Vehicle Routing Problem with Time Windows, Part II: Metaheuristics

Bräysy, Olli; Gendreau, Michel

doi:10.1287/trsc.1030.0057

Cited by 644 publications

(221 citation statements)

References 80 publications

(232 reference statements)

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“…Cordeau et al [5] review a number of different approaches, and the recent surveys by Bräysy and Gendreau [6,7] provide a complete list of studies utilizing a number of heuristics and a comparison of the results obtained. Although numerous metaheuristic methods have been proposed, this paper concentrates on those using evolutionary computation methods, since they have a natural approach for dealing with multi-objective problems, and have been successful in many practical situations [8].…”

Section: Introductionmentioning

confidence: 99%

An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows

García-Nájera

Bullinaria

2011

Computers & Operations Research

155

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The Vehicle Routing Problem with Time Windows is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. Its main objective is to find the lowest distance set of routes to deliver goods, using a fleet of identical vehicles with restricted capacity, to customers with service time windows. However, there are other objectives, and having a range of solutions representing the trade-offs between objectives is crucial for many applications. Although previous research has used evolutionary methods for solving this problem, it has rarely concentrated on the optimization of more than one objective, and hardly ever explicitly considered the diversity of solutions. This paper proposes and analyzes a novel multi-objective evolutionary algorithm, which incorporates methods for measuring the similarity of solutions, to solve the multi-objective problem. The algorithm is applied to a standard benchmark problem set, showing that when the similarity measure is used appropriately, the diversity and quality of solutions is higher than when it is not used, and the algorithm achieves highly competitive results compared with previously published studies and those from a popular evolutionary multi-objective optimizer.

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Section: Introductionmentioning

confidence: 99%

An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows

García-Nájera

Bullinaria

2011

Computers & Operations Research

155

View full text Add to dashboard Cite

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“…local search) [6]. Various metaheuristics for vehicle routing problem can be found in Bräysy and Gendreau survey [7].…”

Section: Fig 1 Capacitated Vehicle Routing Problemmentioning

confidence: 99%

Vehicle Routing Optimization Using Multiple Local Search Improvements

2014

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Original scientific paperCombinatorial optimization problems on graphs arise in many practical applications. One of the most studied practical combinatorial optimization problem is the Vehicle Routing Problem (VRP). When coupled with modern in-car navigation and fleet management software, real world applications of VRP optimization result in significant cost savings. In this paper novel multiple improvements pivoting rule for Capacitated VRP (CVRP) is proposed. Its application significantly reduces computational time needed for CVRP optimization. A novel pivoting rule is implemented as part of the search step selection mechanism in the Iterated Local Search algorithm. Augmented iterated local search algorithm is tested on 4 large scale real-world problems in Croatia with up to 7, 065 customers and 236 vehicles, and on standard CVRP benchmark sets. Real-world problem data was obtained from a large Croatian logistics company. Comparison of well known first and best pivoting rules with proposed novel multiple improvements pivoting rule regarding travel distance, number of search moves and computational time is given. Achieved computational speed-ups are up to 29 times compared to the first improvement pivoting rule and 9 times compared to the best improvement pivoting rule, without any substantial degradation in quality of the obtained solution. Key words: VRP, CVRP, iterated local search, multiple improvementsOptimizacija usmjeravanja vozila primjenom višestrukih poboljšanja u lokalnom pretraživanju. Kombinatoričke optimizacije na grafu pojavljuju se u mnogim aplikacijama u praksi. Jedan od najviše proučavanih kombinatoričkih optimizacijskih problema je problem usmjeravanja vozila. Ukoliko se optimizacija usmjeravanja vozila poveže sa suvremenim u vozila ugra enim sustavima navigacije i nadgledanja voznog parka moguće je postići značajne uštede u troškovima dostave. U ovom radu je predložen novi mehanizam odabira smjera lokalnog pretraživanja zasnovan na višestrukim poboljšanjima za rješavanje kapacitivnog problema usmjeravanja vozila. Predloženi novi mehanizam je implementiran kao dio mehanizma odabira smjera lokalnog pretraživanja u algoritmu iterativnog lokalnog pretraživanja. Prošireni algoritam iterativnog lokalnog pretraživanja je provjeren na 4 vrlo velika optimizacijska problema sa stvarnim podacima iz Hrvatske (skup od 7.065 kupaca i 236 dostavnih vozila) i na standardnim testnim skupovima. Stvarni testni podaci dobiveni su od jedne velike hrvatske logističke tvrtke. U radu je napravljena usporedba izme u mehanizama odabira smjera lokalnog pretraživanja zasnovanih na prvom i najboljem poboljšanju te predloženog mehanizma poboljšanja lokalne pretrage. Usporedba je napravljena prema prije enom putu, broju pomaka lokalnog pretraživanja i vremenu izračuna. Dobiveni rezultati pokazuju ubrzanje u vremenu izračuna za 29 puta u usporedbi sa prvim smjerom poboljšanja lokalne pretrage te 9 puta u usporedbi sa najboljim smjerom poboljšanja lokalne pretrage bez značajnijih degradacija u kvaliteti dobivenog rješenja.

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“…They are called hard when violations are not permitted, i.e., vehicles are allowed to wait with no cost if they arrive early and they are prohibited to serve if they arrive late. For reviews on the VRPTW the reader is referred to Bräysy and Gendreau [4,5], Gendreau and Tarantilis [17], and Kallehauge [20].…”

Section: Introductionmentioning

confidence: 99%