چکیده:
Objective: In today's competitive economy, the supply of a product in an optimal quantity, quality and minimum cost at the right time are the pillars of success in a business. A continually efficient supply chain also plays an important role in solidifying this success. This research aims to model an integrated supply chain network to achieve maximum profit while minimizing the overall response time. Methods: In view of the importance of supply chain management and logistics in recent times, the design and optimization of the supply chain network are extremely critical. This is mostly because, at each decision level, a well-optimized decision will result in a competitive advantage in the market. In this research, the said design and optimization is explained in two levels; a strategic level where a supply chain is designed, and a tactical level dedicated to operational planning of the supply chain. In this paper, a mathematical model has been formulated for these levels. To verify and validate the proposed model, the acquired data from a computer case manufacturing company are examined and the outcomes were highlighted. Results: This paper provides a tool in order to optimize the supply chain network. This tool can be useful for any manager or for those who are planning a supply chain from production or designing a product distribution network. Managers can also use this model to ensure the performance parameters of a particular supply chain. Conclusion: The results show that the proposed model can provide an effective and easy-to-follow method to achieve a well-established plan in an integrated supply chain
خلاصه ماشینی:
Designing an Optimization Model for Integrated Strategic and Tactical Levels of the Supply Chain Network Hamidreza Fallah Lajimi * * Corresponding Author, Assistant Professor, Department of Industrial Management, Faculty of Economics and Administrative Sciences, Mazandaran University, Babolsar, Iran.
Many studies have been conducted in the field of supply chain network optimization (Wang & Liang 4, 2005; Lodree & Uzochukwu 5, 2008, Ugwu, Liu, Wang 6, 2011; Zhang & Lee, 7 2016; Farias, Li, Galvez, Borenstein, 2017 and Aras & Bilge 8, 2018).
In these studies, solving these problems has been done using zero-one programming modeling (Tanhue, Bostel & Péton 2012, 1; Lee & Kwon 2010, 2), non-linear programming (Zhang & Li, 2016), multi-objective programming (Govindan, Jafarian, Nourbakhsh 3, 2015; Vahdani & Mohammadi 4, 2015), mixed integer linear programming (Eras & Bilgen, 2018) and stochastic programming (Yang, Li, Jiao, Wang 5, 2018).
Various methods exist in the literature to solve this modeling, including the Benders decomposition method (Keyvanshokooh, Aryan, Kabir 7, 2016), the Lagrangian method (Kumar & Tiwari 2013, 8), the genetic algorithm (Altiparmak, Gen, Lin, Paksoy 2006, 9), (Bashiri & Rezanezhad, Tavakkoli-Moghaddam, Hasanzadeh 10, 2018) and heuristic techniques (Soleimani & Kannan 11, 2015).
Qiu, Zhang, Ren, Suganthan, Amaratunga 5 Mota, Gomes, Carvalho, Barbosa-Povoa Research Methodology Problem Definition As mentioned, the aim of the research is to present a mathematical model for optimizing the supply chain network at strategic and tactical levels.
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