The continuous maximal covering location problem in large-scale natural disaster rescue scenes

Yang, Pei; Xiao, Yiyong; Zhang, Yue; Zhou, Shenghan; Yang, Jingwen; Xu, Yuchun

doi:10.1016/j.cie.2020.106608

Cited by 20 publications

(12 citation statements)

References 36 publications

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“…Salienta-se que o MCLP foi inicialmente explorado por [Church and ReVelle 1974], resultando no desenvolvimento de modelos voltados para a cobertura do maior número de pontos de demanda com um número mínimo de BTS. Atualmente, o MCLP vem sendo investigado adicionando-se novas características ao problema, como energia de transmissão, perda de sinal, tempo de processamento, bem como o uso de novos algoritmos heurísticos e meta-heurísticos para solucionar o problema [Mehboob et al 2016, Yang et al 2020, Alizadeh et al 2021.…”

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

“…A modelagem de [Costa 2016] é simplificada e seus resultados são avaliados apenas em termos de velocidade. As abordagens de [Costa 2016] e [Yang et al 2020] não exploram os ajustes dos hiperparâmetros dos algoritmos meta-heurísticos empregados. Como citado em [Farahani et al 2012] e ratificado por [Alizadeh et al 2021], os modelos desenvolvidos estão longe dos problemas do mundo real, indicando um campo de pesquisa promissor que este trabalho investiga.…”

Section: Introduc ¸ãOunclassified

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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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Section: Trabalhos Relacionadosunclassified

Section: Introduc ¸ãOunclassified

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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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“…Developing a model for each of the applications with the specific real situation, resulted in various extensions developed for the covering models since their introduction. The hierarchical MCLP [2,3], probabilistic MCLP [4,5] MCLP in competitive environments [6], large-scale dynamic MCLP [7], continuous MCLP for natural disasters [8], and maximal hub location problem [9] are some examples among the vast literature of the MCLP. The growing attention and interest in covering location problems are due to their applications.…”

Section: Introductionmentioning

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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“…For example, Davari et al [31] developed a MCLP with fuzzy travel times; Arana-Jiménez et al [32] developed a fuzzy MCLP; Vatsa and Jayaswal ( [33,34]) modeled a capacitated multiperiod MCLP with server uncertainty; and Cordeau et al [9] introduced the MCLP algorithm to determine a subset of facilities, maximizing customer requests by considering budget constraints. A continuous MCLP was also developed by Yang et al [35] to optimize a continuous location of the cellular network's communication centers for natural disaster rescue. ReVelle et al [36] also solved the MCLP with heuristic concentration which is used to determine a prominent case solution to maximum coverage locations with a high coverage percentage.…”

Section: Introductionmentioning

confidence: 99%

Extended Maximal Covering Location and Vehicle Routing Problems in Designing Smartphone Waste Collection Channels: A Case Study of Yogyakarta Province, Indonesia

2021

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Most people will store smartphone waste or give it to others; this is due to inadequate waste collection facilities in all cities/regencies in Indonesia. In Yogyakarta Province, there is no electronic waste collection facility. Therefore, an e-waste collection network is needed to cover all potential e-waste in the province of Yogyakarta. This study aims to design a collection network to provide easy access to facilities for smartphone users, which includes the number and location of each collection center and the route of transporting smartphone waste to the final disposal site. We proposed an extended maximal covering location problem to determine the number and location of collection centers. Nearest neighbor and tabu search are used in forming transportation routes. The nearest neighbor is used for initial solution search, and tabu search is used for final solution search. The study results indicate that to facilitate all potential smartphone waste with a maximum distance of 11.2 km, the number of collection centers that must be established is 30 units with three pick-up routes. This research is the starting point of the smartphone waste management process, with further study needed for sorting, recycling, repairing, or remanufacturing after the waste has been collected.

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The continuous maximal covering location problem in large-scale natural disaster rescue scenes

Cited by 20 publications

References 36 publications

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

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

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

Extended Maximal Covering Location and Vehicle Routing Problems in Designing Smartphone Waste Collection Channels: A Case Study of Yogyakarta Province, Indonesia

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