Solving the capacitated clustering problem with variable neighborhood search

Brimberg, Jack; Mladenović, Nenad; Todosijević, Raca; Urošević, Dragan

doi:10.1007/s10479-017-2601-5

Cited by 29 publications

(25 citation statements)

References 13 publications

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“…The whole process is iterated until a predefined stopping condition (e.g., maximum number of iterations or maximum CPU time) is met. Many variants of VNS have been derived from the Basic VNS scheme and applied to different optimization problems (for a survey, see, e.g., Hansen et al, ; for recent publications, see Irawan et al, ; Janković et al, ; Pinto et al, ; Brimberg et al, ; Chagas et al, ; Gruler et al, ).…”

Section: Basic Variable Neighborhood Search For the Opmpmentioning

confidence: 99%

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

Mladenović

Alkandari

Pei

et al. 2019

Int Trans Operational Res

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The goal of the less is more approach (LIMA) for solving optimization problems that has recently been proposed in Mladenović et al. (2016) is to find the minimum number of search ingredients that make a heuristic more efficient than the currently best. In this paper, LIMA is successfully applied to solve the obnoxious p‐median problem (OpMP). More precisely, we developed a basic variable neighborhood search for solving the OpMP, where the single search ingredient, the interchange neighborhood structure, is used. We also propose a new simple local search strategy for solving facility location problems, within the interchange neighborhood structure, which is in between the usual ones: first improvement and best improvement strategies. We call it facility best improvement local search. On the basis of experiments, it appeared to be more efficient and effective than both first and best improvement. According to the results obtained on the benchmark instances, our heuristic turns out to be highly competitive with the existing ones, establishing new state‐of‐the‐art results. For example, four new best‐known solutions and 133 ties are claimed in testing the set with 144 instances.

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Section: Basic Variable Neighborhood Search For the Opmpmentioning

confidence: 99%

Less is more approach: basic variable neighborhood search for the obnoxious p‐median problem

Mladenović

Alkandari

Pei

et al. 2019

Int Trans Operational Res

Self Cite

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“…As previously mentioned, the neighborhood exploited by FLS is defined by a joint use of three basic move operators, which have previously been employed in [7,10,24,29,30]. These operators are briefly described as follows:…”

Section: Neighborhood Structuresmentioning

confidence: 99%

“…This section provides an extensive assessment of the proposed algorithms on a well-known set of 133 benchmark instances from CCPLIB, as well as on two new groups of 50 large instances recently generated in [7]. To evaluate the effectiveness of the proposed approaches, we perform comparisons with several state-of-the-art algorithms from the literature.…”

Section: Computational Experimentsmentioning

confidence: 99%

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Heuristic search to the capacitated clustering problem

Zhou

Benlic²,

et al. 2019

European Journal of Operational Research

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Given a weighted graph, the capacitated clustering problem (CCP) is to partition a set of nodes into a given number of distinct clusters (or groups) with restricted capacities, while maximizing the sum of edge weights corresponding to two nodes from the same cluster. CCP is an NP-hard problem with many relevant applications. This paper proposes two effective algorithms for CCP: a Tabu Search (denoted as FITS) that alternates between exploration in feasible and infeasible search space regions, and a Memetic Algorithm (MA) that combines FITS with a dedicated cluster-based crossover. Extensive computational results on five sets of 183 benchmark instances from the literature indicate that the proposed FITS competes favorably with the state-of-the-art algorithms. Additionally, an experimental comparison between FITS and MA under an extended time limit demonstrates that further improvements in terms of the solution quality can be achieved with MA in most cases. We also analyze several essential components of the proposed algorithms to understand their importance to the success of these approaches.

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“…There are some works already published about VNS as Affi et al (2017) for vehicle routing problem, Amous et al (2017) for capacitated vehicle routing problem, and Brimberg et al (2017) for capacitated grouping problem, for instance. Despite that, this algorithm was not evaluated on forest production planning problem.…”

Section: Introductionmentioning

confidence: 99%