Dynamic capacitated maximal covering location problem by considering dynamic capacity

Bagherinejad, Jafar; Shoeib, Mahnaz

doi:10.5267/j.ijiec.2017.5.004

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…GA dan algoritma lebah (Bee Colony) diusulkan untuk memecahkan masalah. Untuk mencapai kinerja yang lebih baik, algoritma ini dipecahkan dengan menggunakan metode Taguchi (Bagherinejad & Shoeib, 2018).…”

Section: Pendahuluanunclassified

Pengembangan Model Capacitated Maximal Covering Location Problem (CMCLP) untuk Penentuan Lokasi dan Tipe Distribution Center

Santoso¹,

Heryanto

2022

Jurnal Optimasi Sistem Industri

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Facility location decision making is necessary for both the public and private sectors for optimum utilization of resources. While the private sector may locate facilities to maximize profit or minimize cost, the public sector aims at providing services to cover as many in the population as possible. In this research, a Capacitated Maximal Covering Location problem (CMCLP) with constraints in actual world such as various types of facilities and consider maximum available budget to build the facilities. An Mixed Integer Linear Programming (MILP) model is constructed in order to find the optimal solution. The problem to be solved is determining the strategic location for establishment of a Distribution Center (DC) that maximize number of demands that can be fulfilled. A numerical example in the previous study will be used and solved using MATLAB 9.0. From the development model, the company finds the optimal location to build a DC and knows the number of products allocated from DC to each demand point and the maximum number of demands that could be fulfilled as in the previous study. The company also could find out the type of DC to be built and of course meet the available budget to build the DC.

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Section: Pendahuluanunclassified

Pengembangan Model Capacitated Maximal Covering Location Problem (CMCLP) untuk Penentuan Lokasi dan Tipe Distribution Center

Santoso¹,

Heryanto

2022

Jurnal Optimasi Sistem Industri

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“…Bagherinejad and Shoeib [24] studied a multi-period maximal covering location problem in which the total number of facilities that have to be opened, is located gradually over time. From this perspective, their model is an implicitly dynamic model.…”

Section: Mclpmentioning

confidence: 99%

Multi-Period Maximal Covering Location Problem with Capacitated Facilities and Modules for Natural Disaster Relief Services

et al. 2021

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The paper aims to study a multi-period maximal covering location problem with the configuration of different types of facilities, as an extension of the classical maximal covering location problem (MCLP). The proposed model can have applications such as locating disaster relief facilities, hospitals, and chain supermarkets. The facilities are supposed to be comprised of various units, called the modules. The modules have different sizes and can transfer between facilities during the planning horizon according to demand variation. Both the facilities and modules are capacitated as a real-life fact. To solve the problem, two upper bounds—(LR1) and (LR2)—and Lagrangian decomposition (LD) are developed. Two lower bounds are computed from feasible solutions obtained from (LR1), (LR2), and (LD) and a novel heuristic algorithm. The results demonstrate that the LD method combined with the lower bound obtained from the developed heuristic method (LD-HLB) shows better performance and is preferred to solve both small- and large-scale problems in terms of bound tightness and efficiency especially for solving large-scale problems. The upper bounds and lower bounds generated by the solution procedures can be used as the profit approximation by the managerial executives in their decision-making process.

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“…Tal abordagem consiste em aplicar alterac ¸ões nos cromossomos do AG, por meio de um processo de clusterizac ¸ão, para determinar a localizac ¸ão e o número de BTS, de forma a permitir a cobertura de todos os clientes. Enquanto em [Bagherinejad and Shoeib 2018] tratam o problema de localizac ¸ão de cobertura máxima em multi-períodos. A sua soluc ¸ão é baseada no uso de algoritmos de AG e colônia de abelhas.…”

Section: Trabalhos Relacionadosunclassified

“…Diferentes SADs foram propostos para lidar com o problema do MCLP. Na maior parte desses estudos [Mehboob et al 2016, Atta et al 2018, Bagherinejad and Shoeib 2018, Seda et al 2020, as soluc ¸ões propostas sugerem melhorias para lidar com MCLP de maneira genérica, i.e., apesar de existir abordagens que exploram aplicac ¸ões do MCLP para domínio específico, como posicionamento de garagens de veículos [Gazani and Niaki 2021], alocac ¸ão de antenas [Costa 2016] ou até mesmo veículos de comunicac ¸ão [Yang et al 2020] aplicados em operac ¸ões de resgate, tais abordagens tratam apenas de um subconjunto do problema, não refletindo a modelagem para cenários reais. A modelagem de [Costa 2016] é simplificada e seus resultados são avaliados apenas em termos de velocidade.…”

Section: Introduc ¸ãOunclassified

Soluções Otimizadas para o Problema de Localização de Máxima Cobertura em Redes Militarizadas 4G/LTE

Antunes

Caetano

Marotta

et al. 2021

Anais Do XXVI Workshop De Gerência E Operação De Redes E Serviços (WGRS 2021)

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Este trabalho propõe resolver o problema de localização de máxima cobertura do Centro de Coordenação de Operações Móvel (CCOp Mv) que visa apoiar o comando operacional do Exército Brasileiro. Este problema consiste em selecionar, em uma região limitada e com infraestrutura precária de comunicação na área de operação das tropas terrestres, as posições dos veículos equipados com antenas para maximizar a área de cobertura. Em razão disso, foi proposta uma modelagem analítica baseada em Problemas Lineares Inteiros Mistos que fundamentou duas soluções de otimização: (i) EALLOCATOR – Exact ALLOCATiOn seRvice; e (ii) M-ALLOCATOR – Metaheuristic ALLOCATiOn seRvice. As soluções foram avaliadas em um cenário que emprega o uso do CCOp Mv em apoio à uma operação de resgate com base na tragédia ocorrida em janeiro de 2019 em Brumadinho-MG. Os resultados mostraram que o E-ALLOCATOR é adequado quando há baixa carga de trabalho, enquanto o M-ALLOCATOR é adequado para os cenários com alta carga de trabalho. Além disso, os resultados indicam que o M-ALLOCATOR fornece soluções subótimas dentro de um tempo computacional adequado para todas as instâncias do problema.

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Dynamic capacitated maximal covering location problem by considering dynamic capacity

Cited by 10 publications

References 29 publications

Pengembangan Model Capacitated Maximal Covering Location Problem (CMCLP) untuk Penentuan Lokasi dan Tipe Distribution Center

Pengembangan Model Capacitated Maximal Covering Location Problem (CMCLP) untuk Penentuan Lokasi dan Tipe Distribution Center

Multi-Period Maximal Covering Location Problem with Capacitated Facilities and Modules for Natural Disaster Relief Services

Soluções Otimizadas para o Problema de Localização de Máxima Cobertura em Redes Militarizadas 4G/LTE

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