1
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Dhumras H, Bajaj RK, Shukla V. On utilizing modified TOPSIS with R-norm q-rung picture fuzzy information measure green supplier selection. Int J Inf Technol 2023; 15:1-7. [PMID: 37360318 PMCID: PMC10257187 DOI: 10.1007/s41870-023-01304-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 05/23/2023] [Indexed: 06/28/2023]
Abstract
The present communication introduces a new discriminant measure coined as R-norm q-rung picture fuzzy discriminant information measure which is more generalized in nature and has the capability to handle more flexibility inherited in the inexact information. The notion of q-rung picture fuzzy set (q-RPFS) has an integrated advantage of picture fuzzy set and q-rung orthopair fuzzy set with flexibility of qth level relations. The proposed parametric measure is then applied in the conventional "technique for order preference by similarity to the ideal solution (TOPSIS) method" for solving a green supplier selection problem. The numerical illustration to exhibit the proposed methodology for the green supplier selection problem has been presented in an empirical form to establish the consistency of the model. Also, the advantageous features of the proposed scheme in the setup of impreciseness have been discussed.
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Affiliation(s)
- Himanshu Dhumras
- Department of Mathematics, Jaypee University of Information Technology, Waknaghat, Solan, 173234 HP India
| | - Rakesh K. Bajaj
- Department of Mathematics, Jaypee University of Information Technology, Waknaghat, Solan, 173234 HP India
| | - Varun Shukla
- Department of ECE, PSIT, Kanpur, Street, Kanpur, 209305 UP India
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2
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Tavakoli M, Tajally A, Ghanavati-Nejad M, Jolai F. A Markovian-based fuzzy decision-making approach for the customer-based sustainable-resilient supplier selection problem. Soft comput 2023; 27:1-32. [PMID: 37362282 PMCID: PMC10195666 DOI: 10.1007/s00500-023-08380-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/28/2023]
Abstract
The supplier selection problem is one of the most important issues in supply chain management. So, many papers have investigated the mentioned problem. However, the related literature shows that researchers had less attention to the sustainability and resilience aspects based on the customer preferences in supplier selection problem. To cover this gap, this research tries to investigate the customer-based sustainable-resilient supplier selection problem. In this way, a Markovian-based fuzzy decision-making method is proposed. At the outset, the customer preferences are evaluated using a combination of the quality function deployment and the Markov transition matrix. Then, by combining the transition matrix and the fuzzy best-worst method, the weights of the indicators are calculated. Finally, the decision matrix is formed and the performance of suppliers is measured based on the multiplication of the decision matrix and vector of sub-criteria weights. Regarding the recent pandemic disruption (COVID-19), the importance of online marketplaces is highlighted more than the past. Hence, this study considers an online marketplace as a case study. Results show that in a pandemic situation, the preferences of customers when they cannot go shopping normally will change after a while. Based on the Markov steady state, these changes are from the priority of price, availability, and performance in initial time to serviceability, reliability, and availability in the future. Finally, based on the FBWM results, from the customer point of view, the top five sub-criteria for sustainable-resilient supplier selection include cost, quality, delivery, responsiveness, and service. So, based on these priorities, the case study potential suppliers are prioritized, respectively.
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Affiliation(s)
- Mahdieh Tavakoli
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Amirreza Tajally
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mohssen Ghanavati-Nejad
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Fariborz Jolai
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
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3
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Wu C, Zou H, Barnes D. A supply risk perspective integrated sustainable supplier selection model in the intuitionistic fuzzy environment. Soft comput 2023; 27:1-19. [PMID: 37362292 PMCID: PMC10156080 DOI: 10.1007/s00500-023-08336-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/24/2023] [Indexed: 06/28/2023]
Abstract
With the recent focus on supply risk management in sustainable supply chains, it is more important than ever to evaluate and select the right sustainable suppliers from a supply risk perspective. However, few existing studies consider supply risks from the perspective of all three triple-bottom-line dimensions at the same time. To bridge this research gap, this research constructs a supply risk perspective integrated sustainable supplier selection model in the intuitionistic fuzzy environment. First of all, the weights of decision-makers in the decision-making group are obtained by intuitionistic fuzzy set. Secondly, after obtaining the aggregated intuitionistic fuzzy decision matrix considering the weight of decision-makers, the fuzzy entropy weight method is used to calculate criteria weight, objectively. Then, an improved failure mode and effects analysis is used to undertake risk assessments and to identify high-risk suppliers. Last but not least, the extended alternative queuing method is adopted to rank the eligible sustainable suppliers in the intuitionistic fuzzy environment. The proposed model not only reduces the uncertainty of decision-making in sustainable supplier selection, but also enables focal companies to reduce supply risk in their sustainable supplier selection practices and prevent the failure modes that relate to supply risk. The practicality and effectiveness of the proposed model are verified through an empirical illustration in a leading electrical appliance manufacturer in China.
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Affiliation(s)
- Chong Wu
- School of Management, Xiamen University, Xiamen, 361005 People’s Republic of China
| | - Haohui Zou
- School of Management, Xiamen University, Xiamen, 361005 People’s Republic of China
| | - David Barnes
- Westminster Business School, University of Westminster, London, NW1 5LS UK
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4
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Hosseini Dolatabad A, Heidary Dahooie J, Antucheviciene J, Azari M, Razavi Hajiagha SH. Supplier selection in the industry 4.0 era by using a fuzzy cognitive map and hesitant fuzzy linguistic VIKOR methodology. Environ Sci Pollut Res Int 2023; 30:52923-52942. [PMID: 36843168 DOI: 10.1007/s11356-023-26004-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Organizations will be increasingly concerned about maintaining their positions in today's changing world, the high-tech era, and the emergence of innovative technologies because of the industrial revolutions. Everyone has come to believe that to survive and continue their constructive roles, they must achieve competitive advantages by working based on the trends. It is undeniable that the introduction of Industry 4.0 has had a significant impact on enterprises, organizations, and, of course, supply chains. In the meantime, selecting a supplier is one of the main strategic decisions of the organization because choosing the right supplier leads to increasing profitability, improving market competition, better accountability, enhancing product quality, and reducing costs. While the issue of supplier evaluation has been one of the interesting topics for researchers in recent decades, its development in the fourth supply chain generation needs further consideration. In this regard, current technologies in the fourth-generation industrial revolution, methods, and criteria used in previous studies based on industry 4.0 and before that are reviewed separately. By reviewing previous articles and experts' opinions, thirteen sub-criteria considering industry 4.0 have been identified for selecting suppliers in three categories, economic, environmental, and social. The weight of each criterion has been determined using a set of fuzzy cognitive maps (FCMs) and considering the centrality of criteria in the concept of communication networks. To prioritize the suppliers, the hesitant fuzzy linguistic term sets (HFLTS) VIKOR method has been used in hesitant fuzzy linguistic terms. Finally, a case study is introduced to illustrate the effectiveness and usefulness of our integrated methodology and prioritize its four suppliers.
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Affiliation(s)
- Asana Hosseini Dolatabad
- Faculty of Management, University of Tehran, Jalal Al-E-Ahmad Ave., Nasr Bridge, Tehran, 14155-6311, Iran
| | - Jalil Heidary Dahooie
- Faculty of Management, University of Tehran, Jalal Al-E-Ahmad Ave., Nasr Bridge, Tehran, 14155-6311, Iran
| | - Jurgita Antucheviciene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Al. 11, 10223, Vilnius, Lithuania.
| | - Mostafa Azari
- Faculty of Management, University of Tehran, Jalal Al-E-Ahmad Ave., Nasr Bridge, Tehran, 14155-6311, Iran
| | - Seyed Hossein Razavi Hajiagha
- Department of Management, Faculty of Management and Finance, Khatam University, Hakim Azam St., North Shiraz St., Mollasadra Ave., Tehran, 19395-3486, Iran
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5
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Cheng C, Wang X, Ren X. Selection of outsourcing logistics providers in the context of low-carbon strategies. Environ Sci Pollut Res Int 2023; 30:18701-18717. [PMID: 36219288 DOI: 10.1007/s11356-022-23468-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 10/01/2022] [Indexed: 06/16/2023]
Abstract
As more attention is given to climate change and sustainable development in China, enterprises start to care about carbon emissions related to their supply chains. One important issue faced by an enterprise is to select a logistics provider who can provide a high-quality service with low carbon levels. To solve this issue, this paper proposes a selection criterion from the perspective of low carbon levels based on previous studies. The selection criterion consists of comprehensive strength, financial status, market competitiveness, development potential and low carbon level. Next, this paper applies a combined method of information entropy and grey correlation vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) model to evaluate the proposed selection criterion. Subsequently, this paper took company S and its four logistics providers as a case study to check the applicability of our proposed selection method and to illustrate how to use it. Sensitivity analysis and comparative analysis are also conducted. Related managerial insights are also proposed based on the evaluation results. One finding of this paper is to establish a decision-making framework to evaluate logistics suppliers under the new background of 'emission peak and carbon neutrality'.
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Affiliation(s)
- Cheng Cheng
- School of Management Science and Engineering, Shanxi University of Finance and Economics, No. 140 Wucheng Road, Xiaodian District, Taiyuan, 030006, Shanxi Province, China
| | - Xiaomin Wang
- School of Management Science and Engineering, Shanxi University of Finance and Economics, No. 140 Wucheng Road, Xiaodian District, Taiyuan, 030006, Shanxi Province, China
| | - Xiaohang Ren
- Business School, Central South University, No. 932, Lu Shan Nan Lu, Yue Lu District, Changsha, 410083, Hunan Province, China.
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6
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Wei Q, Zhou C. A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China. Environ Sci Pollut Res Int 2023; 30:10540-10559. [PMID: 36083365 PMCID: PMC9461430 DOI: 10.1007/s11356-022-22783-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/24/2022] [Indexed: 06/15/2023]
Abstract
Electric vehicle deployment shows promising potentials in promoting cleaner energy utilization and reducing carbon emission. Due to increasing carbon neutral pressure and market competition from transportation sector, government agencies and public bodies (GAPBs) have emphasized the significance of electric vehicle adoption through supplier selection. Consequently, GAPBs must consider a reasonable criteria system and a comprehensive supplier selection framework and rationally select the electric vehicle supplier that matches their practical needs in terms of economic, social, environmental, and technical factors. This paper provides insights into electric vehicle supplier selection (EVSS) from the perspective of GAPBs using an integrated multi-criteria decision-making (MCDM) framework based on best-worst method (BWM) and fuzzy ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Initially, 14 critical factors from economic, social, environmental, and technical dimensions are identified as the criteria by literature review and experts' opinions. Then, a comprehensive decision framework using the integrated MCDM approach is proposed. To validate the applicability and feasibility of the proposed framework, a case study is launched and analyzed. It emerges that bad environmental record, cost, quality, service, and environmental initiatives are the most important criteria in EVSS for GAPBs with the weight values of 0.1995, 0.1172, 0.1219, 0.0708, and 0.2553. The comparative analysis and the sensitivity analysis are performed for verifying the reliability of the proposed framework. The work helps to understand the electric vehicle supplier selection criteria and makes methodological decision-making support for GAPBs.
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Affiliation(s)
- Qiushuang Wei
- Emergency Management Institute of Guangxi Normal University, School of Politics and Public Administration, Guangxi Normal University, Guilin, 541004 China
- Guangxi Key Laboratory of Landscape Resources Conservation and Sustainable Utilization in Lijiang River Basin, Guangxi Normal University, Guilin, 541004 China
| | - Chao Zhou
- Emergency Management Institute of Guangxi Normal University, School of Politics and Public Administration, Guangxi Normal University, Guilin, 541004 China
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7
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Rafigh P, Akbari AA, Bidhandi HM, Kashan AH. A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models. J Comb Optim 2022; 44:1387-1432. [PMID: 36062162 PMCID: PMC9418663 DOI: 10.1007/s10878-022-00891-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
This study proposes a framework for the main parties of a sustainable supply chain network considering lot-sizing impact with quantity discounts under disruption risk among the first studies. The proposed problem differs from most studies considering supplier selection and order allocation in this area. First, regarding the concept of the triple bottom line, total cost, environmental emissions, and job opportunities are considered to cover the criteria of sustainability. Second, the application of this supply chain network is transformer production. Third, applying an economic order quantity model lets our model have a smart inventory plan to control the uncertainties. Most significantly, we present both centralized and decentralized optimization models to cope with the considered problem. The proposed centralized model focuses on pricing and inventory decisions of a supply chain network with a focus on supplier selection and order allocation parts. This model is formulated by a scenario-based stochastic mixed-integer non-linear programming approach. Our second model focuses on the competition of suppliers based on the price of products with regard to sustainability. In this regard, a Stackelberg game model is developed. Based on this comparison, we can see that the sum of the costs for both levels is lower than the cost without the bi-level approach. However, the computational time for the bi-level approach is more than for the centralized model. This means that the proposed optimization model can better solve our problem to achieve a better solution than the centralized optimization model. However, obtaining this better answer also requires more processing time. To address both optimization models, a hybrid bio-inspired metaheuristic as the hybrid of imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) is utilized. The proposed algorithm is compared with its individuals. All employed optimizers have been tuned by the Taguchi method and validated by an exact solver in small sizes. Numerical results show that striking similarities are observed between the results of the algorithms, but the standard deviations of PSO and ICA-PSO show better behavior. Furthermore, while PSO consumes less time among the metaheuristics, the proposed hybrid metaheuristic named ICA-PSO shows more time computations in all small instances. Finally, the provided results confirm the efficiency and the performance of the proposed framework and the proposed hybrid metaheuristic algorithm.
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Affiliation(s)
- Parisa Rafigh
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Ali Akbar Akbari
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Hadi Mohammadi Bidhandi
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Ali Husseinzadeh Kashan
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
- Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
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8
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Mahmoudi A, Javed SA. Probabilistic Approach to Multi-Stage Supplier Evaluation: Confidence Level Measurement in Ordinal Priority Approach. Group Decis Negot 2022; 31:1051-1096. [PMID: 36042813 PMCID: PMC9409630 DOI: 10.1007/s10726-022-09790-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
A popular framework of the supplier selection process is usually characterized by problem definition, criteria formulation, supplier screening, and supplier selection. The literature review suggested limitations of this framework as it ignores the screening of criteria (beyond criteria weighing) and evaluators (buyers) and its inability to guide the supplier selection problems where a measure of confidence or trust is needed to confirm the reliability of the selected supplier. While extending de Boer's influential supplier selection framework, the current study argues that the supplier selection problem is not merely about ranking suppliers based on given criteria; instead, it involves evaluating criteria and evaluators as well. Guided by the theory of statistics and the Ordinal Priority Approach (OPA), the study pioneers a probabilistic approach of supplier evaluation and selection under incomplete information using a novel Confidence Level measure. The study suggests, the probability that a supplier shortlisted for selection is actually the optimum choice or not can be explained through a probability distribution, called W-distribution, therefore, confidently preventing the decision-makers from selecting the sub-optimum suppliers. The study presents a novel contribution to the theory of multiple-attribute decision-making through the OPA. The proposed approach can help build intelligent decision support systems to aid managers while providing them with early warning tools and suggestions to improve confidence in their selection.
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Affiliation(s)
- Amin Mahmoudi
- Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, 210096 China
| | - Saad Ahmed Javed
- School of Business, Nanjing University of Information Science and Technology, Nanjing, 210044 China
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9
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Afrasiabi A, Tavana M, Di Caprio D. An extended hybrid fuzzy multi-criteria decision model for sustainable and resilient supplier selection. Environ Sci Pollut Res Int 2022; 29:37291-37314. [PMID: 35050472 PMCID: PMC8771628 DOI: 10.1007/s11356-021-17851-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 11/25/2021] [Indexed: 06/14/2023]
Abstract
The formalization and solution of supplier selection problems (SSPs) based on sustainable (economic, environmental, and social) indicators have become a fundamental tool to perform a strategic analysis of the whole supply chain process and maximize the competitive advantage of firms. Over the last decade, sustainability issues have been often considered in combination with resilient indexes leading to the study of sustainable-resilient supplier selection problems (SRSSPs). The current research on sustainable development, particularly concerned with the strong impact that the recent COVID-19 pandemic has had on supply chains, has been paying increasing attention to the resilience concept and its role within SSPs. This study proposes a hybrid fuzzy multi-criteria decision making (MCDM) method to solve SRSSPs. The fuzzy best-worst method is used first to determine the importance weights of the selection criteria. A combined grey relational analysis and the technique for order of preference by similarity to ideal solution (TOPSIS) method is used next to evaluate the suppliers in a fuzzy environment. Triangular fuzzy numbers (TFNs) are used to express the weights of criteria and alternatives to account for the ambiguity and uncertainty inherent to subjective evaluations. However, the proposed method can be easily extended to other fuzzy settings depending on the uncertainty facing managers and decision-makers. A real-life application is presented to demonstrate the applicability and efficacy of the proposed model. Sixteen evaluation criteria are identified and classified as economic, environmental, social, or resilient. The results obtained through the case study show that "pollution control," "environmental management system," and "risk awareness" are the most influential criteria when studying SRSSPs related to the manufacturing industry. Finally, three different sensitivity analysis methods are applied to validate the robustness of the proposed framework, namely, changing the weights of the criteria, comparing the results with those of other common fuzzy MCDM methods, and changing the components of the principal decision matrix.
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Affiliation(s)
- Ahmadreza Afrasiabi
- Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
| | - Madjid Tavana
- Business Systems and Analytics Department, La Salle University, Philadelphia, PA USA
- Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, Paderborn, Germany
| | - Debora Di Caprio
- Department of Economics and Management, University of Trento, Trento, Italy
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10
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Pamucar D, Torkayesh AE, Biswas S. Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach. Ann Oper Res 2022; 328:1-43. [PMID: 35039705 PMCID: PMC8754374 DOI: 10.1007/s10479-022-04529-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/03/2022] [Indexed: 05/07/2023]
Abstract
Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308.
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Affiliation(s)
- Dragan Pamucar
- Department of Logistics, Military Academy, University of Defence in Belgrade, Belgrade, 11000 Serbia
| | - Ali Ebadi Torkayesh
- School of Business and Economics, RWTH Aachen University, 52072 Aachen, Germany
| | - Sanjib Biswas
- Decision Sciences and Operations Management Area, Calcutta Business School, Bishnupur, South 24 Parganas, West Bengal 743503 India
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11
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Abstract
Due to apparent flexibility of Intuitionistic Fuzzy Set (IFS) concepts in dealing with the imprecision or uncertainty, these are proving to be quite useful in many application areas for a more human consistent reasoning under imperfectly defined facts and imprecise knowledge. In this paper, we apply notions of entropy and intuitionistic fuzzy sets to present a new fuzzy decision-making approach called intuitionistic fuzzy entropy measure for selection and ranking the suppliers with respect to the attributes. An entropy-based model is formulated and applied to a real case study aiming to examine the rankings of suppliers. Furthermore, the weights for each alternative, with respect to the criteria, are calculated using intuitionistic fuzzy entropy measure. The supplier with the highest weight is selected as the best alternative. This proposed model helps the decision-makers in better understanding of the weight of each criterion without relying on the mere expertise.
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Affiliation(s)
- Mohamadtaghi Rahimi
- Department of Mathematics and Statistics, University of Northern British Columbia, Prince George, BC Canada
| | - Pranesh Kumar
- Department of Mathematics and Statistics, University of Northern British Columbia, Prince George, BC Canada
| | - Behzad Moomivand
- Department of Management, Qom Branch, Islamic Azad University, Qom, Iran
| | - Gholamhosein Yari
- Department of Mathematics, Iran University of Science and Technology, Tehran, Iran
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12
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Toffano F, Garraffa M, Lin Y, Prestwich S, Simonis H, Wilson N. A multi-objective supplier selection framework based on user-preferences. Ann Oper Res 2021; 308:609-640. [PMID: 35035013 PMCID: PMC8724141 DOI: 10.1007/s10479-021-04251-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/30/2021] [Indexed: 06/14/2023]
Abstract
This paper introduces an interactive framework to guide decision-makers in a multi-criteria supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit the decision-maker's preferences among the criteria by processing pre-collected data from different stakeholders. We propose a different approach where the preferences are elicited through an active learning loop. At each step, the framework optimally solves a combinatorial problem multiple times with different weights assigned to the objectives. Afterwards, a pair of solutions among those computed is selected using a particular query selection strategy, and the decision-maker expresses a preference between them. These two steps are repeated until a specific stopping criterion is satisfied. We also introduce two novel fast query selection strategies, and we compare them with a myopically optimal query selection strategy. Computational experiments on a large set of randomly generated instances are used to examine the performance of our query selection strategies, showing a better computation time and similar performance in terms of the number of queries taken to achieve convergence. Our experimental results also show the usability of the framework for real-world problems with respect to the execution time and the number of loops needed to achieve convergence.
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Affiliation(s)
- Federico Toffano
- Insight Centre for Data Analytics, School of Computer Science and IT, University College Cork, Cork, Ireland
| | - Michele Garraffa
- United Technologies Research Centre, Cork, Ireland
- School of Computer Science and IT, University College Cork, Cork, Ireland
| | - Yiqing Lin
- United Technologies Research Centre, East Hartford, USA
| | - Steven Prestwich
- Insight Centre for Data Analytics, School of Computer Science and IT, University College Cork, Cork, Ireland
| | - Helmut Simonis
- Insight Centre for Data Analytics, School of Computer Science and IT, University College Cork, Cork, Ireland
| | - Nic Wilson
- Insight Centre for Data Analytics, School of Computer Science and IT, University College Cork, Cork, Ireland
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13
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Mumtaz U, Ali Y, Petrillo A, De Felice F. Identifying the critical factors of green supply chain management: Environmental benefits in Pakistan. Sci Total Environ 2018; 640-641:144-152. [PMID: 29859432 DOI: 10.1016/j.scitotenv.2018.05.231] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Revised: 05/18/2018] [Accepted: 05/19/2018] [Indexed: 06/08/2023]
Abstract
Pakistan is a developing country characterized by a growing industrialization, which is the major cause of environmental pollution in the country. To control the significant increase in pollution a green incentive has started, aiming to moderate the adverse effects of environmental pollution. Thus, Green Supply Chain Management (GSCM) plays an important role in influencing the total environment impact of any organizations. This study considers ten Pakistani industries that have implemented GSCM practices. The Decision-Making Trial and Evaluation Laboratory technique (DEMATEL) is used to find influential factors in selecting GSCM criteria. The results show that organizational involvement is the most important dimension useful to implement GSCM practices. In addition, commitment from senior managers, ISO 14000 certification of suppliers and recycle of waste heat are considered significant factors. The paper also signifies the casual relationship among the dimensions and the factors in the form of diagraphs. The main management implication of the paper is to help decision makers to focus on the critical dimensions/factors in order to implement the GSCM practices more effectively in Pakistan.
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Affiliation(s)
- Ubaidullah Mumtaz
- Faculty of Management Sciences, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Technology, Topi, Pakistan.
| | - Yousaf Ali
- Department of Management Science & Humanities, Ghulam Ishaq Khan Institute of Engineering Sciences & Technology Topi, Pakistan.
| | - Antonella Petrillo
- University of Naples "Parthenope", Department of Engineering, Napoli, Italy.
| | - Fabio De Felice
- University of Cassino and Southern Lazio, Department of Engineering, Cassino, Italy.
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Mehralian G, Rajabzadeh Gatari A, Morakabati M, Vatanpour H. Developing a suitable model for supplier selection based on supply chain risks: an empirical study from Iranian pharmaceutical companies. Iran J Pharm Res 2012; 11:209-19. [PMID: 24250442 PMCID: PMC3813095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The supply chain represents the critical link between the development of new product and the market in pharmaceutical industry. Over the years, improvements made in supply chain operations have focused largely on ways to reduce cost and gain efficiencies in scale. In addition, powerful regulatory and market forces have provided new incentives for pharmaceutical firms to basically rethink the way they produce and distribute products, and also to re-imagine the role of the supply chain in driving strategic growth, brand differentiation and economic value in the health continuum. The purpose of this paper is to formulate basic factors involved in risk analysis of pharmaceutical industry, and also determine the effective factors involved in suppliers selection and their priorities. This paper is based on the results of literature review, experts' opinion acquisition, statistical analysis and also using MADM models on data gathered from distributed questionnaires. The model consists of the following steps and components: first factors involved in to supply chain risks are determined. Based on them a framework is considered. According the result of statistical analysis and MADM models the risk factors are formulated. The paper determines the main components and influenceial factors involving in the supply chain risks. Results showed that delivery risk can make an important contribution to mitigate the risk of pharmaceutical industry.
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Affiliation(s)
- Gholamhossein Mehralian
- Department of Pharmacoeconomics and Pharmaceutical Management, School of Pharmacy, Shaheed Beheshti University of Medical Sciences, Tehran, Iran.
- Student Research committee, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | | | | | - Hossein Vatanpour
- Department of Pharmacoeconomics and Pharmaceutical Management, School of Pharmacy, Shaheed Beheshti University of Medical Sciences, Tehran, Iran.
- Pharmaceutical Sciences Research Center, Shaheed Beheshti University of Medical Sciences, Tehran, Iran.
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