نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد،گروه مدیریت، واحد پرند، دانشگاه آزاد اسلامی، پرند، ایران

2 استادیار، گروه مدیریت، واحد پرند، دانشگاه آزاد اسلامی، پرند، ایران

10.22054/jims.2022.66537.2766

چکیده

انتخاب تأمین‌کنندگان یکی از مسائل مهم در حوزه‌ی مدیریت و بهینه‌سازی است که با اهداف مختلفی از قبیل کاهش هزینه، افزایش کیفیت تولید یا خدمات، کاهش ریسک مواجهه با کمبود و غیره مورد توجه پژوهشگران بوده است. در ادبیات موضوع مدلی برای در نظر گرفتن ریسک عدم تأمین در کنار اثر برند توسعه نیافته است. با الهام از همین حقیقت، این تحقیق مدل انتخاب تأمین‌کنندگان را با هدف بهینه‌سازی زمان تحویل و سود بصورت یک مدل برنامه‌ریزی ریاضی عدد صحیح مدلسازی خواهد نمود. برای حل مدل از داده‌های شرکت لوکاد استفاده شده و یک روش جدید بر مبنای الگوریتم جستجوی همسایگی توسعه یافت که در آن علاوه بر عملگرهای جستجو از یک رویکرد امتیازدهی برای هوشمندسازی انتخاب عملگرهای موجود استفاده شده است. بر اساس آزمون ویلکاکسون و نتایج محاسباتی مشخص گردیده است که جبهه‌ی پارتوی بدست آمده در مدل پیشنهادی، جواب‌های بدست آمده از مدل لوآن و همکاران (2019) را مغلوب می‌کند. تحلیل حساسیت انجام شده بر روی محدودیت بودجه مشخص نمود تغییرات بودجه بر روی سود نهایی تاثیر بیشتری نسبت به زمان تدارک دارد. همچنین شاخص‌های فاصله از ایده‌آل و تنوع جواب‌ها نیز شاخص‌های کمی بوده‌اند که در مقایسات بکارگیری شده‌اند.

کلیدواژه‌ها

عنوان مقاله [English]

Developing Combined Supplier Selection Model Based on non-Supply Risk and Effect of Brand on Demand

نویسندگان [English]

  • Ali Akbar Mohamadian 1
  • Masoud Simkhah 2

1 Master Student, Department of Management, Parand Branch, Islamic Azad University, Parand, Iran

2 Assistant Professor, Department of Management, Parand Branch, Islamic Azad University,Parand, Iran

چکیده [English]

Supplier selection is one of the most important problems in management and optimization area which aims at optimizing cost of supply, quality of products and services, risk of non-supply etc. In the literature, risk of non-supply and brand effect on demand is not considered in the models. Inspiring of this fact, the current research develops supplier selection integer model to take lead time and risk of non-supply into account. To solve the model, LOKAD benchmark database are employed and a new adaptive variable neighborhood search will be introduced according to a scoring strategy to deal with complexity of the model and achieve optimal or appropriate near optimal solutions. According to Wilcoxon test, the obtained pareto solutions outperforms Luan et al. (2019) results. Sensitivity analysis of the solutions on budget reveals that the final profit is more sensitive comparing to lead time. In addition, distance from ideal and diversity measures used as quantitative measures to compare the results.

کلیدواژه‌ها [English]

  • Supplier selection
  • multi-objective decision making
  • Variable neighborhood search algorithm
  • Effect of brand on demand
  • Risk of non-supply
Bhattacharya, A., Sarkar, B., & Mukherjee, S. K. (2005). Integrating AHP with QFD for robot selection under requirement perspective. International journal of production research43(17), 3671-3685.
Bhattacharya, A., Geraghty, J., & Young, P. (2019). Supplier selection paradigm: An integrated hierarchical QFD methodology under multiple-criteria environment. Applied Soft Computing10(4), 1013-1027.
Weber, C. A., Current, J. R., & Benton, W. C. (2019). Vendor selection criteria and methods. European journal of operational research50(1), 2-18.
Kahraman, C., Ruan, D., & Doǧan, I. (2003). Fuzzy group decision-making for facility location selection. Information sciences157, 135-153.
Igarashi, M., de Boer, L., & Fet, A. M. (2013). What is required for greener supplier selection? A literature review and conceptual model development. Journal of Purchasing and Supply Management, 19(4), 247-263.
Kanagaraj, G., Ponnambalam, S. G., & Jawahar, N. (2016). Reliability-based total cost of ownership approach for supplier selection using cuckoo-inspired hybrid algorithm. The International Journal of Advanced Manufacturing Technology84(5-8), 801-816.
Chai, J., Liu, J. N., & Ngai, E. W. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert systems with applications40(10), 3872-3885.
Mutingi, M., & Mbohwa, C. (2017). Modeling Supplier Selection Using Multi-Criterion Fuzzy Grouping Genetic Algorithm. Grouping Genetic Algorithms, 11(3), 213-228
Paydar, M., & Saidi-Mehrabad, M. (2017). A hybrid genetic algorithm for dynamic virtual cellular manufacturing with supplier selection, International Journal of Advanced Manufacturing Technology, 19(4), 247-263.
Tsai, Y. L., Yang, Y. J., & Lin, C. H. (2010). A dynamic decision approach for supplier selection using ant colony system. Expert systems with applications, 37(12), 8313-8321.
Abdollahzadeh, H., & Atashgar, K. (2017). Optimal design of a multi-state system with uncertainty in supplier selection. Computers & Industrial Engineering, 105(1), 411-424.
Fashoto, S. G., Akinnuwesi, B., Owolabi, O., & Adelekan, D. (2016). Decision support model for supplier selection in healthcare service delivery using analytical hierarchy process and artificial neural network. African journal of business Management, 10(9), 209-232.
Büyüközkan, G., & Göçer, F. (2017). Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Applied Soft Computing, 52(3), 1222-1238.
Wang, Y. C., & Chen, T. (2021). A Bi-objective AHP-MINLP-GA approach for Flexible Alternative Supplier Selection amid the COVID-19 pandemic. Soft Computing Letters, 3(1), 100016.
Sarkar, S., Lakha, V., Ansari, I., & Maiti, J. (2017). Supplier selection in uncertain environment: a fuzzy MCDM approach. In Proceedings of the First International Conference on Intelligent Computing and Communication, pp. 257-266.
Tavana, M., Shaabani, A., Di Caprio, D., & Bonyani, A. (2021). An integrated group fuzzy best-worst method and combined compromise solution with Bonferroni functions for supplier selection in reverse supply chains. Cleaner Logistics and Supply Chain, 2(2), 100-109.
Nazari-Shirkouhi, S., Shakouri, H., Javadi, B., & Keramati, A. (2013). Supplier selection and order allocation problem using a two-phase fuzzy multi-objective linear programming. Applied Mathematical Modelling, 37(22), 9308-9323.
Fallahpour, A., Olugu, E. U., Musa, S. N., Khezrimotlagh, D., & Wong, K. Y. (2016). An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach. Neural Computing and Applications, 27(3), 707-725.
Pramanik, D., Haldar, A., Mondal, S. C., Naskar, S. K., & Ray, A. (2017). Resilient supplier selection using AHP-TOPSIS-QFD under a fuzzy environment. International Journal of Management Science and Engineering Management, 12(1), 45-54.
Kilic, H. S., & Yalcin, A. S. (2020). Modified two-phase fuzzy goal programming integrated with IF-TOPSIS for green supplier selection. Applied Soft Computing, 93, 106371.
Lima-Junior, F. R., & Carpinetti, L. C. R. (2016). Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management. International Journal of Production Economics, 174, 128-141.
Azadi, M., Jafarian, M., Saen, R. F., & Mirhedayatian, S. M. (2015). A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context. Computers & Operations Research, 54, 274-285.
Karsak, E. E., & Dursun, M. (2014). An integrated supplier selection methodology incorporating QFD and DEA with imprecise data. Expert Systems with Applications, 41(16), 6995-7004.
Dotoli, M., Epicoco, N., Falagario, M., & Sciancalepore, F. (2016). A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty. International Transactions in Operational Research, 23(4), 725-748.
Lin, C. T., Chen, C. B., & Ting, Y. C. (2011). An ERP model for supplier selection in electronics industry. Expert Systems with Applications, 38(3), 1760-1765.
Toloo, M. (2016). A cost efficiency approach for strategic vendor selection problem under certain input prices assumption. Measurement, 85, 175-183.
Hfeda, M., Marchand, F., & Dao, T. M. (2017). Optimization of Milk-Run Delivery Issue in Lean Supply Chain Management by Genetic Algorithm and Hybridization of Genetic Algorithm with Ant Colony Optimization: An Automobile Industry Case Study. Journal of Management & Engineering Integration, 10(2), 90-99.
Guo, X., Zhou, L., Guo, Q., & Rouyendegh, B. D. (2021). An optimal size selection of hybrid renewable energy system based on Fractional-Order Neural Network Algorithm: A case study. Energy Reports, 7, 7261-7272.
Karabati, S., Tan, B., & Öztürk, Ö. C. (2009). A method for estimating stock-out-based substitution rates by using point-of-sale data. IIE Transactions, 41(5), 408-420.
Luan, J., Yao, Z., Zhao, F., & Song, X. (2019). A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization. Mathematics and Computers in Simulation, 156, 294-309.
Memari, A., Dargi, A., Jokar, M. R. A., Ahmad, R., & Rahim, A. R. A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, 50, 9-24.
Li, J., Fang, H., & Song, W. (2019). Sustainable supplier selection based on SSCM practices: A rough cloud TOPSIS approach. Journal of Cleaner Production, 222(1), 606-621.
Haeri, S. A. S., & Rezaei, J. (2019). A grey-based green supplier selection model for uncertain environments. Journal of Cleaner Production, 221(2), 768-784.
Li, H., Wang, F., Zhang, C., Wang, L., An, X., & Dong, G. (2021). Sustainable supplier selection for water environment treatment public-private partnership projects, Journal of Cleaner Production, 324(1), 210-225.
Liberopoulos, G., Deligiannis, M. (2021). Optimal supplier inventory control policies when buyer purchase incidence is driven by past service, European Journal of Operational Research, 307(1), 189-207.