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.

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of End-of-Life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition-integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products.

As one of the most important components of machine tool, guideway has an important driving-force to comprehensively improve the remanufacturability of machine tools. To select optimal guideway for machine tool remanufacturing, an integrated multi-criteria decision making (MCDM) approach that combines improved Analytic Hierarchy Process (AHP) and Connection Degree based Technique of ranking Preferences by Similarity to the Ideal Solution (CD-TOPSIS) method is proposed. The improved AHP is employed to calculate the weights of each criterion and the CD-TOPSIS is adapted to complete the task of sorting, finally, the comprehensive evaluation of the alternatives is carried out. A case study, i.e., eight types of guideways, is illustrated to verify the proposed MCDM method. In addition, comparison with existing methods are performed to validate the effective and reliability for the proposed hybrid approach. Also, sensitivity analysis is provided to evaluate the robustness of the method. The final result shows the method provides reliable decision support for the selection of machine tool guideways for remanufacturing.

Summary Minimizing investment in oil field development is an important subject that has attracted a considerable amount of industry attention. One method to reduce investment involves the optimal placement and selection of production facilities. Because of the large amount of capital employed in this process, saving a small percent of the total investment may represent a large monetary value. Algorithms using mathematical programming techniques that were designed to solve the proposed problem in a global optimal manner have been reported in the literature. Due to the high computational complexity and the lack of user friendly interfaces for data entry, model development and results display, decision makers are not willing to accept mathematical programming techniques. This paper describes an interactive, graphical software system that provides a global optimal solution to the problem of placement and selection of production facilities in oil field development processes. This software system can be used as an investment minimization tool and a scenario study simulator. The developed software system consists of five basic modules: an interactive data input unit, a cost function generator, an optimization unit, a graphic output display and a sensitivity analysis unit. The interactive data input unit allows users to enter cost, facility, and constraints data; the cost function generator determines which cost function to use based on data input; the optimization unit allows users to select one of the two optimization algorithms: 0-1 integer programming with preprocessing and Lagrangian Relaxation; the graphic output unit displays the optimal solution graphically; and the sensitivity analysis unit allows users to perform if-then type analysis. Data from both on-shore and off-shore oil fields have been used to test the performance of the developed system. Significant reduction of computer run time and memory storage were obtained. Investments in the order of several million dollars were saved through the use of this system. These results show that the developed system can be applied where significant investments in off-shore and/or on-shore field developments are involved. Introduction At the early stage of an oil field development, after reservoir boundaries have been delineated and bottomhole well locations have been determined, a critical investment decision for reservoir and facility engineers is to choose an economically optimal, mix of drilling and facilities construction options given the environment, facilities, and engineering restrictions (mud slide areas, wet land, or shipping lanes, facility availability in one area, safety and other policy restrictions, limits on horizontal well deviation). Drilling options include wells drilled directionally or vertically from fixed or floating facilities. Facilities construction options include selection of sub-sea templates, manifolds, platforms (off-shore), and drilling pads (on-shore). Costs include facility investment and drilling and completion expenses.

No abstract