Optimal sorting policies in remanufacturing systems: Application of product life-cycle data in quality grading and end-of-use recovery

Abstract

The quality of used products returned back to recovery facilities is often highly uncertain. Quality grading and sorting policies are immediate solutions that are used in remanufacturing systems to handle this source of variability in incoming products. The sorting policies offered in the literature so far are mainly based on external criteria such as market trends, corporate policies and assessment of the product's physical condition. In this study, we offer a new sorting method based on both product's internal factors such as future reusability of components, product identity data, and product health status as well as external factors such as market trends. The purpose of this paper is to improve decision making in remanufacturing operations by integrating the product life cycle information, particularly product usage phase data, into determining both optimal sorting policies and End-of-Life/End-of-Use (EoL/EoU) decisions. To achieve this, two related analyses are conducted: first, a reusability index is derived for each product unit based on the reusability of its components, product features, and the product usage phase information. Second, the reusability index is used as a quality measure to derive the optimal EoU decision for each product category. Clustering algorithms are employed to identify similar products that could go through the same recovery process. A data set of hard disk drive Self-Monitoring Analysis and Reporting Technology (S.M.A.R.T.) coupled with simulated data has been analyzed to illustrate the application of the model. The proposed framework helps decision makers include product identity data in the EoU recovery decision making under the quality heterogeneity.

A Review on the Lifecycle Strategies Enhancing Remanufacturing

“…Relevant Papers Case Study Used EoL product collection [30] Electronic products [31] Emphasis on remanufacturing [32] Mobile phones [33] WEEE CLSC [34] OEMs [35] Electric vehicle batteries [36] System dynamics analysis [37] Paper recycling Predetermined remanufacturing timing [38] Manufacturing systems [39] Multi-scale PCA [40] Wind turbines RUL [41] Used parts [40] Wind turbines [42] Aircraft engines [43] Railway D-cables Reverse logistics [44] Cell phones [45] Use of algorithm in Taoyuan City [46] Improved genetic algorithm [47] Chinese automobile parts DfRem [48] Disassembly line balancing [49] Analyzing operational factors [50] Analytic network process Product lifecycle data [51] Quality grading and end-of-use recovery The relevant papers were quantified and coded based on which strategies they had an impact on, the lifecycle stage in which they were applied and the case studies used to demonstrate the theory. Table 1 below shows the case studies used for each strategy.…”

“…EoL product collection [30] Electronic products [31] Emphasis on remanufacturing [32] Mobile phones [33] WEEE CLSC [34] OEMs [35] Electric vehicle batteries [36] System dynamics analysis [37] Paper recycling Predetermined remanufacturing timing [38] Manufacturing systems [39] Multi-scale PCA [40] Wind turbines RUL [41] Used parts [40] Wind turbines [42] Aircraft engines [43] Railway D-cables Reverse logistics [44] Cell phones [45] Use of algorithm in Taoyuan City [46] Improved genetic algorithm [47] Chinese automobile parts DfRem [48] Disassembly line balancing [49] Analyzing operational factors [50] Analytic network process Product lifecycle data [51] Quality grading and end-of-use recovery [52] IoT scheduling to predict remanufacturing timing [53] Big data in product lifecycle PSS [54] Baby prams [55] Upgradable PSS Collaborative robots [56] Human-robot collaborative disassembly cell [57] Enhanced discrete bee algorithm [58] Maintenance of autonomous train [59] A study on attitude and acceptance in an industrial context Additive manufacturing [60] Metals (silver, iron, etc.) [61] Future outlook for remanufacturing Industry 4.0 [62] Inter-country comparative perspective [63] Use of augmented reality Upgrading products [64] EoL tires [65] PSS SRDM [66] Algorithm to optimize disassembly Smart reman...…”

Investigating remanufacturing competition with yield uncertainty on market share, profit, and consumer surplus

“…This literature mainly discusses the topic in a supply chain system, and it would be practical and necessary to extend the research in a competitive environment. Raihanian Mashhadi and Behdad () pointed out that the used products remanufactured by recovery facilities is uncertain in quality. Optimal sorting policies are derived in a remanufacturing system by considering product life cycle.…”

Joint Discrete-Continuous Model of Travel Mode and Departure Time Choices

“…Many studies show that travelers' lifestyles, attitudes, and perceptions significantly affect their travel behavior, and heterogeneous travelers may response differently to transport policies [such studies include, e.g., Anable ( 31 Cluster analysis is one of the most widely used approaches to deal with heterogeneity of individuals in terms of their decision-making criteria toward various choices, as shown in Everitt et al (34) and Raihanian Mashhadi and Behdad (35). In this research, a distancebased clustering method was used that classified observations into relatively homogeneous collections by minimizing the variance across variables of interest within clusters and maximizing the variance between clusters (10).…”