A General VNS heuristic for the traveling salesman problem with time windows

Silva, Rodrigo Ferreira da; Urrutia, Sebastián

doi:10.1016/j.disopt.2010.04.002

Cited by 71 publications

(44 citation statements)

References 16 publications

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“…We compare the performance of DABC with the results of two recent best performing algorithms from literature. The first one is based on the beam search hybridized with the ant colony algorithm (BACO) in [29] and the second one is using a global variable neighborhood search (GVNS) in [30]. We restricted the time limit to 60 seconds per run and per instance.…”

Section: Computational Resultsmentioning

confidence: 99%

“…For the future work, the challenging new benchmark suite developed in [30] and solved by the GVNS algorithm will be considered. These instances range from 200 nodes to 400 nodes as well as from the time window width from 100 to 500.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Ohlmann and Thomas [13] proposed a compressed annealing (CA) algorithm. Most recently, [29] uses a Beam-ACO which is a hybrid method combining ant colony optimization with beam search and [30] proposes a general VNS heuristic composed of construction and optimization stages. These two studies are used for comparison with our results.…”

Section: Introductionmentioning

confidence: 99%

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A discrete artificial bee colony algorithm for the traveling salesman problem with time windows

Karabulut

Taşgetiren

2012

2012 IEEE Congress on Evolutionary Computation

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This paper presents a discrete artificial bee colony algorithm (DABC) for solving the traveling salesman problem with time windows (TSPTW) in order to minimize the total travel cost of a given tour. TSPTW is a difficult optimization problem arising in both scheduling and logistic applications. The proposed DABC algorithm basically relies on the destruction and construction phases of iterated greedy algorithm to generate neighboring food sources in a framework of ABC algorithm. In addition, it also relies on a classical 1-opt local search algorithm to further enhance the solution quality. The performance of the algorithm was tested on a benchmark set from the literature. Experimental results show that the proposed DABC algorithm is very competitive to or even better than the best performing algorithms from the literature. Keywords-traveling salesman problem with time windows; artificial bee colony algorithm; iterated greedy algorithm; swarm intelligence; heuristic optimization.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Computational Resultsmentioning

confidence: 99%

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A discrete artificial bee colony algorithm for the traveling salesman problem with time windows

Karabulut

Taşgetiren

2012

2012 IEEE Congress on Evolutionary Computation

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“…Gendreau et al [70] proposed an insertion heuristic, which gradually builds the path to the construction phase and improves the refinement phase. Urrutia et al [71] proposed a two-stage heuristic, based on VNS. In the first step, a feasible solution is manufactured using VNS, wherein the mixed linear, integer, objective function is represented as an infeasibility measurement.…”

Section: Previous Work On Tsptwmentioning

confidence: 99%

A QUBO Model for the Traveling Salesman Problem with Time Windows

et al. 2019

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This work focuses on expressing the TSP with Time Windows (TSPTW for short) as a quadratic unconstrained binary optimization (QUBO) problem. The time windows impose time constraints that a feasible solution must satisfy. These take the form of inequality constraints, which are known to be particularly difficult to articulate within the QUBO framework. This is, we believe, the first time this major obstacle is overcome and the TSPTW is cast in the QUBO formulation. We have every reason to anticipate that this development will lead to the actual execution of small scale TSPTW instances on the D-Wave platform.

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“…For the acceleration of computational time, sacrificing some of the solution's quality by adopting heuristic and metaheuristic approaches is commonly accepted [4][5][6]. Heuristics are fast approximation computational methods divided into construction and improvement heuristics.…”

Section: N } Is the Set Of Nodes Andmentioning

confidence: 99%

Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection

Papalitsas¹,

Karakostas²,

Andronikos³

et al. 2017

Preprint

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General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.

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A General VNS heuristic for the traveling salesman problem with time windows

Cited by 71 publications

References 16 publications

A discrete artificial bee colony algorithm for the traveling salesman problem with time windows

A discrete artificial bee colony algorithm for the traveling salesman problem with time windows

A QUBO Model for the Traveling Salesman Problem with Time Windows

Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection

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