Active guided evolution strategies for large-scale vehicle routing problems with time windows

Mester, David; Bräysy, Olli

doi:10.1016/j.cor.2003.11.017

Cited by 185 publications

(107 citation statements)

References 53 publications

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“…A new and effective meta-heuristic algorithm, active guided evolution strategies, for the VRPTW is presented by [52]. The algorithm combines the strengths of the guided local search and evolution strateg eta-heuristics into an iterative two-stage procedure.…”

Section: Meta-heuristicsmentioning

confidence: 99%

A Survey on the Vehicle Routing Problem and Its Variants

Kumar¹,

Panneerselvam²

2012

IIM

226

126

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In this paper, we have conducted a literature review on the recent developments and publications involving the vehicle routing problem and its variants, namely vehicle routing problem with time windows (VRPTW) and the capacitated vehicle routing problem (CVRP) and also their variants. The VRP is classified as an NP-hard problem. Hence, the use of exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. The vehicle routing problem comes under combinatorial problem. Hence, to get solutions in determining routes which are realistic and very close to the optimal solution, we use heuristics and meta-heuristics. In this paper we discuss the various exact methods and the heuristics and meta-heuristics used to solve the VRP and its variants.

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Section: Meta-heuristicsmentioning

confidence: 99%

A Survey on the Vehicle Routing Problem and Its Variants

Kumar¹,

Panneerselvam²

2012

IIM

226

126

View full text Add to dashboard Cite

show abstract

“…In our experiments, such upper bound was set equal to the best of three upper bounds: (a) the upper bound computed by the heuristic of Taillard et al [1996] (sent us by private communication Taillard [2009]), (b) the upper bound reported in Olivera and Viera [2007], and (c) the cost of the CVRP solution found by the heuristic of Mester and Bräysy [2005] if the routes of such solution can be arranged so as to obtain a feasible MTVRP solution.…”

Section: Computational Resultsmentioning

confidence: 99%

“…The estimated computing time T U B is the sum of the computing times of the algorithms of Taillard et al [1996], Olivera and Viera [2007], and Mester and Bräysy [2005] normalized to our machine by considering that, according to http://www.cpubenchmark.net/cpu_list.php, our machine is ≈ 100 times faster than the 100 MHz machine used by Taillard et al [1996], 4 times faster than the 1.8 GHz AMD Athlon XP 2200+ used by Olivera and Viera [2007], and 6.3 times faster than the Pentium 4 2 GHz used by Mester and Bräysy [2005].…”

Section: Computational Resultsmentioning

confidence: 99%

“…For example, by increasing such upper bound by 1%, the exact method, as it is, could not solve any of the instances of the classes CMT-2, CMT-3 and CMT-11, and it could solve only 12 out of 17 instances of the classes CMT-1 and CMT-12. Anyway, as shown in Table 5.1, high-quality upper bounds can be computed in a few minutes by running the heuristic algorithms of Taillard et al [1996], Olivera and Viera [2007], and Mester and Bräysy [2005].…”

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confidence: 99%

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Exact algorithms for different classes of vehicle routing problems

Roberti¹

2012

4OR-Q J Oper Res

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“…For example, the most powerful Operations Research (OR) heuristics for the VRP, referred in the extensive surveys (Gendreau & al., 2002), (Cordeau & al., 2005), are based on metaheuristic frameworks as the Tabu Search, simulated annealing, and population based methods, such as evolutionary algorithms, adaptive memory and ant algorithms. Other methods can hybridize several metaheuristics principles, such as for example the very powerful active guided local search (Mester & Bräysy, 2005), which is maybe the overall winner approach considering both quality solution and computation time.…”

mentioning

confidence: 99%