Entscheiden bei unschärfe — Fuzzy decision support systeme: Springer-Verlag, Berlin, 1988, ix + 304 pages, DM45.00

Effects of increasing fuzziness on analytic hierarchy process for spatial multicriteria decision analysis

Selecting process of a machine tool has been very important issue for companies for years, because the improper selection of a machine tool might cause of many problems affecting negatively on productivity, precision, flexibility and company’s responsive manufacturing capabilities. On the other hand, selecting the best machine tool from its increasing number of existing alternatives in market are multiple-criteria decision making (MCDM) problem in the presence of many quantitative and qualitative attributes. Therefore, in this paper, an analytic hierarchy process (AHP) is used for machine tool selection problem due to the fact that it has been widely used in evaluating various kinds of MCDM problems in both academic researches and practices. However, due to the vagueness and uncertainty on judgments of the decision-maker(s), the crisp pair wise comparison in the conventional AHP seems to insufficient and imprecise to capture the right judgments of decision-maker(s). That is why; fuzzy number logic is introduced in the pair wise comparison of AHP to make up for this deficiency in the conventional AHP. Shortly, in this study, an intelligent approach is proposed, where both techniques; fuzzy logic and AHP are come together, referred to as fuzzy AHP. First, the fuzzy AHP technique is used to weight the alternatives under multiple attributes; second Benefit/Cost (B/C) ratio analysis is carried out by using both the fuzzy AHP score and procurement cost, of each alternative. The alternative with highest B/C ratio is found out and called as the ultimate machine tool among others. In addition, a case study is also presented to make this approach more understandable for a decision-maker(s).

Purpose Since the demand for energy has dramatically increased in the countries which have fast-growing population and economy, they have faced with a critical problem of how to evaluate a set of potential energy sources (i.e. nuclear, natural gas, bio, geothermal, hydro, wind and solar) and choose the ultimate energy source for their needs. On the other hand, this critical problem turns into a multiple-criteria decision-making (MCDM) in the presence of a set of energy source alternatives and evaluation criteria. In literature, there are many MCDM methods introduced to solve for different kinds of problems. The purpose of this paper is to present an integrated approach for evaluating energy sources using fuzzy AHP and GRA, with a case for Turkey. Design/methodology/approach In this paper, the analytic hierarchy process (AHP) and grey relational analysis (GRA) methods are used because of their advantages for similar problems. On the other hand, due to the fact that the conventional AHP by a nine-point scale and GRA method using a scale with crisp values can be unable to handle to capture the right judgments of a decision-maker(s), to reflect the vagueness and uncertainty on the judgments of a decision-maker, the fuzzy logic is integrated with the AHP and GRA. Findings The contributions of the paper to the literature are given in two dimensions as follows: it presents an integrated approach for complex decision processes with subjective data or vague information; the proposed approach, the fuzzy AHP-GRA method for energy source selection, is unique for the related problem in literature. The results of the proposed model from the case of Turkey will help practitioners and experts of how to apply it to the similar problems in the field of energy management. Research limitations/implications In short, in this paper, an integrated approach is proposed through the fuzzy AHP and the fuzzy GRA methods. As the fuzzy AHP is used to determine the weights of evaluation criteria, the fuzzy GRA is used to rank energy source alternatives. Practical implications In addition, a case study for Turkey is presented to show the applicability of the proposed approach for potential practitioners who are authority in the field of energy in public and private sectors. Social implications On the other hand, the proposed approach, the fuzzy AHP-GRA for energy source selection can also be an intelligent tool for public and private energy companies in Turkey, as well as others in the world. Originality/value On the other hand, in this paper, to the best of the authors’ knowledge, the study contributes to the literature that the first time, they use the fuzzy alpha-cut AHP and GRA in fuzzy environment for energy source evaluation problem.

Determining road handling priority is considered as a complicated multicriteria decision making problem. In so doing, the Analytic Hierarchy Process (AHP) has been widely used to weight the importance. Fuzziness and vagueness however, are typical in many decision-making problems, so that fuzzy sets could be integrated with the pairwise comparison as an extension of the AHP. This study uses Fuzzy Analytic Hierarchy Process (FAHP) and TOPSIS method in determining regencial road handling priority for road links under severe circumstances in Badung regency in Bali province. Data are taken from a previous study, which had also been conducted for Badung regencial road handling priority using the AHP and SK.NO.77/KPTS/Db/1990 method. The weights of main and sub criteria are determined using FAHP and subsequently the ranking of road links is determined using TOPSIS method. The AHP method gave somewhat different result to 'SK.NO.77/KPTS/Db/1990' method. On the other hand, FAHP and TOPSIS method produce the same result to 'SK.NO.77/KPTS/Db/1990' method. This is probably best explained by the fact that they similarly considered traffic volumes as the most significant factor. FAHP and TOPSIS method however, are preferred to the AHP and SK.NO.77/KPTS/Db/1990 method in determining regencial road handling priority in Badung regency.Abstrak. Prioritas penanganan jalan merupakan salah satu tugas berat dan penting yang dihadapi oleh pengambil keputusan pada pemerintah daerah. Pada kenyataannya, penentuan penanganan jalan dapat dilihat sebagai permasalahan pengambilan keputusan yang melibatkan banyak kriteria yang bersifat kompleks. Metode proses hirarki analitik (AHP) telah banyak digunakan untuk menentukan bobot kriteria di dalam penentuan prioritas penanganan jalan. Akan tetapi karena keragu-raguan merupakan hal yang lazim terjadi di dalam pengambilan keputusan, maka teknik fuzzy dapat dikombinasikan ke dalam metode AHP. Pada studi ini penentuan prioritas penanganan jalan kabupaten untuk kondisi rusak berat di Kabupaten Badung, Bali dilakukan dengan metode Fuzzy AHP (FAHP) dan TOPSIS. Data penelitian digunakan dari studi sebelumnya di Kabupaten Badung yang menggunakan metode AHP dan SK.NO.77/KPTS/Db/1990. Metode FAHP digunakan untuk pembobotan kriteria sedangkan metode TOPSIS digunakan untuk penentuan urutan ruas jalan yang akan mendapat penanganan. Metode AHP memberikan hasil yang sedikit berbeda dengan metode SK.NO.77/KPTS/Db/1990. Sementara itu FAHP dan metode TOPSIS memberikan hasil yang sama dengan metode SK.NO.77/KPTS/Db/1990. Hal ini kemungkinan karena kedua metode tersebut menggunakan volume lalu lintas sebagai faktor yang paling berpengaruh pada penelitian ini. FAHP dan metode TOPSIS lebih disarankan untuk digunakan di dalam penentuan prioritas penanganan jalan di Kabupaten Badung.

ABSTRACTThe selection process for the best computer-aided manufacturing (CAM) software among a set of potential number of alternatives in market has been critical issue for most companies that aim to make their design/manufacturing-related activities automated. Because this selection process is very vital for companies because a wrong decision might put them into a difficult position in terms of economical, market share and time spent. Therefore, today’s companies have used different multiple-criteria decision making (MCDM) methods for the evaluation study of CAM software alternatives to make this complex process easily applicable and not time-consuming. Among the MCDM methods in literature, the analytic hierarchy process (AHP) method has been widely used for various MCDM problems in both academic researches and industrial practices. However, in some cases, because of the vagueness and uncertainty on the judgments of decision maker(s), the crisp pair wise comparison in the conventional AHP seems to be insufficient and imprecise to capture the right judgments of decision maker(s). Therefore, a fuzzy logic method is integrated in the pair wise comparison of AHP to make up for this deficiency in the conventional AHP, called as fuzzy AHP. Moreover, the proposed approach is also realized on a case study.

The evaluation process of conceptual design alternatives in a new product development environment is a critical point for companies who operate in fast-growing markets. Various methods exist that are able to successfully carry out this difficult and time-consuming process. One of these methods, the Analytic Hierarchy Process (AHP) has been widely used to solve multiple-criteria decision-making problems (i.e., concept evaluation, equipment selection) in both academic research and in industrial practice. However, due to vagueness and uncertainty in the decision-maker's judgment, a crisp, pair-wise comparison with a conventional AHP may be unable to accurately capture the decision-maker's judgment. Therefore, fuzzy logic is introduced into the pair-wise comparison in the AHP to compensate for this deficiency in the conventional AHP. This is referred to as fuzzy AHP. In this paper, a fuzzy AHP method is used to reduce a set of conceptual design alternatives by eliminating those whose scores (or weights) are smaller than a predetermined constant value obtained under certain circumstances. Then, simulation analysis is integrated with the fuzzy AHP method, and the hybrid method is used to help the decision-makers (product engineers or managers) evaluate the remaining alternatives from the fuzzy AHP method. A real-life manufacturing system is used as the testbed for the proposed techniques. Finally, the results of both techniques, fuzzy AHP and simulation, are used for Preference Ratio analysis to reach to the final alternative.

Recent developments in cutting tool materials of CNC maching have brought many exciting changes in the field of metal cutting . Machine operators can benefit from wider selection of insert grade in cutting process. On the other hand, selecting the optium insert grade from its increasing number of existing alternatives in workshop is multiple-criteria decision making (MCDM) problem in the presence of many quantitative and qualitative attributes. Therefore,in this paper, a fuzzy AHP (analytic hierarchy process) is used for machine tool selection problem. Due to the crisp pairwise comparison in the conventional AHP seems to be imprecise to capture the right judgments on vagueness and uncertainty machine tool selection decision-maker, fuzzy logic is introduced in the pairwise comparison of AHP to improve this conventional AHP deficiency. Finally a selected optium grade coincides with practical application in workshop.

In this paper, a new analytic hierarchy process (AHP) is proposed to determine the importance weights of customer requirements (CRs) in quality function deployment (QFD) for customer-oriented product design. The new approach combines conventional and fuzzy AHP. It takes into account one’s uncertainty in comparing different pairwise CRs to improve the imprecise rankings in conventional AHP. By employing semi-fuzzy matrices, it guarantees that the final pairwise comparison matrices based on fuzzy scales are positive reciprocal. The problem of imprecise pairwise comparisons in conventional AHP is ameliorated and more accurate results are provided. Finally, a case study of new sports earphones design is given as an example to illustrate this approach.

Shipping logistics is one of the very important criteria which can directly and indirectly affect the economy and GDP of any country. Shipping logistics depends on various factors which have been addressed by several authors in their previous studies. Studies in this literature are focused on selecting the most impactful factors among all the criteria. Methods used in this literature are fuzzy Analytical hierarchy process (AHP) and fuzzy Technique for order of Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision analysis. These methods also helped in this literature to develop a new hybrid method “fuzzy TOPSIS AHP”. There have been no studies involving maritime logistics with comparative analysis of multi-criteria decision making i.e., fuzzy AHP and fuzzy TOPSIS AHP. The literature involved large number of expert opinions on the factor prioritization of maritime logistics. Factors selected for prioritization are Environmental Sustainability, Supply and Demand, Operations and Port Selection. However, the research showed that the comparative analysis of the results was quite opposite to one another and proposed a new way for researchers to use the hybrid method of fuzzy TOPSIS AHP method in future research. The study aimed to improve the existing maritime model which can help professionals to get connected with the maritime logistics firms. The study also aims to contribute this model for researchers in their study related to maritime logistics.

Effective disaster management heavily relies on accurate flood susceptibility mapping. The fuzzy analytic hierarchy process (FAHP) is adept at considering the imprecise nature of decision-making criteria. This study assesses FAHP's effectiveness in flood susceptibility mapping, comparing it with the conventional analytic hierarchy process (AHP). By using Geographic Information System-analyzed remotely sensed data, the research systematically evaluates flood risk southeast of Algiers. Various datasets, including Digital Elevation Model, slope, precipitation, and land use maps, were collected via remote sensing. A linear fuzzy membership function transformed the data into fuzzy values. AHP determined the importance of each dataset, with calculated weights multiplied by corresponding fuzzy values. Fuzzy analysis combined these characteristics into a five-category flood risk map, verified with Google Earth and satellite images. Results indicate a high potential for flood hazard mapping, categorizing 30% of frequently flooded regions as high risk. Maps reveal north basin areas are more flood prone due to excessive precipitation, and urban areas in floodplains are vulnerable. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) assessments demonstrate AHP and FAHP's effectiveness. AUC values of 88.40 and 92% indicate that both models accurately predict flood-prone areas. FAHP excels, reducing subjectivity and ambiguity in human judgments.

Performance analysis of fuzzy analytic hierarchy process multi-criteria decision support models for contractor selection

Comparing non-structural fuzzy decision support system and analytical hierarchy process in decision-making for construction problems

In the Quality Function Deployment (QFD) process, determining the importance weights for the customer requirements is an essential and crucial process. The Analytic Hierarchy Process (AHP) has been used to determine the importance weights for product planning, but this has occurred mainly in crisp (non-fuzzy) decision applications. However, human judgment on the importance of customer requirements is always imprecise and vague. To make up for this deficiency in the AHP, a fuzzy AHP with an extent analysis approach is proposed to determine the importance weights for the customer requirements. In the method, triangular fuzzy numbers are used for the pairwise comparison of a fuzzy AHP. By using the extent analysis method and the principles for the comparison of fuzzy numbers, one can derive the weight vectors. The new approach can improve the imprecise ranking of customer requirements inherited from studies based on the conventional AHP. Furthermore, the fuzzy AHP with extent analysis is simple and easy to implement to prioritize customer requirements in the QFD process compared with the conventional AHP. This paper uses an example of a hair dryer design to illustrate the proposed approach.

This chapter discusses the implementation of the enhanced fuzzy Analytic Hierarchy Process (AHP), which is the improved version of the fuzzy AHP discussed in Chapter 3 for the determination of importance of customer requirements. Similar to the latter, the enhanced fuzzy AHP converts the linguistic assessment of customer requirements to triangular fuzzy numbers, which are used to build the pairwise comparison matrix of AHP. Then the enhanced fuzzy AHP uses the analysis method and the principles of comparison of fuzzy numbers to derive weight vectors. This improves the hitherto imprecise ranking of importance weights of customer requirements inherited from the previous works which used the conventional AHP and the fuzzy AHP discussed in Chapter 3. The enhanced fuzzy AHP with analysis refers to the to which an object satisfies a goal and where satisfied extent is defined by means of triangular fuzzy numbers. The weight vectors of the fuzzy AHP can be calculated using analysis and the principles of comparison of fuzzy numbers. Compared to eigenvectors which are used to calculate weight vectors in the conventional AHP, the enhanced fuzzy AHP is simple and easy to implement for the purpose of prioritizing customer satisfaction of quality function deployment (QFD). A case study of a hair dryer design is used to illustrate the effectiveness of the enhanced fuzzy AHP.

Rapid identification of groundwater sources is crucial for emergency water supplies. Yudu County (YDC) in Southern China serves as a case study due to its typical mountainous terrain and pressing groundwater demands. To address the limitations of conventional groundwater mapping methods in large-scale areas with sparse data, this study integrates remote sensing (RS), geographic information systems (GIS), and multi-criteria decision analysis (MCDA) techniques to delineate groundwater potential zones (GWPZs) in YDC. Following a series of correlation tests, seven assessment indicators were selected from various groundwater influencing factors, including two innovative ones: terrestrial water storage change (TWSC) and spring flow. The analytic hierarchy process (AHP) and fuzzy AHP (FAHP) models were employed to calculate factor weights, and GWPZ maps were generated using weighted overlay analysis in GIS. The model performance was validated using borewell data, receiver operating characteristic (ROC) curves, and yield prediction models. Additionally, four water enrichment types and their spatial distribution were identified by field investigations and yield prediction assessments. Results indicated a remarkable similarity between GWPZs delineated by AHP and FAHP, categorized into five classes: very high (13.92% for AHP and 14.33% for FAHP), high (26.29 and 27.55%), medium (29.33 and 28.14%), low (20.66 and 21.50%), and very low (9.80 and 8.48%). The area under the curve (AUC) for FAHP was 85.09%, slightly higher than the 84.41% of AHP, while the correlation coefficient (R2 of the prediction model improved from 0.747 to 0.817 with FAHP. These findings confirmed the reliability of combining GIS and MCDA methods to delineate GWPZs, with FAHP demonstrating an advantage over AHP. The proposed methodology and resulting mapping significantly enhance sustainable water resource management and development in YDC, offering a practical framework for rapid groundwater investigations in disaster response, as well as for long-term water security planning in similar mountainous environments.