A Simulated Annealing method to solve a generalized maximal covering location problem

Tabrizi, Behzad Bankian; Jabalameli, Mohammad Saeed; Moshref‐Javadi, Mohammad

doi:10.5267/j.ijiec.2011.01.003

Cited by 5 publications

(5 citation statements)

References 16 publications

(9 reference statements)

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“…In this example, the facility j = 1 is closed and the facility j = 2 is opened. Several heuristics (such as Greedy-Add [18] and Lagrangian Relaxation [13]) and metaheuristics (such as Genetic Algorithms [19], Simulated Annealing [21] and Tabu Search [22]) have been used to solve MCLP in an approximate way. They have obtained good results for large instances.…”

Section: Algorithm 1 Original Objectivementioning

confidence: 99%

“…CPLEX [15], Gurobi [16], LINGO [17], among others, are the main tools that have been used to solve the MCLP in an exact way for small instances. Heuristics (such as Greedy-Add [18] and Lagrangian Relaxation [13]), and metaheuristics (such as Genetic Algorithms [19], GRASP [20], Simulated Annealing [21] and Tabu Search [22]) and Hybrid approaches [23] have been used to solve the MCLP in an approximate manner. These approximate methods have obtained good results for large-scale instances.…”

Section: Introductionmentioning

confidence: 99%

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Partial Evaluation and Efficient Discarding for the Maximal Covering Location Problem

et al. 2021

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The maximal covering location problem attempts to locate a limited number of facilities in order to maximize the coverage over a set of demand nodes. This problem is NP-Hard and it has been often addressed by using metaheuristics, where the execution time directly depends on the number of evaluations of the objective function. In this article, the principles of efficient discarding and partial evaluation are applied to obtain more efficient versions of the objective function of this problem, i.e. not-approximate surrogate objective functions. An experimental study is presented to compare the surrogate functions in terms of number of distance comparisons and runtime. The results show that (on average) the best surrogate function is more than 5 times faster than the original function in general, and more than 8 times faster in the largest instances. This proposal allows for a more efficient metaheuristic solution based on swap operators.

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Section: Algorithm 1 Original Objectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Partial Evaluation and Efficient Discarding for the Maximal Covering Location Problem

et al. 2021

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“…Berman et al (2013) considered this problem on networks with a general decay function. Jabalameli et al (2011) developed a special form of the MCLP model on networks in which methods of gradual coverage, cooperative coverage, and covering with variable radius are applied simultaneously.…”

Section: Cooperative Coveringmentioning

confidence: 99%

General form of a cooperative gradual maximal covering location problem

2017

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Cooperative and gradual covering are two new methods for developing covering location models. In this paper, a cooperative maximal covering location-allocation model is developed (CMCLAP). In addition, both cooperative and gradual covering concepts are applied to the maximal covering location simultaneously (CGMCLP). Then, we develop an integrated form of a cooperative gradual maximal covering location problem, which is called a general CGMCLP. By setting the model parameters, the proposed general model can easily be transformed into other existing models, facilitating general comparisons. The proposed models are developed without allocation for physical signals and with allocation for nonphysical signals in discrete location space. Comparison of the previously introduced gradual maximal covering location problem (GMCLP) and cooperative maximal covering location problem (CMCLP) models with our proposed CGMCLP model in similar data sets shows that the proposed model can cover more demands and acts more efficiently. Sensitivity analyses are performed to show the effect of related parameters and the model's validity. Simulated annealing (SA) and a tabu search (TS) are proposed as solution algorithms for the developed models for large-sized instances. The results show that the proposed algorithms are efficient solution approaches, considering solution quality and running time.

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“…An emerging trend in the literature during the last decade is to develop models that take into account more than one of the issues mentioned above (gradual coverage, cooperative coverage, and variable facility radius). Mohammad et al (2011) developed a special form of maximal covering location problem, which combines the characteristics of gradual cover models, cooperative cover models, and variable radius models. The proposed model was formulated as a mixed integer non-linear problem, and the resulting problem was solved using some meta-heuristic method.…”

Section: Introductionmentioning

confidence: 99%

“…Mohammad et al. (2011) developed a special form of maximal covering location problem, which combines the characteristics of gradual cover models, cooperative cover models, and variable radius models. The proposed model was formulated as a mixed integer non‐linear problem, and the resulting problem was solved using some meta‐heuristic method.…”

Section: Introductionmentioning

confidence: 99%

Discrete cooperative coverage location models with alternative facility types in a probabilistic setting

Michopoulou,

Giannikos

2024

Int Trans Operational Res

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In this paper, we discuss discrete location problems where the objective is to locate a given number of facilities of different types in order to appropriately cover a given set of demand points. The coverage provided to each demand point is the result of the cooperation among the located facilities. The different types of facility refer to the coverage radius or quality of coverage that each type may provide. We present a non‐linear formulation of the problem where the objective is to maximize the percentage of demand points that are appropriately covered. We then show how the model can be linearized based on a representation of probabilities through a network structure. To address large instances of the problem, we introduce a genetic algorithm (GA) that is based on the representation of the solution by two chromosomes. Computational experiments over a wide range of randomly generated problems indicate the GA clearly outperforms CPLEX, especially in larger instances.

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A Simulated Annealing method to solve a generalized maximal covering location problem

Cited by 5 publications

References 16 publications

Partial Evaluation and Efficient Discarding for the Maximal Covering Location Problem

Partial Evaluation and Efficient Discarding for the Maximal Covering Location Problem

General form of a cooperative gradual maximal covering location problem

Discrete cooperative coverage location models with alternative facility types in a probabilistic setting

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