108 ◽  
Author(s):  
Hiba Yahyaoui ◽  
Islem Kaabachi ◽  
Saoussen Krichen ◽  
Abdulkader Dekdouk
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Large Problem ◽  
Solution Quality ◽  
Routing Problem ◽  
Petrol Station ◽  
Capacity Limitations ◽  
Problem Instances

Abstract We address in this paper a multi-compartment vehicle routing problem (MCVRP) that aims to plan the delivery of different products to a set of geographically dispatched customers. The MCVRP is encountered in many industries, our research has been motivated by petrol station replenishment problem. The main objective of the delivery process is to minimize the total driving distance by the used trucks. The problem configuration is described through a prefixed set of trucks with several compartments and a set of customers with demands and prefixed delivery. Given such inputs, the minimization of the total traveled distance is subject to assignment and routing constraints that express the capacity limitations of each truck’s compartment in terms of the pathways’ restrictions. For the NP-hardness of the problem, we propose in this paper two algorithms mainly for large problem instances: an adaptive variable neighborhood search (AVNS) and a Partially Matched Crossover PMX-based Genetic Algorithm to solve this problem with the goal of ensuring a better solution quality. We compare the ability of the proposed AVNS with the exact solution using CPLEX and a set of benchmark problem instances is used to analyze the performance of the both proposed meta-heuristics.

scholarly journals ADAPTATION OF THE VARIABLE NEIGHBORHOOD SEARCH HEURISTIC TO SOLVE THE VEHICLE ROUTING PROBLEM

2012 ◽  
Vol 12 (1) ◽  
pp. 10 ◽  
Author(s):  
ARIF IMRAN ◽  
LIANE OKDINAWATI
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Variable Neighborhood Search ◽  
Initial Solution ◽  
Neighborhood Search ◽  
Search Methods ◽  
Routing Problem ◽  
Search Heuristic ◽  
Dijkstra's Algorithm ◽  
Sweep Algorithm

The vehicle routing problem is investigated by using some adaptations of the variable neighborhood search (VNS).The initial solution was obtained by Dijkstra’s algorithm based on cost network constructed by the sweep algorithm andthe 2-opt. Our VNS algorithm use several neighborhoods which were adapted for this problem. In addition, a number oflocal search methods together with a diversification procedure were used. The algorithm was then tested on the data setsfrom the literature and it produced competitive results if compared to the solutions published.

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