Simulation on Remanufacturing Cost by Considering Quality Grade of Returns and Buffer Capacity

doi:10.1007/s12204-019-2096-0

Abstract

Abstract: Selecting the remanufacturing system as object of the study, the buffer capacity is served as control variable, and design of experiment (DOE) and simulation are used to analyze the effect that uncertain quality of returns acts on system performance. The remanufacturing time and the recovery rate in each station are used to represent the quality level of the returns, and the variance of remanufacturing time is used to denote the variability of returns’ quality. Three factors (the variability, the proportion and the recovery rate) of different quality levels in returns are considered. By analyzing the variance and the range of the simulation results, some important conclusions are obtained: recovery rate affects the remanufacturing cost by far, and the variability has the minimum influence; furthermore, for the returns, the more obvious of the dispersion degree, the higher proportion of the high-level quality, and the higher of the recovery rate, the lower the cost of remanufacturing will be.

Key words: remanufacturing| buffer| simulation| design of experiment (DOE)| quality grade

摘要： Selecting the remanufacturing system as object of the study, the buffer capacity is served as control variable, and design of experiment (DOE) and simulation are used to analyze the effect that uncertain quality of returns acts on system performance. The remanufacturing time and the recovery rate in each station are used to represent the quality level of the returns, and the variance of remanufacturing time is used to denote the variability of returns’ quality. Three factors (the variability, the proportion and the recovery rate) of different quality levels in returns are considered. By analyzing the variance and the range of the simulation results, some important conclusions are obtained: recovery rate affects the remanufacturing cost by far, and the variability has the minimum influence; furthermore, for the returns, the more obvious of the dispersion degree, the higher proportion of the high-level quality, and the higher of the recovery rate, the lower the cost of remanufacturing will be.

关键词: remanufacturing| buffer| simulation| design of experiment (DOE)| quality grade

CLC Number:

F 270

ZHENG Yuqiao *(郑玉巧), ZHANG Chengcheng (张铖铖), SU Chun (苏春). Simulation on Remanufacturing Cost by Considering Quality Grade of Returns and Buffer Capacity[J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(4): 471-476.

References

[1] LIU WJ, ZHANG J, JIN M Z, et al. Key indices of theremanufacturing industry in China using a combinedmethod of grey incidence analysis and grey clustering[J]. Journal of Cleaner Production, 2017, 168: 1348-1357. [2] ZHANG G Q, JING X D, PU G Q, et al. Theassessment method and model of remanufacturability[J]. Journal of Shanghai Jiao Tong University, 2005,39(9): 1431-1436 (in Chinese). [3] AYDIN R, KWONG C K, GEDA M W, et al. Determiningthe optimal quantity and quality levels ofused product returns for remanufacturing under multiperiodand uncertain quality of returns [J]. The InternationalJournal of Advanced Manufacturing Technology,2018, 94(9/10/11/12): 4401-4414. [4] ARAS N, BOYACI T, VERTER V. The effect of categorizingreturned products in remanufacturing [J]. IIETransactions, 2004, 36(4): 319-331. [5] WANG J, ZHAO J, WANG X. Optimum policy in hybridmanufacturing/remanufacturing system [J]. Computers& Industrial Engineering, 2011, 60(3): 411-419. [6] FERGUSON M, GUIDE V D, KOCA E, et al. Thevalue of quality grading in remanufacturing [J]. Productionand Operations Management, 2009, 18(3):300-314. [7] XI S H, CHEN Q X, MAO N, et al. Modelingand analysis of assembly manufacturing system withbulk storage and transportation [J]. Computer IntegratedManufacturing Systems, 2017, 23(1): 32-43 (inChinese). [8] DEMIR L, TUNALI S, ELIIYI D T. The state ofthe art on buffer allocation problem: A comprehensivesurvey [J]. Journal of Intelligent Manufacturing,2014, 25(3): 371-392. [9] AKSOY H K, GUPTA S M. Buffer allocation planfor a remanufacturing cell [J]. Computers & IndustrialEngineering, 2005, 48(3): 657-677. [10] AKSOY H K, GUPTA S M. Near optimal bufferallocation in remanufacturing systems with N-policy[J]. Computers & Industrial Engineering, 2010, 59(4):496-508. [11] SU C, XU A J. Buffer allocation for hybrid manufacturing/remanufacturing system considering qualitygrading [J]. International Journal of Production Research,2014, 52(5): 1269-1284. [12] CORUM A, VAYVAY ¨ O, BAYRAKTAR E. The impactof remanufacturing on total inventory cost and ordervariance [J]. Journal of Cleaner Production, 2014,85: 442-452. [13] SU C. Modeling and simulation for manufacturing system[M]. 2nd ed. Beijing: China Machine Press, 2014(in Chinese). [14] LUND R T, HAUSER W M. Remanufacturing: AnAmerican perspective [C]//5th International Conferenceon Responsive Manufacturing: Green Manufacturing.Ningbo, China: IET, 2010: 1-6. [15] WU C H. Product-design and pricing strategies withremanufacturing [J]. European Journal of OperationalResearch, 2012, 222(2): 204-215. [16] MAO S S, ZHOU J X, CHEN Y. Design of experiment[M]. 2nd ed. Beijing: China Statistic Press, 2012 (inChinese). [17] HE Y P. Design and analysis of experiments [M]. Beijing:Chemical Industry Press, 2013 (in Chinese).