Data-driven ecological performance evaluation for remanufacturing process

Abstract

Remanufacturing has received extensive attention due to its advantages in material and energy saving, emission reduction and is often considered a viable approach for the realization of a circular economy. Remanufacturing ecological performance reflects the ability of an enterprise to balance economic and environmental benefits. Therefore, evaluating the remanufacturing ecological performance is of great significance for leveraging the benefits of remanufacturing and promoting the concept of sustainability and the implementation of a circular economy in the industry. To this end, a set of data-driven techniques, i.e., data envelopment analysis, R clustering and grey relational analysis, are deployed to analyze and evaluate the ecological performance of a remanufacturing process. The effectiveness and feasibility of the proposed method are illustrated via a case study of remanufacturing for hydraulic cylinder and boom cylinder. Furthermore, a number of critical factors, e.g., energy-saving rate, remanufacturing process cost and rate of remanufacturing, for end-of-life products have been identified as the key drivers impacting the remanufacturing ecological performance. So as to improve remanufacturing ecological performance, optimizing production technology, implementing lean remanufacturing and raising public acceptability over remanufacturing products are effective measures. The research results of the present work can provide support for remanufacturing enterprises to guide and improve their ecological performance and formulate better development strategies.

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

“…Undesirable aspects have been considered when evaluating the performance of suppliers [73][74][75][76][77][78][79][80][81], eco-efficiency [82][83][84][85][86][87][88][89][90][91], and resource and environmental [92][93][94][95][96][97] efficiency. Some studies also introduced DEA efficiency evaluation by taking into account unwanted issues, and unwanted factors can be considered inputs or unwanted outputs in the DEA models [98][99][100][101][102][103][104]. Scientists have introduced various methods to address unwanted outputs in DEA [105].…”

Multi-Objective Parameter Optimization Dynamic Model of Grinding Processes for Promoting Low-Carbon and Low-Cost Production

“…O 5 W i 0.285 0.249 0.153 0.092 0.222 (30) The weighted average centralized processing is used to construct the correlation fuzzy matter element, namely R k = K 1 K 2 K 3 · · · K 30 0.4994 0.4933 0.5067 · · · 0.5593 (31) The minimum value is obtained by sorting the degree of association, and the optimal solution is K 21 . The results in the single target case are obtained and compared, as shown in Table 5.…”

“…For the analysis of the results of green manufacturing, many scholars have proposed many assessment and decision-making methods of energy efficiency [29][30][31][32][33][34]. Cai et al [35] proposed energy performance certification to manage energy consumption and improve energy performance.…”

Multi-Objective Optimization of Workshop Scheduling with Multiprocess Route Considering Logistics Intensity

“…Small and medium-sized enterprises often design different products in response to the changing needs of customers, so that a variety of small batch production models are formed from the limited production cycle and manufacturing resources. Many scholars have used a variety of methods to solve the optimization problems of energy saving, emission reduction, cost reduction, and time saving for the existing traditional manufacturing system, so that the green upgrade optimization of the system can be achieved without a large amount of investment [1][2][3][4][5][6][7][8][9]. It is worth mentioning that Li Congbo et al [10] used the concept of feature elements and processing elements to establish a multi-objective machining route optimization model.…”