Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms

“…To design a λ-interchange local search descent method (λ-LSD), one typically needs to specify the following choices: how an initial feasible solution is generated; which move-generation mechanism to use; which acceptance criterion and the stopping criterion to use (Bräysy and Gendreau, 2005a). The λ-LSD searches the set of neighbors generated by the LSD for a given integer equal to 1 and 2.…”

Section: Algorithmmentioning

confidence: 99%

A Solution for the Bi-Objective Vehicle Routing Problem With Time Windows Using Local Search and Genetic Algorithms

Mungwattana¹,

Manisri²,

Charoenpol³

et al. 2016

IJTTE

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This paper deals with the vehicle routing problem with time windows (VRPTW). The VRPTW routes a set of vehicles to service customers having two-sided time windows, i.e. earliest and latest start of service times. The demand requests are served by capacitated vehicles with limited travel times to return to the depot. The purpose of this paper is to develop a hybrid algorithm that uses the modified push forward insertion heuristic (MPFIH), a λ-interchange local search descent method (λ-LSD) and a genetic algorithm to solve the VRPTW with two objectives. The first objective aims to determine the minimum number of vehicles required and the second is to find the solution that minimizes the total travel time. A set of well-known benchmark problems are used to compare the quality of solutions. The results show that the proposed algorithm provides effective solutions compared with best found solutions and better than another heuristic used for comparison.

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“…Neighboring moves are evaluated with neighborhood operators. For detailed description of operators see [10]. Most common operators for CVRP are relocate, exchange, 2−opt between i + 1 and j.…”

Section: Local Search Operatorsmentioning

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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Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms

Cited by 917 publications

References 58 publications

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A Solution for the Bi-Objective Vehicle Routing Problem With Time Windows Using Local Search and Genetic Algorithms

Vehicle Routing Optimization Using Multiple Local Search Improvements

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