Solving an ammunition distribution network design problem using multi-objective mathematical modeling, combined AHP-TOPSIS, and GIS

Akgün, İbrahim; Erdal, Hami̇t

doi:10.1016/j.cie.2019.02.004

Cited by 41 publications

(19 citation statements)

References 78 publications

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“…The Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) method includes five steps: “… … . build the standard decision matrix, compute the normalized and the weighted normalized decision matrices, determine the positive‐ideal and negative‐ideal solutions, calculate separation measures (a distance of each alternative from the ideal solutions), compute the relative closeness of each alternative to the ideal solution.” In the present study, the standard decision matrix “D” of step 1 in TOPSIS can be built as following based on the functions from to :

normalD = ([], \begin{matrix} 0 & 35.9 a & 35.9 b 107.7 c 5 d \\ 0 & 33.7 a & \begin{matrix} 33.7 b & 67.4 c & 3 d \end{matrix} \\ \begin{matrix} 0 \\ M \\ \begin{matrix} M \\ M \end{matrix} \end{matrix} & \begin{matrix} 1.9 a \\ 49.9 a \\ \begin{matrix} 36.1 a \\ 39.8 a \end{matrix} \end{matrix} & \begin{matrix} \begin{matrix} 1.9 b & 0 & 3 d \end{matrix} \\ \begin{matrix} 49.9 b & 149.7 c & 5 d \end{matrix} \\ \begin{matrix} \begin{matrix} 36.1 b & 108.3 c & 3 d \end{matrix} \\ \begin{matrix} 39.8 b & 79.6 c & 3 d \end{matrix} \end{matrix} \end{matrix} \end{matrix}) .

…”

Section: Optimal Selection Of Hydrogen Refueling Plan For Fuel Cell Bmentioning

confidence: 99%

“…The Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) method includes five steps 40 :…”

Section: Optimal Selection Of Hydrogen Refueling Planmentioning

confidence: 99%

“…It should be pointed out that the supply chain of hydrogen resource #1 and candidate NG station 6 in the present study, fortunately, has the lowest cost, GWP, and risk among all the plans. In the future study, if more than one possible plans of the hydrogen supply chain have advantages at different objectives or more than one hydrogen refueling area is needed, the method of TOPSIS can be employed to find the best possible hydrogen plan.…”

Section: Optimal Selection Of Hydrogen Refueling Plan For Fuel Cell Bmentioning

confidence: 99%

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Addition of hydrogen refueling for fuel cell bus fleet to existing natural gas stations: A case study in Wuhan, China

Long

Sui

et al. 2019

Int J Energy Res

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Summary Hydrogen refueling is an essential infrastructure for fuel cell vehicles, and currently, it appears to be a critical service needed to initiate the highly anticipated hydrogen economy in China. A practical selecting procedure of adding hydrogen refueling service to existing natural gas (NG) stations is proposed in this study. A case study in Wuhan, China, is established to assess the feasibility and future planning. The demand for hydrogen fuel and initial supply chain of hydrogen in Wuhan are estimated based on the deployment objective of fuel cell buses. The existing NG stations are evaluated based on 300 kg/day to determine whether they meet the hydrogen safety requirement using Google map or field investigation. The safety space requirement of the hydrogen refueling area on existing NG station is determined as 25.9 × 27.1 m2. The optimal hydrogen refueling plan for fuel cell buses is calculated with multi‐objective analysis in economic, environmental, and safety aspects from the view of the hydrogen refueling supply chain. It is shown that adding hydrogen refueling stations to existing NG stations is feasible in technology, economics, regulation, and operation considerations. This study provides guidelines for building the hydrogen infrastructure for fuel cell buses at their early stage of commercial operation.

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normalD = ([], \begin{matrix} 0 & 35.9 a & 35.9 b 107.7 c 5 d \\ 0 & 33.7 a & \begin{matrix} 33.7 b & 67.4 c & 3 d \end{matrix} \\ \begin{matrix} 0 \\ M \\ \begin{matrix} M \\ M \end{matrix} \end{matrix} & \begin{matrix} 1.9 a \\ 49.9 a \\ \begin{matrix} 36.1 a \\ 39.8 a \end{matrix} \end{matrix} & \begin{matrix} \begin{matrix} 1.9 b & 0 & 3 d \end{matrix} \\ \begin{matrix} 49.9 b & 149.7 c & 5 d \end{matrix} \\ \begin{matrix} \begin{matrix} 36.1 b & 108.3 c & 3 d \end{matrix} \\ \begin{matrix} 39.8 b & 79.6 c & 3 d \end{matrix} \end{matrix} \end{matrix} \end{matrix}) .

…”

Section: Optimal Selection Of Hydrogen Refueling Plan For Fuel Cell Bmentioning

confidence: 99%

“…The Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) method includes five steps 40 :…”

Section: Optimal Selection Of Hydrogen Refueling Planmentioning

confidence: 99%

Section: Optimal Selection Of Hydrogen Refueling Plan For Fuel Cell Bmentioning

confidence: 99%

See 1 more Smart Citation

Addition of hydrogen refueling for fuel cell bus fleet to existing natural gas stations: A case study in Wuhan, China

Long

Sui

et al. 2019

Int J Energy Res

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“…For example, Micale et al [26] combined ELECTRE and TOPSIS for solving the storage location assignment problem. Akgün et al [27] combined AHP-TOPSIS and GIS to solve an ammunition distribution network design problem. DEMATEL and ANP were combined for evaluating the performance of hospital supply chain [28].…”

Section: Literature Reviewmentioning

confidence: 99%

A Linguistic Hierarchy Model with Self-Confidence Preference Relations and Its Application in Co-Regulation of Food Safety in China

Fan

Zhang

Tang

2019

IJERPH

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Linguistic preference relations are widely used by decision makers to elicit their preferences over alternatives in the Group Decision Making (GDM) process. Recent studies have shown that self-confidence, as an important human psychological behavior, has an important influence on decision-making results. However, multiple self-confidence levels of decision makers are seldom considered in the linguistic preference relation. Meanwhile many real-word decision-making problems are analyzed in a hierarchical structure, in which a complicated problem can be divided into several easier comprehended sub-problems. Hence, this paper aims at designing a linguistic hierarchy model with self-confidence preference relation (LHM-SCPR) to discuss complex GDM problems in a hierarchical structure. In the SC-LPR, each element contains two components, the first one is the preference value between pairs of alternatives, and the second one that is defined on a linguistic term set represents decision maker’s self-confidence level associated to the first component. Meanwhile, a nonlinear programming model is proposed to derive individual preference vector from SC-LPR. Then, we apply LHM-SCPR in co-regulation of food safety to present the validity of this method, and find that improving the participation skills regarding co-regulation of food safety is the most pressing task. Finally, detailed comparative analysis and discussion are presented to verify the validity of the proposal.

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“…Definir a localização de uma instalação é uma tarefa que demanda uma análise detalhada de diversos fatores tomando como o regra base que, as empresas competem entre si para possuírem vantagens em comparação aos seus concorrentes, para melhor satisfazer seus clientes, conquistar novos e lhes proporcionar maiores lucros. É de fácil entendimento, que a localização ideal é aquela que minimiza os custos envolvidos na produção e negócio e a maximização do nível de serviços oferecidos a seus clientes [1] [2].…”

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Definição de localidade para instalação industrial com o apoio do Fuzzy – Processo Analítico Hierárquico

Souza

Corso²

2020

SCI

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É uma característica da indústria moderna, a busca constante por mais produtividade, agilidade, inovação e rapidez em seus processos. E para somar, a expressão Indústria 4.0, que engloba tecnologias para automação, troca de dados, internet das coisas, computação na nuvem e conceitos de sistemas ciber-físicos, busca facilitar a visão de fábricas inteligentes. Porém, quando ainda não há uma indústria instalada ou quando deseja-se ampliar as instalações, essa importante decisão deve ser tomada levando diversos fatores em consideração. Comumente executivos sentem dificuldade para apresentar justificativas técnicas quando possuem a tarefa de definição da localidade da instalação industrial. Este tipo de decisão, pode ser de suma importância para a saúde do empreendimento, e é essencial que se tenha a maior assertividade possível na escolha. A fim de modelar a incerteza da preferência humana, o presente artigo teve como objetivo apresentar a aplicação do método AHP combinado com a Lógica Fuzzy aplicado à um problema de definição de localidade para instalação industrial. O problema em questão conta com quatro possíveis localidades, e para cada, avalia-se seis diferentes critérios. As ferramentas apresentadas neste artigo demonstram-se eficientes e úteis do ponto de vista metodológico, uma vez que vários trabalhos já comprovaram a sua eficácia.

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Solving an ammunition distribution network design problem using multi-objective mathematical modeling, combined AHP-TOPSIS, and GIS

Cited by 41 publications

References 78 publications

Addition of hydrogen refueling for fuel cell bus fleet to existing natural gas stations: A case study in Wuhan, China

Addition of hydrogen refueling for fuel cell bus fleet to existing natural gas stations: A case study in Wuhan, China

A Linguistic Hierarchy Model with Self-Confidence Preference Relations and Its Application in Co-Regulation of Food Safety in China

Definição de localidade para instalação industrial com o apoio do Fuzzy – Processo Analítico Hierárquico

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