A review of techniques to determine alternative selection in design for remanufacturing

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1 IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS A review of techniques to determine alternative selection in design for remanufacturing To cite this article: A Z Mohamed Noor et al 2017 IOP Conf. Ser.: Mater. Sci. Eng View the article online for updates and enhancements. Related content - Application of Fuzzy TOPSIS for evaluating machining techniques using sustainability metrics Abhijeet K Digalwar - Equipment Selection by using Fuzzy TOPSIS Method Mahmut Yavuz - Review of techniques for on-line monitoring and inspection of laser welding J Shao and Y Yan This content was downloaded from IP address on 06/11/2018 at 13:59

2 A review of techniques to determine alternative selection in design for remanufacturing A Z Mohamed Noor *, M H F Md Fauadi, F A Jafar, N R Mohamad, A S Mohd Yunos Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia,Hang Tuah Jaya, 76100, Durian Tunggal Malacca, Malaysia *Corresponding author:ahamadzaki.mohamednoor@gmail.com Abstract. This paper discusses the techniques used for optimization in manufacturing system. Although problem domain is focused on sustainable manufacturing, techniques used to optimize general manufacturing system were also discussed. Important aspects of Design for Remanufacturing (DFReM) considered include indexes, weighted average, grey decision making and Fuzzy TOPSIS. The limitation of existing techniques are most of them is highly based on decision maker s perspective. Different experts may have different understanding and eventually scale it differently. Therefore, the objective of this paper is to determine available techniques and identify the lacking feature in it. Once all the techniques have been reviewed, a decision will be made by create another technique which should counter the lacking of discussed techniques.in this paper, shows that the hybrid computation of Fuzzy Analytic Hierarchy Process (AHP) and Artificial Neural Network (ANN) is suitable and fill the gap of all discussed technique. 1. Introduction Design for Remanufacturing (DFReM) is a concept of manufacturing environment which was developed for future generation of manufacturing and process technology. Remanufacturing is a process of returning used product by rework, reassemble, disassemble reprocess, inspect and testing in order to bring back current appearance to brand new part [1]. There are three indicators in Design for Remanufacturing. The indicators are economic, social and ecological [2]. The problem with current situation is that the techniques used are very difficult to apply in the assessment of the remanufacturing process [2]. Total of alternatives can be more than five, could also be twenty and the techniques used are not suitable to the situation. The techniques that are look into are narrowed down to a technique used for optimization in manufacturing field. Therefore, the objective of this paper is to determine the technique used for selection purpose of criterion in DFReM economy indicator. The next objective is to further identify the lacking in method and proposed another alternative for decision making purposes in DFReM. 2. Methodology This paper starts with identifying the techniques used for cost optimization in economy indicator under DFReM. The techniques are index, weighted average, grey decision making method and fuzzy TOPSIS. All the techniques are presented with the steps to be carried out in order for determination of final answer. The steps are introduced with example from published experiment carried out by previous author. Next step is to propose personal argument regarding the steps especially the advantage and lacking. This paper further carried out by suggesting a new method that will be Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by Ltd 1

3 introduced in Design for Remanufacturing to be proven that this new method covers all the lacking from current introduced method. Figure 1 shows the overall flow chart of this paper. Figure 1. Overall Flow Chart of the Review 3. Techniques Used for Decision Making in DFReM There are several techniques which being used for decision making in remanufacturing process. The techniques used are indexes, weighted average, grey decision making and Fuzzy TOPSIS the next section of this paper will show the step carried out in order to demonstrate these methods. 3.1 Indexes The authors wants to evaluate product recyclability to be applied during the design phase, acting as a supporting technique for designer decision making. Aguiar et al [3] have proposed indexes to be used with the product s Bill of Material (BOM) during conceptual and embodiment phase of product design. The indexes were used in basically two process which are recycling and disassembly. To be more specific, the criteria being evaluated under these processes are: Table 1: Comprehensive Criterion Evaluation for Recycling and Disassembly Processes. Recycling Process Number of fastener Type of fastener Accessibility of fasteners Disassembly Process Existence of contaminants Material compatibility Presence of hazardous materials These criteria have several options to be decided as the exact number of fastener or the best type of fasteners and other suitable criteria needed. The products that the author chooses to solve are portable 2

4 cassette and CD player. The index scale is determined through colour scale for recyclability indexes. The green shows the ideal state and the red colour shows undesired state. Green will indicate that the selection is highly desired and should be selected in any situation. For this paper, author placed this colour for fasteners type in portable cassette and CD player. The presentation of ideal and undesired state is shown in figure 1. Figure 2. Colour scale for recyclability Index [3] The colour have been placed its scale according to colour. The green is known as value one and the red shows value four [3]. In this paper, quantity of types of fasteners index (QTFI) is discussed in different types of fastener. The least type of fastener used is the ideal selection. However, the more types of fastener show code red. Therefore the indexes used are between values 1 to 4. Table shows value of indexes according to the colour code. Table 2. Quantity of Types of Fastener Indexes[3] QTFI Values Description 1 1 Type 2 2 Types 3 3 Types 4 4 or more types Meanwhile Ijomah et al [2] use indexes in the same field which is design for remanufacturing. The product used in this paper is engine block. They proposed fundamental steps required to build on past work. The authors have improved the robustness of DFReM methodologies. Table 3. Design features affecting product remanufacturability[4] Design Feature A)Assembly Type 1-4 B)Product Complexity 3 Materials 4 Design Cycle 3 Severity of Impact Table 3 (a). Problem identified from design feature affecting product remanufacturability[4] Problem Identified A)Screws 1 Rivets 2 Welding 3-4 Strong Adhesive 4 B)Numerous Component 2 Product Dimension 2 Internal Component 2-3 Arrangement Coatings 2 Severity of Impact From the table 3, the index used is from 1 to 4. Value 1 shows low impact and value 4 shows very high impact[4]. The indexes used are still between ranges hardly to differentiate between criteria. Example of paper [5] uses index for the purpose of creating quantitative approach is assessing product 3

5 design for remanufacturing. The authors created an assessment for remanufacturing of initial product design. The assessment consists of technical and economic feasibility. However, the author focuses usage of indexes in technical feasibilities. Figure 2 shows the exact operation where the index is used. To determine the index, authors use formulas. Each process has own calculation of index. The highest index shows high impact. Table 4 shows the index calculation for all process in technical feasibilities. Assessment for remanufacturing of initial product design Technical Feasibilities Economic Feasibilities Returning Index Disaasembly Index Cleaning Index Testing Index Refurbishment Index Reassembly Index Figure 3. An assessment system for remanufacturing of initial product design returning[5] Table 4. Computation of index with respective formula[5] Type of Index Returning Index Disassembly Index Cleaning Index Testing Index Refurbishment Index Reassembly Index Formula h = h = (h ) 1.5 h = (h ) 1( : h ) h () = (h ) 10 = 1 h h h h = (h ) 3 h The authors [5] mentioned an evaluation model based on the remanufacturing process which comprehensive assess product design for remanufacturing need to be further tested and verified. Therefore, there are still room for improvement to improve quantitative approach using index. 3.2 Weighted Average Weighted is another quantitative method that is used in design for remanufacturing. Basically this method uses decision maker s perception to determine weightage of criteria. When humans make decision, it may influence by mood, environment sometime time constraint making the weightage assigned sometimes gives incorrect answer. [1] create a metric for assessing remanufacturability by using weighted average method. The objective of the paper is to establish efficient and effective design metrics to measure the remanufacturability of product designs. This paper carried out experiment in remanufacturing automotive parts. The method for weighted average uses for the same process as the previous paper which are assembly, disassembly, testing, repair, cleaning, inspection, refurbishment and replacement process. 4

6 Figure 4. Structure of Remanufacturability Assessment[1] Observed in figure 3, the weight placed need to achieve equivalent to 100%. The weight can be either 20% - 80%, or 40% - 60% can also be 30% - 70%. All depends on the decision maker s perception. Figure 4 shows the amount of investment needed for each method by pairwise comparison in order to generate their relative importance. Since the relative importance were based on incremental decisions, as shown in the legend, it is not appropriate to take the exact relative importance as being precisely correct. Therefore, the calculated importance is rounded to a set of approximations to the true relative importance. Prioritization Matrix Legend 5 (row) requires much more investment than (column) 3 (row) requires more investment than (column) 1 (row) requires the same investment as (column) 1/3 (row) requires less investment than (column) 1/5 (row) requires much less investment than (column) Blown Abraded Baked Washed Score Relative Importance Approximate Cleaning Score Usable Cleaning Score Blown % Abraded % Baked % Washed % % Figure 5. Example Prioritization of Cleaning Process[1] The authors comment that due to the linearity of this technique, a poor response (even a zero value) is compensated for by a higher response. Using this technique, it is possible to have undesirable scenarios such as the following: 30% of the indices are set to zero, and 70% are set to 100%, resulting in a total remanufacturability index of 70%. 3.3 Grey Decision Making The advantage of grey theory is that able to deal flexibly with the fuzziness situation. Supplier selection can be viewed as a grey system process [6]. Grey Method of computing consist of 8 steps depends on its application. For this paper, the use of grey decision making is to determine best supplier selection. Step 1 is to determine the attribute weight. K shows the total number of decision makers. = (1) The next step which is step 2 is to identify the rating value = [ ] (2) Once step 2 is done, the next procedure step 3 is to establish the grey decision matrix. 5

7 = (3) Since the scaling performed in matrices shows different sum and hardly to relate, therefore the fourth step is to normalize the grey decision matrix. = (4) Whereby for benefit attribute, =, (5) for cost attribute is expressed as =, (6) Step 5: The weighted normalize grey decision matrix is expressed as = (7) where =. Step 6: Supplier alternatives can be obtained by max,max, = max,max, (8) max,max Step 7: Calculate grey possibility degree between compared suppliers alternatives and ideal referential supplier alternatives. { } = 1 Step 8: The order of supplier alternatives were ranked. When { } is smaller, the ranking order is better. According to [6], the ranking order of all suppliers alternatives can be determine and selection of the best among a set of feasible suppliers. Next paper which uses grey decision making as a technique for classification of sustainability level in remanufacturing companies. The objective of [2] are to provide a new technique for decision making. (9) Figure 6. The classification using grey decision making[2] In this paper, grey decision making is used to determine the final answer obtained. The answer will be further classified in three different categories. There are three values of represent in three different colours. The values with respect to colours are =1(green), =2(yellow) and =3(red). When the colour show green, which means there is no immediate improvement action needed to 6

8 remanufacture the product. If the colour show yellow, it means that the level of sustainability is conditional. There will be a little remanufacture needed to fix the product. Lastly if the colour code show red after being computed using grey decision making, the level of sustainability is unacceptable. Major remanufacture or reject will take place. The classification will depends on the final computation from grey decision making method. However, the limitation of the presented approach is the fact that, it relies on the experts' knowledge of the decision makers. Furthermore, the prioritization scheme is elaborated based on experts' knowledge, so further research might be needed in order to verify the assigned criteria weights 3.4 Fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) Fuzzy TOPSIS consists of eight steps. The first step is to determine the weightage of evaluation criteria. Most Fuzzy TOPSIS use triangular fuzzy AHP to find fuzzy preference weight[7], [8], [9].Therefore, only three values are used under Fuzzy TOPSIS for evaluation. It is expressed in = (,, ). Second step is to choose the appropriate linguistic judgement based from table 5 for choices or alternatives with respect to criteria. The table scaling is performed by[7]. Table 5. Linguistic Term for Fuzzy TOPSIS[8] Linguistic Term Scale Very High (9,10,10) High (7,9,10) Fair (5,7,9) Average (3,5,7) Low (1,3,5) Very Low (0,0,3) Once all the terms have been identified and pairwise comparison made. The third step is to construct a fuzzy decision matrix. Fuzzy decision matrix is shown in equation (10). = = 1,2,. ; = 1,2,., (10) The fourth step is to perform normalization. The normalized values are calculated using equation (11). = = 1,2,., ; = 1,2,., (11) Previous steps were done for continuing in the fifth step. The fifth step is to identify the weighted normalized value ( ) which can be identified through =.. Weight is symbolized as ( ) whereby it is the weight of th attribute[9]. Next stage is to determine the ideal solution either to be positive or negative. This is basically the sixth step in Fuzzy TOPSIS. For fuzzy positive ideal solution (FPIS): =(,,, ) where = (1,1,1) (12) For fuzzy negative ideal solution (FNIS): =(,,, ) where = (0,0,0) (13) After FPIS and FNIS are identified after using equations (12) and (13), the distance of each alternative from and [10].The seventh step is to determine distance between alternative through equation (14) will be employed to calculate (, )[11]: 7

9 (, ) =(, ) = [( ) + ( ) + ( ) ] (14) Last step is to calculate the closeness coefficient for each alternative. The closest final answer to 1 will be rank and score as the best alternatives to carry out as a decision[12], [13]. Equation (15) shows how to calculate the closeness coefficient. =, =1,2,., (15) A paper written by [14] perform an experiment to carry out material selection. The objective of the paper is to evaluate the performance of candidate material using Fuzzy TOPSIS. Table 6 shows crisp value and table 7 shows representation in terms of fuzzy with respect to the crisp value. Rating of relative importance Table 6. Crisp value for subjective importance rating Very low (VL) Low (L) Medium (M) High (H) Very high (VH) Crisp value Rating of material performance Table 7. Linguistic rating and its triangular fuzzy number[14] Poor (P) Medium Poor (MP) Fair (F) Medium Good (MG) Good (G) Fuzzy Value [0, 1, 3] [1, 3, 5] [3, 5, 7] [5, 7, 9] [7, 9, 10] The authors discover that in this framework, the concept of entropy evaluates weight factor for each evaluation criterion. Fuzzy TOPSIS ranks the candidate materials in a fuzzy environment, are utilized to minimize personal bias and vagueness of the evaluation process. This research work has distilled a list of the material properties that are relevant to remanufacturing performance and formulated a systematic and rational methodology for evaluating the materials based on the proposed selection criteria. To conclude, the techniques used in design for remanufacturing are indexes, weighted average, grey decision making and Fuzzy TOPSIS. Strength and weakness of this method with application will be reviewed and suggest a way to overcome the lacking feature of every technique. 4. Review of Techniques Used for Decision Making in DFReM There are several methods that are being used for the field of Design for Remanufacturing. The methods are Indexes, Weighted Average, Grey Method and Fuzzy TOPSIS. Below table shows the limitation of all the method discuss in this paper. 4.1 Review on Indexes Method The advantage of scaling is that easy to use and the index can also show ranking. The process is short will save more time. However the accuracy of decision making is very low because placing index is hardly can be control. The index given can be subjective depends on decision maker s perception. Index is created according to their application. Usually the indexes are between scales from 1 to 4 representing undesired states to ideal state. Other application of several options may need higher scale. Therefore changes in scale needed and takes longer time to create and to make sure its adaptability. 4.2 Review on Weighted Average Method The advantage of this step is simple and the weightage is placed according to prioritization. The score will be multiplied to weighted average. However, the final decision can be manipulated depends on the weighted average. If alternative A is needed to be selected, therefore, the weight for alternative A should be highest or score placed higher than the rest. Weighted given are basically from decision 8

10 maker. Weighted average can be manipulated by selecting alternatives which is more than average value. This will make the option which is correct theoretically rejected due to fail achieve the average value. 4.3 Review on Grey Decision Making Method Advantage of this method is able to process uncertainty. The scale is presented in matrices form usually for every researcher who carried out Grey Decision Making. The scale is based on human s perspective. The accuracy is high because able to process uncertainty. However, the scaling can never be control and it can vary according to decision makers. Grey Decision Making is used before the condition was used determine what action should be taken. For this application either the product is good to go to customer (green), minor rework of product (yellow) or total reject (red). If the alternatives are more than three, therefore the model is no more relevant for decision making purpose. 4.4 Review on Fuzzy TOPSIS Method Fuzzy TOPSIS can process uncertainty and make the final answer to be well interpreted and easy understanding regardless if the user has low knowledge regarding the technique. Fuzzy AHP is used to calculate relative weights of each coordination criterion and then partners are ranked based on closeness coefficient, calculated for each partner using Fuzzy TOPSIS. Authors failed to calculate relative weights and instantly assume the weight. The weakness is that, this method needed to perform in every changed environment. 4.5 Suggestion of Method To counter all the lacking above, we suggest using Fuzzy Analytic Hierarchy Process (FAHP) hybrid with Artificial Neural Network in Design for Remanufacturing. This is because, to our knowledge, there is never been an implementation of hybrid computation of Fuzzy AHP and Artificial Neural Network in design for remanufacturing for cost optimization. The ANN feature has the ability to learn whereby it can counter the lacking of hardly adaptive in Fuzzy TOPSIS. The scale is hardly manipulated and must be kept in range due to consistency analysis. The scaling is performed once for all environments. The weights are determined several time, ANN will act as learning agent to adopt scale if any parameter needed to be placed as high importance. 5. Conclusions There are four design aspects that are required to optimize Design for Remanufacturing. Those are indexes, weighted average, grey decision making and fuzzy TOPSIS. Indexes and Weighted average are easily manipulated if the desired alternative is determined before the aspects were selected. The weightage and the indexes scaling needed to be change according to every changes within the environment. For grey decision making, the approach is capable to process uncertainty however, the scaling placed in matrices form is not presented such as Fuzzy AHP. This is due to the scaling is hardly can be control and depends on decision maker s perception. The same goes for Fuzzy TOPSIS but the scaling is control due to the presence of consistency and sensitivity analysis. Therefore, future recommendation, to cover the lacking from all this four techniques, we suggest using hybrid computation of Fuzzy AHP and ANN for optimization purposes. Future work, we will use hybrid computation of Fuzzy AHP and ANN to determine the highest optimization in sustainability field especially in design for remanufacturing for economy indicator. Acknowledgement We would like to thank Mybrain15 by Malaysian Ministry of Higher Education for financial support given throughout this research. References [1] B. Bras and R. Hammond, Towards Design for Remanufacturing Metrics for Assessing Remanufacturability, Proc. 1st Int. Work. Reuse, pp. 5 22, [2] P. Golinska, M. Kosacka, R. Mierzwiak, and K. Werner-Lewandowska, Grey Decision Making as a tool for the classification of the sustainability level of remanufacturing companies, J. Clean. Prod., vol. 105, pp ,

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