Determination of Criticality Indexes in the Remanufacturing Process: A GERT-based Simulation Approach
محورهای موضوعی : Strategic Management
Elham Babaei
1
(M.Sc. Student of Industrial engineering, Isfahan university of technology, Isfahan, Iran)
Abdollah Aghaie
2
(Faculty member of Industrial engineering, K.N. Toosi University of Technology, Tehran, Iran)
Mojtaba Hajian heidary
3
(Ph.D student of Industrial engineering, k.n. Toosi University Of Technology, Tehran. Iran)
کلید واژه: simulation, Remanufacturing processes, Criticality indexes, Moment Generation Function,
چکیده مقاله :
In this paper one of the important “end of life options” (remanufacturing) has been analysed. Among the related studies surveyed the various remanufacturing aspects, less attention has been paid to the stochastic process routing. In this regard, a remanufacturing process routing with stochastic activities is modelled as a GERT network. One of the efficient ways to analyse a remanufacturing process is the identification of most effective activities based on the cost and time of the process during the process implementation. Criticality indexes are suitable scales for this purpose. Therefore, to analyse the important aspects of the remanufacturing process, four criticality indexes are developed in this paper. These indexes measure the cost and time of the process and its activities to identify the activities with high importance in terms of cost and time. On the other hand, simulation is an efficient tool to cope with the uncertainties in the production problems. Hence a Monte Carlo approach (which is developed using Arena software) has been adopted to analyse the GERT based model and to calculate the criticality indexes. In addition, a mathematical approach using Moment Generation Functions has been adopted to calculate the expected value of the criticality indexes. In addition, a numerical example (lathe spindle remanufacturing) has been solved using both proposed approaches. Results show the acceptable performance of the proposed GERT based simulation approach.
Agarwal, M., Sen, K., & Mohan, P. (2007). GERT analysis of m -consecutive- k -out-of- n systems. IEEE Transactions on Reliability, 56(1), 26-34.
Chakraborty, K., Mondal, S., & Mukherjee, K. (2017). Analysis of the critical success factors of automotive engine remanufacturing in India. Uncertain Supply Chain Management, 5(3), 215-228.
Clottey, T., Benton, W., & Srivastava, R. (2012). Forecasting product returns for remanufacturing operations. Decision Sciences, 43(4), 589-614.
Corum, A., Vayvay, Ö., & Bayraktar, E. (2014). The impact of remanufacturing on total inventory cost and order variance. Journal of Cleaner Production, 85, 442-452.
Deng, Q.-w., Liao, H.-l., Xu, B.-w., & Liu, X.-h. (2017). The Resource Benefits Evaluation Model on Remanufacturing Processes of End-of-Life Construction Machinery under the Uncertainty in Recycling Price. Sustainability, 9(2), 256.
Galbreth, M. R., & Blackburn, J. D. (2010). Optimal acquisition quantities in remanufacturing with condition uncertainty. Production and Operations Management, 19(1), 61-69.
Huang, X.-Y., Yan, N.-N., & Qiu, R.-Z. (2009). Dynamic models of closed-loop supply chain and robust H∞ control strategies. International Journal of Production Research, 47(9), 2279-2300.
Ilgin, M. A., & Gupta, S. M. (2012). Remanufacturing modeling and analysis: CRC Press.
Ismail, N. H., Mandil, G., & Zwolinski, P. (2014). A remanufacturing process library for environmental impact simulations. Journal of remanufacturing, 4(1), 2.
Johnson, M. R., & McCarthy, I. P. (2013). Modeling the uncertainty of the remanufacturing process for consideration of extended producer responsibility (EPR).Proceedings of the World Academy of Science, Engineering and Technology (78),1540.
Ketzenberg, M. (2009). The value of information in a capacitated closed loop supply chain. European Journal of Operational Research, 198(2), 491-503.
Ketzenberg, M. E., Laan, E., & Teunter, R. H. (2006). Value of information in closed loop supply chains. Production and Operations Management, 15(3), 393-406.
Li, C., Tang, Y., & Li, C. (2011). A GERT-based analytical method for remanufacturing process routing.Proceedings of the Automation Science and Engineering (CASE), 2011 IEEE Conference on,462-467.
Li, C., Tang, Y., Li, C., & Li, L. (2013). A modeling approach to analyze variability of remanufacturing process routing. IEEE Transactions on Automation Science and Engineering, 10(1), 86-98.
Lund, R. T., & Hauser, W. (2012). The database of remanufacturers. Boston University [www. reman. org/Papers/Reman_Database_Lund. pdf].
Mashhadi, A. R., Esmaeilian, B., & Behdad, S. (2015). Uncertainty management in remanufacturing decisions: a consideration of uncertainties in market demand, quantity, and quality of returns. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 1(2), 021007.
Ng, Y. T., Lu, W. F., & Song, B. (2014). Quantification of end-of-life product condition to support product recovery decision. Procedia CIRP, 15, 257-262.
Nicholas, J. M., & Steyn, H. (2008). Project management for business, engineering, and technology: Principles and practice.
Pandey, V., & Thurston, D. (2010). Variability and Component Criticality in Component Reuse and Remanufacturing Systems. Journal of Computing and Information Science in Engineering, 10(4), 041004.
Ren, S., & Yuan, Z. (2012). Simulation Analysis of Criticality Indexes of Activities in GERT Networks. Business Intelligence and Financial Engineering (BIFE), 2012 Fifth International Conference on, 567-571.
Robotis, A., Boyaci, T., & Verter, V. (2012). Investing in reusability of products of uncertain remanufacturing cost: The role of inspection capabilities. International Journal of Production Economics, 140(1), 385-395.
Sabharwal, S., & Garg, S. (2013). Determining cost effectiveness index of remanufacturing: A graph theoretic approach. International Journal of Production Economics, 144(2), 521-532.
Tao, Z., Zhou, S. X., & Tang, C. S. (2012). Managing a remanufacturing system with random yield: properties, observations, and heuristics. Production and Operations Management, 21(5), 797-813.
Teunter, R. H., & Flapper, S. D. P. (2011). Optimal core acquisition and remanufacturing policies under uncertain core quality fractions. European Journal of Operational Research, 210(2), 241-248.
Zhang, X., Zhang, H., Jiang, Z., & Wang, Y. (2016). A decision-making approach for end-of-life strategies selection of used parts. The International Journal of Advanced Manufacturing Technology, 87(5-8), 1457-1464.
Zhou, L., Xie, J., Gu, X., Lin, Y., Ieromonachou, P., & Zhang, X. (2016). Forecasting return of used products for remanufacturing using Graphical Evaluation and Review Technique (GERT). International Journal of Production Economics, 181, 315-324.
Zhou, L., Xie, J., & Lin, Y. (2010). Forecasting returns in reverse logistics using GERT network theory.
