Analytical Optimization of Supply Chain Operation Planning based on a Multi-Item Cell Manufacturing

Document Type : Research Paper

Author

Assistant Professor, Department of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran

Abstract

Supply chain is known as a network of linked and organized elements to control, manage and improve the flow of material, finance and information within the chain (from supplier to final consumer). Evolving competitive objectives among enterprises leading them to increase competitiveness via improving flexibility and response to customers.  This research is to optimize time in a forward, multi-layer, multi- product, and multi-period supply chain. Production time is composed of processes times of all manufacturing cells being investigated in cell manufacturing system. Therefore, a nonlinear integer mathematical program is formulated to minimize the total cycle time and the maximum delay time of the supply chain. Then, numerical instances are solved in GAMS software package by Branch & Bound method. In the two sizes of the example provided, the behavior of the model was reasonable and in the larger problem size the solution time increased, which can be justified by increasing the branching by increasing the number of time periods, product and item in each layer. In addition, sensitivity analysis was performed on the important parameters of the problem and the behavior change of the objective function based on the parameter changes was investigated. The obtained results can be used in decision-making planning of supply chain operations as a separate layer and also in an integrated manner with flexibility, in the presence of crisis

Keywords


  • بهنامیان، جواد، کمیجانی، فاطمه. (1397). ارائه الگوریتم شاخه و برش برای حل مسأله زمان‌بندی تولید کارگاهی با استفاده از نامعادلات معتبرنشریه پژوهش‌های مهندسی صنایع در سیستم های تولید. 6(13)، 139-149.
  • پایدار، محمد مهدی، سعیدی مهرآباد، محمد، (1393). طراحی یک مدل یکپارچه استوار دو‌هدفه زنجیره‌‌تأمین و آرایش سلولی مجازی پویا. نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید، 2(3)، 33-45.
  • Aalaei, A., and Davoudpour, H. (2016). Revised multi-choice goal programming for incorporated dynamic virtual cellular manufacturing into supply chain management: a case study. Engineering Applications of Artificial Intelligence, 47, 3-15.
  • Aalaei, A., and Davoudpour, H. (2016). Two bounds for integrating the virtual dynamic cellular manufacturing problem into supply chain management. Journal of industrial and management optimization, 12(3), 907-930.
  • Aalaei, A., and Davoudpour, H. (2017). A robust optimization model for cellular manufacturing system into supply chain management. International Journal of Production Economics, 183, 667-679.
  • Chang, C, T. (2007). Multi-choice goal programming. Omega 35, 389-396.
  • Charnes, A. and Cooper, W.W. (1977), Goal Programming and Multiple Objective Optimizations, Part I, European Journal of Operational Research 1(1), 39-54.
  • Delgoshaei, A., Ariffin, M. K. A., Baharudian, B. H. B., and Leman, Z. (2016). A new method for decreasing cell-load variation in dynamic cellular manufacturing systems. International Journal of Industrial Engineering Computations, 7(1), 83-110.
  • Ghezavati, V. R. (2011). A new stochastic mixed integer programming to design integrated cellular manufacturing system: A supply chain framework. International Journal of Industrial Engineering Computations, 2(3), 563–574.
  • Ghezavati, V. R., Sadjadi, J., and Nayeri, M. D. (2011). Integrating strategic and tactical decisions to robust designing of cellular manufacturing under uncertainty: Fixed suppliers in supply chain. International Journal of Computational Intelligence Systems, 4(5), 837–854.
  • Houshyar, A.N., Leman, Z., Ariffin, M. K. A., and Ismail, N. (2016). Proposed linear-mathematical model for configuring cell and designing unequal-area facility layout in dynamic cellular manufacturing system. International Journal of Industrial and Systems Engineering, 22(3), 332-357.
  • Jadidi, O., Cavalieri, S., and Zolfaghari, S., (2015). An improved multi-choice goal programming approach for supplier selection problems, Applied Mathematical Modelling 39(14), 4213- 4222.
  • Mehdizadeh, E., and Rahimi, V. (2016). An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts. Applied Soft Computing, 42, 325-341.
  • Melo, M.T., Nickel, S., and Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European journal of operational research, 196(2), 401-412.
  • Niakan, F., Baboli, A., Moyaux, T., and Botta-Genoulaz, V. (2016). A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment. Journal of Manufacturing Systems, 38, 46-62.
  • Pan, F., and Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & operations research, 37(4), 668-683.
  • Paydar, M.M., and Saidi-Mehrabad, M. (2015). Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters. International Journal of Computer Integrated Manufacturing, 28(3), 251-265.
  • Paydar, M.M., and Saidi-Mehrabad, M. (2017). A hybrid genetic algorithm for dynamic virtual cellular manufacturing with supplier selection. The International Journal of Advanced Manufacturing Technology, 92(5-8), 3001-3017.
  • Paydar, M.M., Saidi-Mehrabad, M., and Teimoury, E. (2014). A robust optimisation model for generalised cell formation problem considering machine layout and supplier selection. International Journal of Computer Integrated Manufacturing, 27(8), 772-786.
  • Rao, P.P., and Mohanty, R. P. (2003). Impact of cellular manufacturing on supply chain management: exploration of interrelationships between design issues. International journal of manufacturing technology and management, 5(5-6), 507-520.
  • Sakiani, R., Ghomi, S. F., and Zandieh, M. (2012). Multi-objective supply planning for two-level assembly systems with stochastic lead times. Computers & Operations Research, 39(7), 1325-1332.
  • Saxena, L.K., and Jain, P. K. (2012). An integrated model of dynamic cellular manufacturing and supply chain system design. The International Journal of Advanced Manufacturing Technology, 62(1), 385-404.
  • Schaller, J. (2008). Incorporating cellular manufacturing into supply chain design. International Journal of Production Research, 46(17), 4925-4945.