An efficient variable neighborhood search for solving a robust dynamic facility location problem in emergency service network

Mišković, Stefan; Stanimirović, Zorica; Grujičić, Igor

doi:10.1016/j.endm.2014.11.034

Cited by 12 publications

(13 citation statements)

References 5 publications

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“…The idea of alternating neighborhoods is easy to adapt to various problems [76][77][78] and highly efficient, which makes it very useful for solving NP-hard problems including clustering, location, and vehicle routing problems. In [111,112], Brimberg and Mladenovic and Miskovic et al used the VNS for solving various facility location problems. Cranic et al [113] as well as Hansen and Mladenovic [114] proposed and developed a parallel VNS algorithm for the k-median problem.…”

Section: Variable Neighborhood Searchmentioning

confidence: 99%

Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Kazakovtsev¹,

Rozhnov²,

Popov³

et al. 2020

Computation

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The k-means problem is one of the most popular models in cluster analysis that minimizes the sum of the squared distances from clustered objects to the sought cluster centers (centroids). The simplicity of its algorithmic implementation encourages researchers to apply it in a variety of engineering and scientific branches. Nevertheless, the problem is proven to be NP-hard which makes exact algorithms inapplicable for large scale problems, and the simplest and most popular algorithms result in very poor values of the squared distances sum. If a problem must be solved within a limited time with the maximum accuracy, which would be difficult to improve using known methods without increasing computational costs, the variable neighborhood search (VNS) algorithms, which search in randomized neighborhoods formed by the application of greedy agglomerative procedures, are competitive. In this article, we investigate the influence of the most important parameter of such neighborhoods on the computational efficiency and propose a new VNS-based algorithm (solver), implemented on the graphics processing unit (GPU), which adjusts this parameter. Benchmarking on data sets composed of up to millions of objects demonstrates the advantage of the new algorithm in comparison with known local search algorithms, within a fixed time, allowing for online computation.

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Section: Variable Neighborhood Searchmentioning

confidence: 99%

Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Kazakovtsev¹,

Rozhnov²,

Popov³

et al. 2020

Computation

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“…Work presented in [14] explored a deterministic model of dynamic location problem arising from optimising the emergency service network of Police Special Forces in Serbia. They proposed a Variable Neighborhood Search method with a local search procedure and compared results with CPLEX 12.1.…”

Section: Problem Backgroundmentioning

confidence: 99%

Introducing the Dynamic Customer Location-Allocation Problem

Ankrah

Lacroix

McCall

et al. 2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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In this paper, we introduce a new stochastic Location-Allocation Problem which assumes the movement of customers over time. We call this new problem Dynamic Customer Location-Allocation Problem (DC-LAP). The problem is based on the idea that customers will change locations over a defined horizon and these changes have to be taken into account when establishing facilities to service customers demands. We generate 1440 problem instances by varying the problem parameters of movement rate which determines the possible number of times a customer will change locations over the defined period, the number of facilities and the number of customers. We propose to analyse the characteristics of the instances generated by testing a search algorithm using the stochastic dynamic evaluation (based on the replication of customer movement scenarios) and a deterministic static evaluation (based on the assumption that customer will not move over time). We show that the dynamic approach obtains globally better results, but the performances are highly related to the parameters of the problem. Moreover, the dynamic approach involves a significantly high computational overhead.

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“…Zarandi et al [37] used simulated annealing to solve multi-period MCLP. Miskovic et al [38] proposed a variable neighborhood search method for optimizing the emergency service network of police special force units in a multi-period manner to minimize the maximum load of established emergency units in all time periods. They solved real-life instances and compared the results with CPLEX.…”

Section: Multi-period Location Modelsmentioning

confidence: 99%

A Tabu Search Algorithm for Multi-Period Bank Branch Location Problem: A Case Study in a Turkish Bank

2018

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Banks need to open new branches in new sites as a result of an increase in the population, promotion of individual earnings, and growth in national economy. In this respect, opening new branches or reorganizing the locations of current branches is an important decision problem for banks to accomplish their strategic objectives. This paper presents a decision support method for multi-period bank branch location problems. The aim is to nd bank branch locations based on transaction volume, distance between branches, and cost of opening and closing branches. The proposed method not only develops an integer program and a Tabu Search (TS) algorithm to nd the exact places of branches, but also presents a structuring method to identify the related criteria and their importance. We demonstrate the e ectiveness of the method on random data. In the nal stage, the method is applied to a Turkish bank's branch location problem considering the current and possible places of the branches, availability of the data, and the bank's strategies for a four-year strategic planning.

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An efficient variable neighborhood search for solving a robust dynamic facility location problem in emergency service network

Cited by 12 publications

References 5 publications

Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Introducing the Dynamic Customer Location-Allocation Problem

A Tabu Search Algorithm for Multi-Period Bank Branch Location Problem: A Case Study in a Turkish Bank

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