An Effective Variable Neighborhood Search with Perturbation for Location-Routing Problem

2019 ◽  
Vol 28 (07) ◽  
pp. 1950024
Author(s):  
Hua Jiang ◽  
Corinne Lucet ◽  
Laure Devendeville ◽  
Chu-Min Li

Location-Routing Problem (LRP) is a challenging problem in logistics, which combines two types of decision: facility location and vehicle routing. In this paper, we focus on LRP with multiple capacitated depots and one uncapacitated vehicle per depot, which has practical applications such as mail delivery and waste collection. We propose a simple iterated variable neighborhood search with an effective perturbation strategy to solve the LRP variant. The experiments show that the algorithm is efficient and can compute better solutions than previous algorithms on tested instances.

2013 ◽  
Vol 361-363 ◽  
pp. 1900-1905 ◽  
Author(s):  
Ji Ung Sun

In this paper we consider the location-routing problem which combines the facility location and the vehicle routing decisions. In this type of problem, we have to determine the location of facilities within a set of possible locations and routes of the vehicles to meet the demands of number of customers. Since the location-routing problem is NP-hard, it is difficult to obtain optimal solution within a reasonable computational time. Therefore, a two-phase ant colony optimization algorithm is developed which solves facility location problem and vehicle routing problem hierarchically. Its performance is examined through a comparative study. The experimental results show that the proposed ant colony optimization algorithm can be a viable solution method for the general transportation network planning.


2021 ◽  
Vol 6 (4) ◽  
pp. 67-76
Author(s):  
Alireza Mohamadi-Shad ◽  
Hamed Niakan ◽  
Hasan Manzour

In this paper we proposed a new variable neighborhood search (VNS) for solving the location- routing problem with considering capacitated depots and vehicles. A set of capacitated vehicles, a set of depots with restricted capacities, and associated opening costs, and a set of customers with deterministic demands are given. The problem aims to determine the depots to be opened, fleet assignment to each depot, and the routes to be performed to satisfy the demand of the customers. The objective is to minimize the total costs of the open depots, the setup cost associated with the used vehicles, and transportation cost. We proposed a new VNS which is augmented with a probabilistic acceptance criterion as well as a set of efficient local searches. The computational results implemented on four well-known data sets demonstrate that the proposed algorithm is competitive with other well- known algorithms while reaching many best-known solutions and updating six best new results with reasonable computational time. Conclusions and future research avenues close the paper.


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