An integrated fuzzy approach for evaluating remanufacturing alternatives of a product design

Xiaojun Wang 0 Hing Kai Chan 1 0 Department of Management, University of Bristol , Bristol BS8 1TN, UK 1 Norwich Business School, University of East Anglia , Norwich NR4 7TJ, UK Remanufacturing has emerged as a competitive strategy for manufacturers to tackle environmental and economic challenges. In this paper, an integrated fuzzy approach is developed for the evaluation of remanufacturing alternatives. Then, importance weights of main remanufacturing processes and evaluation criteria are obtained through fuzzy extent analysis. Fuzzy hierarchical TOPSIS is then applied to evaluate the alternatives. A case study is presented to demonstrate the applicability of the proposed approach. The analysis results show that it is a viable approach and can be used as an effective tool for design evaluation from the remanufacturing point of view. Finally, conclusions are discussed and future research directions are suggested. - Background In the last two decades, environmental concerns diffuse into almost all aspects of the manufacturing industry and all phases of products' life cycles. This is simply because resources consumed during the course of manufacturing and production are enormously high, and hence, the amount of waste generated from those processes is also notorious [1]. One of such key areas is the end-of-life treatment [2]. Remanufacturing is one of many end-of-life strategies. Remanufacturing is not a new topic but had not been considered as an important strategic area until the recent decade. In the past, remanufacturing activities focus mainly on recapturing economical values from used products or have been driven by regulatory pressure [3]. Typical activities include recycling of materials and reuse of parts or components, among others, to produce close-to-new refurbished products. Figure 1 shows a flowchart of a typical remanufacturing process. Nevertheless, the processing procedures may vary depending on the nature of the product being remanufactured [4]. Obviously, there are lots of uncertainties in remanufacturing [5]. With the backdrop of increasing environmental awareness, remanufacturing is one of many ways to mitigate environmental impacts by reducing the consumptions of virgin materials, resources in primary production and etc. This has been becoming popular in the last decade [6]. The contemporary school of thought considers that remanufacturing can not only (re-)gain financial benefits, but also reduce the environmental burdens [5]. This is a typical multi-objective problem. Remanufacturing is now referred to as a value-adding process and has emerged Inspect parts and subassemblies Clean parts and subassemblies Reassemble parts and subassemblies Package the remanufactured product Figure 1 Remanufacturing process [4]. as part of closed-loop supply chains [7]. This trend implies the importance of developing decision-making models when remanufacturing activities are involved. Life cycle assessment (LCA) provides the basic modelling framework for evaluating the environmental load and impact throughout the entire product life cycle [8]. It is an effective, comprehensive and practical tool in assessing environmental impact of products [9]. For example, Chan et al. [10] adopted the concept of LCA and proposed a comprehensive framework for the selection of green product designs. The life cycle concept is also applicable to remanufacturing process. For instance, Schau et al. [11] conducted an LCA study of remanufactured alternators. Three designs were considered and the associated environmental impacts were evaluated. However, the major obstacle is that remanufacturing activities are not well structured, so applying LCA to evaluate all design options would be time-consuming, if not impractical. Therefore, it is important to provide designers/engineers a more efficient screening approach to assess the environmental and economic performance of alternative designs. Evaluating the environmental and economic impact of a product or process is essentially a multi-criteria decision-making (MCDM) problem. LCA, for example, considers multiple inputs and multiple outputs, and they are not homogenous in most cases. Saaty [12] developed a groundbreaking tool, called analytic hierarchy process (AHP), to deal with MCDM problems. The merit of AHP is that both qualitative and quantitative factors can be considered in a hierarchical model. Since then, applications of AHP are numerous, with a trend to integrate with other methods [13]. One strand of such integrated approaches is to combine the method with fuzzy theory, which was developed by Zadeh [14] and can handle imprecise information. This characteristic supplements the pairwise comparisons in standard AHP so that a higher degree of uncertainty can be included in the decisionmaking process. The fuzzy AHP approach provides such practical solution, which is simple and less demanding upon the resources needed to make a decision by converting uncertain variables into linguistic variables. In other words, the process can be simplified in that sense. Nevertheless, it is still very easy to have over a hundred pairwise comparisons in order to make a design selection decision, which relies heavily on subjective decisions and is therefore not effective in terms of computational complexity. This research confronts this challenge through integration of fuzzy extent analysis and fuzzy hierarchical Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for conducting effective evaluation of design alternatives from the remanufacturing perspective. Fuzzy extent analysis, developed by Chang [15], stems from the AHP method that is used routinely to estimate comparative weights with a view in solving MCDM problems. Studies that apply fuzzy extent analysis leverage the benefits of fuzzy set theory and make use of linguistic terms (e.g. high, very high) or a fuzzy number in lieu of a precise numerical value when conducting pairwise comparison e.g. [16]. It has been widely applied in different problem environments in the literature: Kahraman et al. [17] developed an analytical selection tool to measure the customer satisfaction in catering firms in Turkey, Celik et al. [18] developed fuzzy AHP methodology based on Chang's extent analysis to model shipping registry selection, and Wang et al. [19] applied fuzzy extent analysis to develop a risk assessment model that enabled a structured analysis of aggregative risk in the food supply chain. The trends in utilizing fuzzy extent analysis in fuzzy AHP evident in the literature have been continued in many of the operational disciplines due to its ease of use and computational simplicity. Fuzzy TOPSIS [20,21] is derived from the TOPSIS technique proposed by Hwang and Yoon [22] to evaluate the performance of alternatives. TOPSIS ranks the alternatives according to their distances from the ideal and the negative ideal solution. The positive ideal solution maximizes the benefit (...truncated)