A Fast and Scalable Heuristic for the Solution of Large-Scale Capacitated Vehicle Routing Problems

2021 ◽  
Author(s):  
Luca Accorsi ◽  
Daniele Vigo
Keyword(s):  
Vehicle Routing ◽  
Large Scale ◽  
Computing Time ◽  
Search Space ◽  
Iterated Local Search ◽  
Solution Quality ◽  
Routing Problem ◽  
Acceptance Criterion ◽  
Acceleration Techniques ◽  
Capacitated Vehicle

In this paper, we propose a fast and scalable, yet effective, metaheuristic called FILO to solve large-scale instances of the Capacitated Vehicle Routing Problem. Our approach consists of a main iterative part, based on the Iterated Local Search paradigm, which employs a carefully designed combination of existing acceleration techniques, as well as novel strategies to keep the optimization localized, controlled, and tailored to the current instance and solution. A Simulated Annealing-based neighbor acceptance criterion is used to obtain a continuous diversification, to ensure the exploration of different regions of the search space. Results on extensively studied benchmark instances from the literature, supported by a thorough analysis of the algorithm’s main components, show the effectiveness of the proposed design choices, making FILO highly competitive with existing state-of-the-art algorithms, both in terms of computing time and solution quality. Finally, guidelines for possible efficient implementations, algorithm source code, and a library of reusable components are open-sourced to allow reproduction of our results and promote further investigations.

scholarly journals A Problem-Reduction Evolutionary Algorithm for Solving the Capacitated Vehicle Routing Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Wanfeng Liu ◽  
Xia Li
Keyword(s):  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Solution Quality ◽  
Routing Problem ◽  
Individual Solution ◽  
Problem Reduction ◽  
Evaluation Phase ◽  
Capacitated Vehicle

Assessment of the components of a solution helps provide useful information for an optimization problem. This paper presents a new population-based problem-reduction evolutionary algorithm (PREA) based on the solution components assessment. An individual solution is regarded as being constructed by basic elements, and the concept of acceptability is introduced to evaluate them. The PREA consists of a searching phase and an evaluation phase. The acceptability of basic elements is calculated in the evaluation phase and passed to the searching phase. In the searching phase, for each individual solution, the original optimization problem is reduced to a new smaller-size problem. With the evolution of the algorithm, the number of common basic elements in the population increases until all individual solutions are exactly the same which is supposed to be the near-optimal solution of the optimization problem. The new algorithm is applied to a large variety of capacitated vehicle routing problems (CVRP) with customers up to nearly 500. Experimental results show that the proposed algorithm has the advantages of fast convergence and robustness in solution quality over the comparative algorithms.

APPLICATION OF GENETIC ALGORITHMS TO A LARGE-SCALE MULTIPLE-CONSTRAINT VEHICLE ROUTING PROBLEM

2003 ◽  
Vol 03 (01) ◽  
pp. 1-21 ◽  
Author(s):  
GEORGE MOURKOUSIS ◽  
MATHEW PROTONOTARIOS ◽  
THEODORA VARVARIGOU
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
High Performance ◽  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Search Space ◽  
Genetic Operators ◽  
Actual Problem ◽  
Routing Problem

This paper presents a study on the application of a hybrid genetic algorithm (HGA) to an extended instance of the Vehicle Routing Problem. The actual problem is a complex real-life vehicle routing problem regarding the distribution of products to customers. A non homogenous fleet of vehicles with limited capacity and allowed travel time is available to satisfy the stochastic demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs respecting the imposed constraints (vehicle capacity, customer time windows, driver working hours and so on). The approach for solving the problem was based on a "cluster and route" HGA. Several genetic operators, selection and replacement methods were tested until the HGA became efficient for optimization of a multi-extrema search space system (multi-modal optimization). Finally, High Performance Computing (HPC) has been applied in order to provide near-optimal solutions in a sensible amount of time.

scholarly journals A Solution Approach from an Analytic Model to Heuristic Algorithm for Special Case of Vehicle Routing Problem with Stochastic Demands

2008 ◽  
Vol 2008 ◽  
pp. 1-16
Author(s):  
Selçuk K. İşleyen ◽  
Ö. Faruk Baykoç
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Iterated Local Search ◽  
Routing Problem ◽  
Solution Approach ◽  
Stochastic Demands ◽  
Expected Length ◽  
The Traveling Salesman Problem ◽  
Special Case

We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD) where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP) and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS) algorithm in order to reach an effective solution.

scholarly journals Stochastic multi-depot capacitated vehicle routing problem with pickup and delivery: heuristic approaches

2021 ◽  
Author(s):  
Brenner H. O. Rios ◽  
Eduardo C. Xavier ◽  
Flávio K. Miyazawa ◽  
Pedro Amorim
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Iterated Local Search ◽  
Neighborhood Search ◽  
Pickup And Delivery ◽  
Data Set ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Expected Length ◽  
Capacitated Vehicle

We present a natural probabilistic variation of the multi-depot vehicle routing problem with pickup and delivery. We denote this variation by Stochastic multi-depot capacitated vehicle routing problem with pickup and delivery (SMCVRPPD). We present an algorithm to compute the expected length of an apriori route under general probabilistic assumptions. To solve the SMCVRPPD we propose an Iterated Local Search (ILS) and a Variable Neighborhood Search(VNS). We evaluate the performance of these heuristics on a data set adapted from TSPLIB instances. The results show that the ILS is effective to solve SMCVRPPD.

scholarly journals K-MEDOID PETAL-SHAPED CLUSTERING FOR THE CAPACITATED VEHICLE ROUTING PROBLEM

2021 ◽  
Vol 32 (2) ◽  
pp. 33-41
Author(s):  
Jacoba Bührmann ◽  
Frances Bruwer
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Travel Cost ◽  
Management Tool ◽  
Clustering Method ◽  
Routing Problem ◽  
Distribution Management ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle

In this research, k-medoid clustering is modelled and evaluated for the capacitated vehicle routing problem (CVRP). The k-medoid clustering method creates petal-shaped clusters, which could be an effective method to create routes in the CVRP. To determine routes from the clusters, an existing metaheuristic — the ruin and recreate (R&R) method — is applied to each generated cluster. The results are benchmarked to those of a well-known clustering method, k-means clustering. The performance of the methods is measured in terms of travel cost and distance travelled, which are well-known metrics for the CVRP. The results show that k-medoid clustering method outperforms the benchmark method for most instances of the test datasets, although the CVRP without any predefined clusters still provides solutions that are closer to optimal. Clustering remains a reliable distribution management tool and reduces the processing requirements of large-scale CVRPs.

A HYBRID SEARCH ALOGRITHM FOR THE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS

2001 ◽  
Vol 10 (03) ◽  
pp. 431-449 ◽  
Author(s):  
WEE-KIT HO ◽  
JUAY CHIN ANG ◽  
ANDREW LIM
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Search Space ◽  
Hybrid Search ◽  
Solution Quality ◽  
Routing Problem ◽  
Complete Problems ◽  
Search Heuristics ◽  
The Individual

The vehicle routing problem with time windows (VRPTW) is an extension of the well-known vehicle routing problem (VRP). It involves a fleet of homogeneous vehicles, originating and terminating at a central depot, with limited capacity and maximum travel time to service a set of customers with known demands and service-time windows. The objective is to find a set of feasible routes that minimizes the total costs using some measures of solution quality. This paper focuses on the study of a hybrid of two search heuristics, Tabu Search (TS) and Genetic Algorithm (GA) on VRPTW. TS is a local search technique that has been successfully applied to many NP-complete problems. On the other hand, GA which is capable of searching multiple search areas in a search space is good in diversification. In this paper, we create a hybrid that combines the strengths of the two search heuristics. Experimental results indicate that such a hybrid outperforms the individual heuristics alone.

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