An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

Kytöjoki, Jari; Nuortio, Teemu; Bräysy, Olli; Gendreau, Michel

doi:10.1016/j.cor.2005.10.010

Cited by 240 publications

(100 citation statements)

References 55 publications

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“…Table 6 provides a comparison on the results with some of the metaheuristics published in the literature. The enhanced ABC heuristic outperforms the deterministic annealing algorithm described in Golden et al (1998), the granular tabu search by Toth and Vigo (2003), the very large neighborhood search by Ergun et al (2006), the path relinking approach by Ho and Gendreau (2006) and the variable neighborhood search procedure by Kytöjoki et al (2007). As with the classical instances, population-based heuristics combined with local search also generated the best results for the large-scale instances.…”

Section: Original Abc Vs Enhanced Abcmentioning

confidence: 86%

An artificial bee colony algorithm for the capacitated vehicle routing problem

Szeto

2011

European Journal of Operational Research

353

128

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This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated vehicle routing problem.

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Section: Original Abc Vs Enhanced Abcmentioning

confidence: 86%

An artificial bee colony algorithm for the capacitated vehicle routing problem

Szeto

2011

European Journal of Operational Research

353

128

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“…As mentioned before, the CVRP is a NP−hard problem [4], so exact algorithms are suitable only for solving smaller sized problems with up to 50 customers in reasonable computing time [5]. Problems with larger number of customers are most commonly solved with metaheuristic algorithms.…”

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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“…VNS has been previously implemented in a range of combinatorial problems (Hansen et al, 2010) including VRP models (Bräysy, 2003;Kytöjoki, Nuortio et al, 2007) and the TDVRP with soft time windows (Kritzinger, Tricoire, Doerner, & Hartl, 2011). VNS uses local search neighbourhoods and avoids local optima with specially designed procedures called "Shaking" which usually have random elements (Hansen & Mladenović, 2001;Hansen et al, 2010).…”

Section: Variable Neighbourhood Searchmentioning

confidence: 99%

A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows

Rincón-García¹,

Waterson²,

Cherrett³

2017

10.5267/j.ijiec

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This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an 'always feasible' search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable time.

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An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

Cited by 240 publications

References 55 publications

An artificial bee colony algorithm for the capacitated vehicle routing problem

An artificial bee colony algorithm for the capacitated vehicle routing problem

Vehicle Routing Optimization Using Multiple Local Search Improvements

A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows

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