Providing a Mathematical Model of Selecting a Production Supplier in the Supply Chain with the Approach of Bee Algorithm and Comparison with Genetic Algorithm
الموضوعات :
Tajaddin Eram
1
,
Nasser Fegh-hi Farahmand
2
,
Yaghoub Alavi Matin
3
1 - Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 - Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 - Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
الکلمات المفتاحية: Bee algorithm, Transportation Cost, mathematical model, Select a Supplier, Supply Chain, Cost of Purchase,
ملخص المقالة :
Selecting suitable suppliers and assigning orders to them, is one of the most important strategic supply chain management activities. Therefore, first, in this research, a mathematical model for choosing the supplier in the supply chain in the framework of the operational research methods was proposed. Linear programming model with consideration of purchase costs and transportation costs with the meta-heuristic bee algorithm approach using MATLAB software was solved and analyzed. Therefore, the research method is applied in terms of purpose, and in terms of method is a descriptive mathematical type that was implemented in the form of a library and field studies. Information gathering tools, such as documentation tools, interviews with experts and production managers were used in relation to production. According to the nature of the research, which is modeling and solving by the algorithm, to determine the sample size were selected by cluster sampling method and random sampling method. Then to validate the mathematical model, lingo and Winqsb software was used, the solution obtained by both software, which is the optimal answer and optimal objective function, indicates the validity of the mathematical model.
Alahyari, M., Pilevari, N. (2020). CO-Active Neuro- Fuzzy Inference System Application in Supply Chain Sustainability Assessment Based on Economic, Social, Environmental, and Governance Pillars. Journal of System Management (JSM). Volume 6, Issue 3, autumn 2020, Pages 265-287.
Amid, A., Ghodsypour, S. H., O´Brien, C., (2006). Fuzzy multiobjective linear model for supplier selection in a supply chain. International Journal of Production Economics, 104, 394-407.
Amiri, Maghsoud, Farhad Hadinejad and Shiva Malek Khoyan, (2017), "Evaluation and Prioritization of Suppliers with a Combined Entropy Approach, Modified Hierarchical and Primitive Analysis Process (Case Study: YouTube)", Journal of Operations Research in Its applications, fourteenth year, fourth issue (55 in a row), winter 96, p. 20.
Bai, C., Sarkis, J., (2010). Integrating sustainability into supplier selection with grey system and rough set methodologies. International Journal of Production Economics, 124, 252-264.
Croce.F.D, R.Tadei and G.Volta (2002) "A Genetic Algorithm for the job shop problem", Computer and operation Research , Vol. 22, No. 1, pp. 53-64
Dargi, A., Anjomdhoae, A., Rahiminezhad Golanshaki, M., Memari, A., Binti, M. Md. tap., (2014). Supplier selection: A fuzzy ANP approach. Journal of Procedia Computer Sience, 31, 691-700.
Eltayeb, K.; Zailani, T. & Ramayah, S. 2010. Green Supply Chain initiatives among certified companies in Malaysia and environmental sustainability: Investigating the outcomes, Resources, Conservation and Recycling, 55: 495-506.
Errtugrul Karsak, E., Dursun, M., (2015). An integrated fuzzy MCDM approach for supplier evaluation and selection. Journal of Computers and Industrial Engineering, 82, 82-93.
Farajpour, F., (2012), Report on the Study of the Role of RFID Technology in Supply Chain Management, The Impact of IT on Organizations and Management, Allameh Tabatabaei University, School of Management and Accounting.
Farsad, Ishaq and Farhad Hosseinpour, (2015), Modeling and solving the problem of scheduling workflow without non-displaced unemployment, International Conference on Management and Economics in the 21st Century, March 3, 2015.
Ghasemi Yaghin, Reza, Zeinab Hosseini and Maryam Ismaili, (2014), "Presenting a multi-objective optimization model for both inventory and pricing decisions in the case of possible procurement times (exponential and uniform) using genetic algorithm" Journal of Operations Research in its Applications, Eleventh Year, First Issue (40 consecutive), Spring 93, pp. 46-31.
Ghodsypour S.H., O`Brien C.; Total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint, Int. J. Production economics, Vol. 73, 2001.
Hamidi, H. (2015). An Approach to Selecting a Suitable Supplier. Journal of Journal of System Management (JSM). Volume 1, Issue 3 - Serial Number 3, summer 2015, Pages 57-66
Hejazi, Hassan, (2011), "Presenting a framework for analysis and selection of raw material suppliers in Isfahan Steel Company using a combined approach of hierarchical analysis process and ideal planning", Master Thesis in Industrial Management, Supervisor Dr. Mahsa Kandahari.
Holland.J.H, (1975) Adaption in natural and artificial system, The university of Michigan Press.
Honarvar, A., Soukhakian, M.A., Honarvar, A. (2015). Supply Chain Risk Assessment Using Analytic Network Processes Case Study: Zagros Petrochemical Company Located in South Pars Economic Zone. Journal of Journal of System Management (JSM). Volume 1, Issue 1 - Serial Number 1, winter 2015, Pages 65-89.
Iranban, S.J. (2019). The Effect of Supply Chain Integration on Operational Efficiency and Value Creation. Journal of System Management 2019, Issue 2, pp. 107-132.
Jafar Nejad, A. , Amouzad Mehdiji, H., (1396), Supply Chain Design and Control, Quantitative Approach, Mehraban Book Publishing Institute, First Edition, Page 283.
-Karaboga, D., 2005- An idea based on honey bee swarm for numerical optimization- Technical Report TR06, Engineering Faculty, Computer Engineering Department, 10p.
Kheirmand, M.; and min rahati, A. (2014), Comparison of the Efficiency of Artificial Bee Clustering Algorithm and Strong and Efficient Clustering of Artificial Bees in Numerical Optimization, The First National Conference on Industrial Mathematics, Tabriz, June 7, 2014.
Kurd, Baqer and Mohammad Javad Jamshidi, (2016), Supply Chain Management, Zahedan: Sistan and Baluchestan University, 2016, ISBN 9786001062551.
Lee, H.I.; Kang, H-Y.; Hsu, C-F. & Hung, H-C. 2009. A green supplier selection model for high-tech industry, Expert Systems with Application, 36: 7917-7927.
-Lima Junior, F. R., Cesor, L., Carpinetti, R., (2016). Combining SCOR model and fuzzy TOPSIS for supplier evaluation and management. International journal of Production Economics, 174, 128-141.
Metters R. (1997). Quantifying the Bullwhip Effect in supply chains. Journal of Operation Management, 15(2), 89–100.
Mohammad Nejad Chari, Fatemeh and Abdolhamid Safaei Ghadiklaei, (2016), "Identification and ranking of supplier selection criteria in large supply chain (Case study: Kaleh food and dairy industry)", Journal of Operations Research in its Applications, Year 13 , No. 4 (51 consecutive), Winter 95, pp. 120-103.
nezhadi, F., (2014), Modeling and solving the problem of selecting a potential multi-objective supplier in the green supply chain using multi-objective colonial competition algorithm, MSc., Engineering, Yazd University, Faculty of Engineering and Engineering.
Nizamuddin, Faqih and Ali Honarvar, (2004), Application of Genetic Algorithm in the Planning of Preventive Inspections, Tehran: Nasim Hayat Publications.
Omurca, S. I., (2013). An intelligent supplier evaluation, selection and development system. Journal of Applied soft computing, 13, 690-697.
Ponte, B., Sierra, E., Fuente, D. D. & Lozano, J. (2017). Exploring the interaction of inventory policies across the supply chain: An agent-based approach. Computers & Operation Research, 78, 335-348.
Rimienė, Kristina. 2011. “Supply Chain Agility Concept Evolution(1990-2010)”, Journal of Economics and Management, 890-905.
Rao, Purba (2004); Greening Production: a South-East Asian Experience, International Journal of Operations & Production Management, Vol. 24, No. 3, pp. 289-320.
Sadeghi Moghadam, Mohammad Reza, Mansour Momeni and Soroush Nalchigar, (2009), "Integrated Supply Chain Planning, Production and Distribution Using Genetic Algorithm", Journal of Industrial Management, Volume 1, Number 2, Spring and Summer 1988, from Pages 71 to 88.
S.A.Kazarlis, A.G.Bakirtzis , Petridis.V, (1992) “A Genetic Algorithm Solution to the Unit Commitment Problem”, IEEE Trans. Power System, Vol. 1, pp. 14-32.
-Shang, K-C.; Lu, C-H. & Li, S.2010. a taxonomy of green supply chain management capability among electronic-related manufacturing firms in Taiwan, Journal of Environmental Management, 91: 1218-1226
Talebi, Davood and Fatemeh Mulataifeh, (2011), "Approach of evaluation and selection of suppliers along the supply chain using a combined technique of fuzzy hierarchical analysis process and fuzzy multi-objective linear programming (Case study of Bahman Motor Center") Industrial Management No. 2- Summer 2011- pp. 27-42.
-Van larhoven. P.J.M, Aarts. E.H.L, (1987) simulated annealing: theory and application dordecht, kluwer.
Yang, G.; and Dirogu, M. (2005), Collective Intelligence and Colonial Optimization of the Bee, Translated by Kaveh Nozarasl, Lotfizadeh.
