Uncertainties in core quality condition, return quantity and timing can propagate and accumulate in process cost and complicate cost assessments. However, regardless of cost assessment complexities, accurate cost models are required for successful remanufacturing operation management. In this paper, joint effects of core quality condition, return quantity, and timing on remanufacturing cost under normal and extreme return conditions is analyzed. To conduct this analysis, a novel multivariate stochastic model called Stochastic Cost of Remanufacturing Model (SCoRM) is developed. In building SCoRM, a Hybrid Pareto Distribution (HPD), Bernoulli process, and a polynomial cost function are employed. It is discussed that core return process can be characterized as a Discrete Time Markov Chain (DTMC). In a case study, SCoRM is applied to assess remanufacturing costs of steam traps of a chemical complex. Its accuracy analyzed and variations of SCoRM in predictive tasks assessed by bootstrapping technique. Through this variation analysis the best and worst cost scenarios determined. Finally, to generate comparative insights regarding predictive performance of SCoRM, the model is compared to artificial neural network, support vector machine, generalized additive model, and random forest algorithms. Results indicate that SCoRM can be efficiently utilized to analyze remanufacturing cost. Keywords: Remanufacturing, extreme value theory, hybrid Pareto distribution, stochastic model.
翻译:核心质量条件、返回数量和时间的不确定性可以在过程成本和复杂成本评估中传播和积累,然而,无论成本评估的复杂性如何,都需要准确的成本模型,才能成功地进行再制造业务管理;本文分析了在正常和极端返回条件下核心质量条件、返回数量和时间对再制造成本的共同影响;为进行这一分析,开发了一个叫作“再制造模型的托盘成本”的新颖的多变量随机模型;在建设SCORM、混合帕雷托分配(HPD)、伯努利进程和混合成本功能时,都需要精确的成本模型;讨论核心回归进程可被定性为在正常和极端返回条件下的混凝土时间马尔多夫链(DTMC);在案例研究中,SCOCRM用于评估一个化学综合体蒸汽陷阱的再制造成本成本模型;SCOCRM在通过靴技术评估的预测性任务中的精确度和变化模型;通过这种变异分析,确定了最佳和最坏的成本假设;最后,为SCOCRM的预测性模型和随机性分析,该模型可以用来比较SOCRM的模型。