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Tytuł artykułu

Fuzzy Multi-Objective Optimization to Evaluate the Performance of Suppliers Taking Into Account the Visibility and Supply Chain Risk

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Adequate and desirable connections between suppliers and customers necessitate an appropriate flow of information. Therefore, a promising and proper data collaboration in the supply chain is of tremendous significance. Thus, the study's main objective is to provide multiple objective programming models under uncertain conditions to assess the performance of suppliers. To meet that aim, a case study for the reliability assessment of the presented model is carried out. That section is associated with supply chain visibility (SCV). Likewise, the likelihood of unpredicted and undesirable incidents involving supply chain risk (SCR) is taken into consideration. The intimate relation between visibility and risk of the supply chain is deemed efficient for the performance of the supply chain. Incoherence in maximization and minimization of SCR and SCV and other factors, including costs, capacity, or demand, necessitates multiple objective programming models to assess suppliers' performance to accomplish the before-mentioned aims. The study’s results indicate the high reliability of the proposed model. Besides, the numeral results reveal that decision-makers in selecting suppliers mainly decrease SCR and then attempt to enhance SCV. In conclusion, the provided model in the study can be a desirable model for analyzing and estimating supplier performance with SCR and SCV simultaneously.
Rocznik
Strony
385--397
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • UNHAN (University of Defense), Indonesia
  • University of Prishtina “Hasan Prishtina”, Economic Faculty, Kosova
  • College of Business Administration, Ajman University, United Arab Emirates
  • Public Health Department, Faculty of Health Science, University of Pembangunan Nasional Veteran Jakarta, Indonesia
  • English department, The Islamic University, Najaf, Iraq
autor
  • College of Dentistry, Al-Ayen University, Thi-Qar, Iraq
  • Human Resource Management, Faculty of Management, Alfaisal University, Jeddah, Saudi Arabia
autor
  • Department of Mathematics, Panimalar Institute of Technology Poonamallee Chennai, Chennai Tamilnadu, India
  • Department of Pharmaceutical Chemistry, College of Pharmacy, University of Mosul, Mosul-41001, Iraq
Bibliografia
  • [1] Barney J., Firm resources and sustained competitive advantage, Journal of Management, 17, 1, 1991, 99-120.
  • [2] Heikkilä J., From supply to demand chain management: efficiency and customer satisfaction. Journal of Operations Management, 20, 6, 2002, 747-767.
  • [3] Goli A., Khademi-Zare H., Tavakkoli-Moghaddam R., Sadeghieh A., Sasanian M., Malekalipour Kordestanizadeh R., An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study. Network: Computation in Neural Systems, 32, 1, 2021, 1-35.
  • [4] Giri B. K., Roy S. K., Neutrosophic multi-objective green four-dimensional fixed-charge transportation problem, International Journal of Machine Learning and Cybernetics, 13, 10, 2022, 3089-3112.
  • [5] Sanders D. R., Manfredo M. R., The role of value‐at‐risk in purchasing: an application to the foodservice industry, Journal of Supply Chain Management, 38, 1, 2002, 38-45.
  • [6] Maity G., Yu V. F., Roy S. K., Optimum Intervention in Transportation Networks Using Multimodal System under Fuzzy Stochastic Environment, Journal of Advanced Transportation, 2022.
  • [7] Zsidisin G. A., Managerial perceptions of supply risk, Journal of Supply Chain Management, 39, 4, 2003, 14-26.
  • [8] Childerhouse P., Disney S. M., Towill D. R., On the impact of order volatility in the European automotive sector, International Journal of Production Economics, 114, 1, 2008, 2-13.
  • [9] Goh M., Lim J. Y., Meng F., A stochastic model for risk management in global supply chain networks, European Journal of Operational Research, 182, 1, 2007, 164-173.
  • [10] Mondal A., Roy S. K., Midya S., Intuitionistic fuzzy sustainable multi-objective multiitem multi-choice step fixed-charge solid transportation problem. Journal of Ambient Intelligence and Humanized Computing, 2021, 1-25.
  • [11] Mondal A., Roy S. K., Multi-objective sustainable opened-and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation, Computers & Industrial Engineering, 159, 2021, 107453.
  • [12] Petersen K. J., Ragatz G. L., Monczka R. M., An examination of collaborative planning effectiveness and supply chain performance, Journal of Supply Chain Management, 41, 2, 2005, 14-25.
  • [13] Bartlett P. A., Julien D. M., Baines T. S., Improving supply chain performance through improved visibility, The International Journal of Logistics Management, 18, 2, 2007, 294-313.
  • [14] Enslow B., Global Supply Chain Benchmark Report: Industry Priorities for Visibility. B2B Collaboration, Trade Compliance and Risk Management, Aberdeen Group, 2006.
  • [15] Delen D., Hardgrave B. C., Sharda R., RFID for better supply‐chain management through enhanced information visibility. Production And Operations Management, 16, 5, 2007, 613-624.
  • [16] Zhou W., RFID and item-level information visibility, European Journal of Operational Research, 198, 1, 2009, 252-258.
  • [17] Ouyang Y., The effect of information sharing on supply chain stability and the bullwhip effect, European Journal of Operational Research, 182, 3, 2007, 1107-1121.
  • [18] Goh M., De Souza R., Zhang A. N., He W., Tan P. S., Supply chain visibility: a decision making perspective. In 2009 4th IEEE Conference on industrial electronics and applications (pp. 2546-2551). IEEE. 2009
  • [19] Amid A., Ghodsypour S. H., O’Brien C., Fuzzy multiobjective linear model for supplier selection in a supply chain. International Journal of production economics, 104, 2, 2006, 394-407.
  • [20] Turgut O., Dalkiran E., Murat A. E., An exact parallel objective space decomposition algorithm for solving multi-objective integer programming problems, Journal of Global Optimization, 75, 1, 2019, 35-62.
  • [21] Xue Y., Li Y.F., Multi-objective integer programming approaches for solving the multi-criteria test-suite minimization problem: Towards sound and complete solutions of a particular search-based software-engineering problem, ACM Transactions on Software Engineering and Methodology (TOSEM), 29, 3, 2020, 1-50.
  • [22] Manuj I., Mentzer J. T., Global supply chain risk management, Journal of Business Logistics, 29 1, 2008, 133-155.
  • [23] Goli A., Tirkolaee E. B., Malmir B., Bian G. B., Sangaiah A. K., A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand, Computing, 101, 6, 2019, 499-529.
  • [24] Rubashkina Y., Galeotti M., Verdolini E., Environmental regulation and competitiveness: empirical evidence on the Porter Hypothesis from European manufacturing sectors, Energy Policy, 83, 2015, 288-300.
  • [25] Gajdzik B., Key Directions in Changes from Steelworks 3.0 to Steelworks 4.0 with analysis of selected technologies of digitalizing the steel industry in Poland. Management Systems in Production Engineering, 30, 1, 2022, 46-53.
  • [26] Goli A., Tirkolaee E. B., Aydın N. S., Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors, IEEE Transactions on Fuzzy Systems, 29, 12, 2021, 3686-3695.
  • [27] Mondal A., Roy S. K., Application of Choquet integral in interval type‐2 Pythagorean fuzzy sustainable supply chain management under risk, International Journal of Intelligent Systems, 37, 1, 2022, 217-263.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c078f4de-a0c3-4406-8818-4fa705147dd9
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