A multi-objective formulation of maximal covering location problem with customers’ preferences: Exploring Pareto optimality-based solutions

Atta, Soumen; Mahapatra, Priya Ranjan Sinha; Mukhopadhyay, Anirban

doi:10.1016/j.eswa.2021.115830

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…In [46] an uncapacitated FLP is proposed as bi-objective formulation with fractional and linear objectives. Many interesting biobjective real-time work has been developed, such as in [28] capacitated FLP was dealt with for locating certain facilities with minimum sum of objectives, in [47], Current et al have treated shortest path problems as bi-objective formulation with coverage and cost as two contrasting objectives [48].…”

Section: Literature Reviewmentioning

confidence: 99%

A Multiobjective Evolutionary Approach to Solving Single-Allocation Hub Median Problem

Bhattacharjee,

Mukhopadhyay

2024

Preprint

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This article presents a multiobjective formulation for the well-known Single-Allocation Hub Median Problem (MO-SA-H-MP). The objective of MO-SA-H-MP is to develop a three-level architecture consisting of demand nodes, hubs, and central hubs, for reducing transportation costs among nodes, while considering two objectives. The first objective is focused on reducing the overheads associated with hubs and central hubs, while the second objective is aimed at reducing transportation costs among nodes. The paper uses two approaches to solve MO-SA-H-MP. The first approach is based on the NSGA-II algorithm, while the second approach uses a Genetic Algorithm (GA) with a local refinement-based technique to solve each objective separately. The resultant network obtained from GA is applied to the other objective, and the solutions of both approaches are compared. The NSGA-II-based approach is found to perform equivalently to the exact method in 48.32% of cases, perform better than the indirect approach of solving each objective separately in more than 81.67% of cases, and have a deviation of less than 10% in 67.50% of cases from the direct approach for solving each objective separately using the Refined GA-based technique.

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Section: Literature Reviewmentioning

confidence: 99%

A Multiobjective Evolutionary Approach to Solving Single-Allocation Hub Median Problem

Bhattacharjee,

Mukhopadhyay

2024

Preprint

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“…The essence of this problem is to maximize or minimize one or more mathematical functions to solve the allocation scheme of public facilities resources, such as the locations of fire stations, hospitals, schools, and environmental monitoring stations, as well as reserve site selection (Church et al, 1996;Malcolma and Revelle, 2005;Murray, 2010;Tong and Murray, 2012). Approaches for such location modeling focus on the P-median location-allocation model (Church and Wang, 2020;Zaferanieh et al, 2022), the maximum minimization model (Wang and Zhang, 2012), the maximum coverage location problem (MCLP) (Atta et al, 2021;Taiwo, 2021), and continuous model of coverage problem (CMCP) (Yang et al, 2020;Blanco and Gaźquez, 2021). Based on the vertex weights and correction costs as independent uncertain variables (both side length and vertex weights are variable), Soltanpour et al (2020) proposed a model of an uncertain inverse P-median location problem to deal with tail values at risk targets and proved that it is a nondeterminism Polynomial(NP) problem; meanwhile, a hybrid PSO algorithm was proposed to obtain the approximate optimal solution of the proposed model.…”

Section: Related Workmentioning

confidence: 99%

An intelligent modeling framework to optimize the spatial layout of ocean moored buoy observing networks

Liu¹,

Song²,

Chen³

et al. 2023

Front. Mar. Sci.

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This research is motivated by the practical requirements in the sustainable deployment of ocean moored buoy observing networks. Ocean moored buoys play an important role in the global marine environment monitoring. Ocean buoy station layout planning is a typical multiple-objective spatial optimization problem that aims to reduce the spatial correlation of buoy stations and improve their spatial monitoring efficiency. In this paper, we develop a multi-objective mathematical model for allocating ocean buoy stations (MOLMofOBS) based on Tobler’s first law of geography. A spatial neighborhood model based on a Voronoi diagram is built to represent the spatial proximity of distributed buoy stations and delimit the effective monitoring region of every station. Then, a heuristic method based on a multiple-objective particles swarm optimization (MOPSO) algorithm is developed to calculate the MOLMofOBS via a dynamic inertia weight strategy. Meanwhile, a series of experiments is conducted to verify the efficiency of the proposed model and algorithms in solving single- and multiple-buoy station location problems. Finally, an interactive portal is developed in the Cyberinfrastructure environment to provide decision-making services for online real-time planning of the ocean buoy station locations. The work reported in this paper will provide spatial decision-making support for the sustainable development of ocean buoy observing networks.

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“…As defined by Atta et al (2021), the maximum coverage location problem (MCLP) is a well-known combinatorial optimization issue with several applications in emergency and military services, as well as in public services. According to the conventional definition, MCLP is a single objective problem with the purpose of maximizing the total number of customer requests that can be met by a specific number of operational facilities.…”

Section: Introductionmentioning

confidence: 99%

Location Analysis of Fire Stations in Cagayan de Oro City using Minimum Impedance (P-Median Problem) and Maximal Covering Location Problem (MCLP) with Q-Coverage Requirement Approaches

Labita,

Namoco

2023

MJST

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This study aimed to address the problem of the Bureau of Fire Protection (BFP) in determining the strategic locations of the fire stations in Cagayan de Oro City, Philippines to provide a fast and timely response using the facility location problem (FLP). This study compared two FLP models, namely minimum impedance and the maximal covering location problem (MCLP) to determine the optimal number and the respective best locations of the fire stations without relocation. In addition, a set of adopted performance criteria was employed to evaluate which model fitted the problem. In the integration of the Q-coverage requirement, results identified the backup fire stations of each barangay (village) if the primary fire station is unavailable or responding to other demands. The results revealed that MCLP performed better than the minimum impedance across the average travel distances of 1.19, 3.43, and 4.44 km for Q values 1, 2 and 3, respectively. Moreover, MCLP outperformed each of the three criteria for all of the Q values. Thus, the MCLP provided an efficient application for deciding on the locations of fire stations to minimize the travel distance between demand, primary and backup fire stations, thereby fulfilling its mandate of protecting communities from destructive fires and other emergencies.

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A multi-objective formulation of maximal covering location problem with customers’ preferences: Exploring Pareto optimality-based solutions

Cited by 7 publications

References 47 publications

A Multiobjective Evolutionary Approach to Solving Single-Allocation Hub Median Problem

A Multiobjective Evolutionary Approach to Solving Single-Allocation Hub Median Problem

An intelligent modeling framework to optimize the spatial layout of ocean moored buoy observing networks

Location Analysis of Fire Stations in Cagayan de Oro City using Minimum Impedance (P-Median Problem) and Maximal Covering Location Problem (MCLP) with Q-Coverage Requirement Approaches

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