Articlediamond2011

The large scale maximal covering location problem

M.H. Fazel Zarandi · Soheil Davari · S.A. Haddad Sisakht

76 citations0/0 files storedScientia Iranica

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Facility Location and Emergency Management Metaheuristic Optimization Algorithms Research Vehicle Routing Optimization Methods

Abstract

The maximal covering location problem (MCLP) is a challenging problem with numerous applications in practice. Previous publications in the area of MCLP proposed models and presented solution methodologies to solve this problem with up to 900 nodes. Due to the fact that in real-life applications, the number of nodes could be much higher, this paper presents a customized Genetic Algorithm (GA) to solve MCLP instances, with up to 2500 nodes. Results show that the proposed approach is capable of solving problems with a fair amount of exactness. In order to fine-tune the algorithm, Tukey’s Least Significant Difference (LSD) tests are employed on a set of test problems.

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References

1974THE MAXIMAL COVERING LOCATION PROBLEM 2007A bibliography for some fundamental problem categories in discrete location science 2006A multiperiod set covering location model for dynamic redeployment of ambulances 2008Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm 2003A maximal covering location model in the presence of partial coverage 2007Determining Optimal Police Patrol Areas with Maximal Covering and Backup Covering Location Models 2003The gradual covering decay location problem on a network 2009Generalized coverage: New developments in covering location models

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2017Solving maximal covering location problem using genetic algorithm with local refinement 2019Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances 2019Balanced maximal covering location problem and its application in bike-sharing 2020Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

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