Extension of VIKOR method based on interval-valued fuzzy sets

Decision making is the process of finding the best option among the feasible alternatives. In classical multiple-criteria decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. However, if decision makers cannot reach an agreement on the method of defining linguistic variables based on the fuzzy sets, the interval-valued fuzzy set theory can provide a more accurate modeling. In this paper, the interval-valued fuzzy VIKOR method is presented, aiming at solving MCDM problems in which the weights of criteria are unequal, using interval-valued fuzzy set concepts. For application and verification, this study presents a numerical example and builds a practical maintenance strategy selection problem to verify our proposed method. Moreover, a comparison is made between the interval-valued fuzzy VIKOR and other adapted MCDM interval-valued fuzzy number-based.     

Evaluating and controlling silicon wafer slicing quality using fuzzy analytical hierarchy and sensitivity analysis

Twelve-inch wafer slicing is the most challenging process in semiconductor manufacturing yield. This study applies the fuzzy analytic hierarchy process (FAHP) not only to construct the multi-criteria decision problem and determine criteria weights, but also to assess the expected contributions of alternatives to previously defined objectives. Therefore, the fuzzy AHP-based proposed algorithm is applied to select evaluation outcomes and evaluate the precision of optimal performing machines. Additionally, a case study and the exponential weighted moving average (EWMA) control chart are presented to demonstrate and verify the feasibility and effectiveness of the proposed method. Finally, sensitivity analysis is conducted to test the stability of the priority ranking and help online engineers understand multi-criteria decision scenarios that reflect alternative future developments or different perspectives regarding the relative importance of the criteria.     

Application of fuzzy VIKOR for concept selection in an agile environment

Due to the increasing competition, modern manufacturing organisations are adopting agile manufacturing (AM) paradigm. AM enables the speedy production of customised, high-quality products in varying lot sizes. Concept selection in the context of AM is a typical multi-criterion decision making problem as it involves several agility criteria. The case organisation needs to select the best concept among five alternative designs. VIKOR (VlseKriterijumskaOptimizacija I KompromisnoResenje in Serbian, meaning multi-criteria optimization and compromise solution) has been used to select the best concept design. Based on the inputs gathered from decision makers, concept design (A3) has been found the best concept design from the perspective of agility. The result derived from Fuzzy VIKOR has been compared with fuzzy TOPSIS (technique for order preference by similarity to an ideal solution) methodology. The selected concept design has been subjected to implementation in the case organisation.     

Analysis of priority mix planning for the fabrication of semiconductors under uncertainty

Because of intensive capital investment in semiconductor manufacturing, the priority mix decision plays a critical role for effective administration. The potential value of products, efficiency in the deployment of equipment, and characteristics of cash flow are inherently difficult to be precisely defined and determined by a decision maker. The main objective in this study is therefore to construct an analytical approach for dealing with the aforementioned managerial problems under subjective judgment environments. Thus, we utilized a fuzzy analytic hierarchy process method to deal with uncertainty. Of primary concern are the criteria of product, equipment efficiency, and finance, and detailed criteria are catered to the requirement for further analysis. Finally, a priority mix of strategic alternatives represented by a priority index can be evaluated. A performance ranking of priority mixes can then be generated. The results provide guidance to select strategies for accepting orders with the consideration of manufacturing efficiency and the aspects of product, equipment efficiency and finance. The model is easily understandable and can be followed by administrators to determine the most suitable priority mix for a fabrication process.     

一种新的敏捷虚拟企业合作伙伴选择与评价方法

针对敏捷虚拟企业合作伙伴选择和评价中出现的待选企业多、信息模糊性增加等新问题，以及传统层次分析法和其他模糊层次分析法在处理类似多方案问题中出现的判断矩阵不一致性等缺陷，将层次分析法、三角模糊数理论和其他基本模糊理论相结合，提出了一种合作伙伴选择和评价的新方法。该方法采用基于三角模糊数的层次分析法求取各层次指标的权重，采用模糊关系矩阵确定各待选企业的属性值，最后归并获得评价结果。在Matlab环境开发了基于该方法的具体应用系统，并进行了实例分析计算。     

Applications of the MOORA method for decision making in manufacturing environment

To meet the challenges of global competitiveness, manufacturing organizations are now facing the problems of selecting appropriate manufacturing strategies, product and process designs, manufacturing processes and technologies, and machinery and equipment. The selection decisions become more complex as the decision makers in the manufacturing environment have to assess a wide range of alternatives based on a set of conflicting criteria. To aid these selection processes, various multi-objective decision-making (MODM) methods are now available. This paper explores the application of an almost new MODM method, i.e., the multi-objective optimization on the basis of ratio analysis (MOORA) method to solve different decision-making problems as frequently encountered in the real-time manufacturing environment. Six decision-making problems which include selection of (a) an industrial robot, (b) a flexible manufacturing system, (c) a computerized numerical control machine, (d) the most suitable non-traditional machining process for a given work material and shape feature combination, (e) a rapid prototyping process, and (f) an automated inspection system are considered in this paper. In all these cases, the results obtained using the MOORA method almost corroborate with those derived by the past researchers which prove the applicability, potentiality, and flexibility of this method while solving various complex decision-making problems in present day manufacturing environment.     

A general fuzzy TOPSIS model in multiple criteria decision making

Decision making is the process of finding the best option among the feasible alternatives. In classical multiple criteria decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. Owning to vagueness of the decision data, the crisp data are inadequate for real-life situations. Since human judgments including preferences are often vague and cannot be expressed by exact numerical values, the application of fuzzy concepts in decision making is deemed to be relevant. In this paper, we proposed the application of a fuzzy distance formula in order to compute a crisp value for the standard deviation of fuzzy data. Then, we use this crisp value of the standard deviation to normalize the fuzzy data using the distance formula again. In our normalization approach, we have enough flexibility to consider various types of fuzzy numbers (such as triangular, trapezoidal, and interval). Finally, we use the technique for order preference by similarity to an ideal solution to determine the ranking order of the alternatives. A numerical example from the literature is solved to demonstrate this applicability of the proposed model. We also compare our proposed approach with similar methods in the literature using some examples with known results and a number of randomly generated test problems. The results point to the applicability of our method and signify its effectiveness in identifying solutions.     

Extension of the ELECTRE method for decision-making problems with interval weights and data

Recent research is recognizing that multicriteria decision making should take into account the concepts of uncertainty, risk, and confidence. In some cases, precise determination of the exact value of the attributes is difficult. Consequently, to surmount this potential problem, their values are considered as fuzzy and intervals. The elimination and choice translating reality (ELECTRE) is one of the most widely used methods to rank a set of alternatives versus a set of criteria to reflect the decision maker’s preference. What is customary in almost all recent papers regarding ELECTRE is the utilization of fuzzy concepts in decision-making process. In this paper, we propose a novel algorithmic ELECTRE method which assimilates the concepts of interval weights and data for the facilitation of evaluation of a set of alternatives against a set of criteria in this situation. Finally, the executive procedure of our proposed ELECTRE method is illustrated by applying it to the problem of supplier selection.     

一类二次偏好聚合函数的多准则分类决策方法研究

求解多准则分类决策问题,虽有很多方法,但均存在一些不足。为此,提出了一种准则权系数不完全信息下的非线性多准则层次分类方法,该方法以层层递进方式对非训练集逐步完成整个分类决策过程。对于每一分类,首先通过训练集和偏好关系,建立一类二次偏好聚合函数的多准则分类优化模型;再利用基于进化策略和退火罚函数的求解算法求解优化模型,由此得到偏好聚合函数的系数等参数,计算每一方案的属于此类和不属于此类的一致性指标,进而确定方案是否属于此类。最后用实例验证了方法的可行性和有效性,得到了更贴近现实情况和科学合理的分类结果。     

An integrated design methodology based on the use of group AHP-DEA approach for measuring lean tools efficiency with undesirable output

The selection of lean tools is one of the crucial factors for decision makers and practitioners in a competitive environment. A few efforts have been made based on problem selection. Conversely, numerical studies have been done on analytical hierarchy process (AHP)–data envelopment analysis (DEA) as well as DEA-undesirable variables separately. Thus, there is a shortage of lean practitioners as well as the methods involved. The present research aims at integrating AHP and DEA with desirable and undesirable factors to evaluate the lean tools and techniques and to rank the aspect of efficacy. We suggest a logical procedure to measure the efficacy of lean tools on leanness and to prioritize them as decision makers. In this extensive research, we apply the integrated multicriteria decision-making approach, including the hybrid groups AHP and DEA models with desirable and undesirable variables, to assess the relative efficiency of lean manufacturing tools and techniques. Case studies are used to demonstrate the lean implementation in companies while being validated by a panel of experts. The integration of these approaches has created synergy and shown to be even more powerful. Thus, the proposed integrated AHP-DEA model can evaluate and rank different alternatives while considering desirable and undesirable variables in the production processes.     

Robot selection by a multiple criteria complex proportional assessment method under an interval-valued fuzzy environment

Recent research is recognizing that multiple criteria analysis should take account of the concepts of uncertainty and risk. In some cases, precise determination of the exact value of alternatives and weights of criteria is difficult. Consequently, to deal with these potential problems, their values are regarded as fuzzy and intervals. This paper proposes an interval-valued fuzzy multiple criteria complex proportional assessment (IVF-COPRAS) method that can reflect both a subjective judgment and objective information in real-life situations. In this method, the performance rating values versus selected criteria as well as the weights of conflicting criteria are linguistic variables represented by interval-valued triangular fuzzy numbers. Moreover, performances of alternatives against subjective criteria are described via linguistic variables and then transformed into interval-valued triangular fuzzy numbers. Finally, an application example for the robot selection problem is given in this paper to show this decision-making steps and the effectiveness of the proposed method in manufacturing companies.     

An optimistic robust optimization approach to common set of weights in DEA

Data envelopment analysis models allow decision making units to select the weights of inputs and outputs that are the most advantageous for calculating efficiency scores. Common set of weights make a basis for comparison and ranking all decision making units under an identical condition. In this paper, first we give the robust counterpart of the CCR model in envelopment form and show that it is the same as the optimistic robust counterpart of the multiplier form of the CCR model. Then the robust solutions for common set of weights under interval uncertainties are calculated using robust efficiency scores of units considering as ideal solutions. On two numerical examples, the performance of the new approach is compared with the approach developed by Omrani (2013) showing that it gives more reliable and better solutions.     

Manufacturing supply chain design and evaluation

Successful supply chain management calls for robust supply chain design and evaluation tools. Many published papers focused on high level strategic aspects of supply chain design and the results are usually generic guidelines for business executives rather than specific tools for plant managers. In this paper, we present a decision-based methodology for supply chain design that a plant manager can use to select suppliers. The methodology utilizes the techniques of analytic hierarchy process and preemptive goal programming. Supply chain operations reference model level I performance metrics are incorporated into the methodology as the decision criteria. In addition, a set of performance metrics is developed to evaluate the overall supply chain effectiveness, which allows direct comparison of different supply chain designs.     

A novel robot arm selection methodology based on axiomatic design principles

Many companies intend to utilize the robotic systems to improve the performance of their manufacturing systems. Since the robotic systems are complex, it is required to determine the most suitable robot arm at the beginning of the complete design process. However, due to the increase in the number of robot arm alternatives and existence of the multiple and conflicting criteria, it has become hard to the decision makers to select the appropriate robot arm for a production system. Although the traditional multiple criteria of decision-making techniques were heavily employed in the past for this problem, they were based on subjective judgments for both the alternatives and the criteria. Therefore, a methodology based on Axiomatic Design principles is proposed to help the decision maker decide the most appropriate robot arm on a scientific, systematic, and objective basis. Moreover, the proposed methodology is extended into a decision support system (DSS) through MATLAB software, to evaluate more alternatives rapidly. Both the proposed methodology and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were applied to solve a small problem, so that the techniques will be compared and the utility of the proposed approach will be revealed. Besides, the proposed DSS was applied to a real robot arm selection problem of a food manufacturing system to show its performance in evaluating several alternatives and choosing the most suitable one in an objective and quick manner.     

A visual interactive multicriteria decision analysis model for FMS design

The main goal of this work is to develop a multicriteria decision model aimed at the evaluation of FMS competing design alternatives. The model was developed to support the design team in exploring the strengths and weaknesses of each alternative, to assist the setting of priorities between conflicting manufacturing strategy components (such as flexibility and quality), to study the sensitivity of the behaviour of alternatives to changes in underlying decision situations, and finally to identify a preferred course of action. In summary, the model can be considered as a mechanism for taking into account designer's preferences and wishes, in order to customise the manufacturing system for the user's particular situation. To be effective, the computational implementation of the multicriteria FMS design model provides extensive graphical and interactive facilities both to model structuring and evaluation. Moreover, the model provides animation facilities for visual interactive sensitivity analysis. A case study illustrates the effectiveness of the decision model.     

Selecting the suitable material handling equipment in the presence of vagueness

Selection of the suitable material handling equipment (MHE) is a very difficult task for the manufacturing companies because of the considerable capital investment required. There are many tangible and intangible factors for choosing the suitable MHE. Multiple criteria decision making (MCDM) has been found to be a useful approach to analyze these conflicting factors. The evaluation of MHE alternatives within the frame of various subjective criteria and the weights of the criteria are usually expressed in linguistic terms. This makes fuzzy logic a more natural approach to this kind of problems. This paper proposes a combined MCDM methodology for evaluation and selection of MHE types for a company in the steel construction industry in Istanbul, Turkey. Fuzzy analytic network process (FANP) is utilized for assigning weights of the criteria for MHE selection and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is used to determine the most proper system alternative using the criteria weights attained by FANP. The selection is based on the compatibility between MHE and production characteristics. Objective is to select the most efficient MHE considering also the cost efficiency. The study was followed by the sensitivity analyses of the results.     

New evaluation methods for conceptual design selection using computational intelligence techniques

The conceptual design selection, which aims at choosing the best or most desirable design scheme among several candidates for the subsequent detailed design stage, oftentimes requires a set of tools to conduct design evaluation. Using computational intelligence techniques, such as fuzzy logic, neural network, genetic algorithm, and physical programming, several design evaluation methods are put forth in this paper to realize the conceptual design selection under different scenarios. Depending on whether an evaluation criterion can be quantified or not, the linear physical programming (LPP) model and the RAOGA-based fuzzy neural network (FNN) model can be utilized to evaluate design alternatives in conceptual design stage. Furthermore, on the basis of Vanegas and Labib’s work, a multi-level conceptual design evaluation model based on the new fuzzy weighted average (NFWA) and the fuzzy compromise decision-making method is developed to solve the design evaluation problem consisting of many hierarchical criteria. The effectiveness of the proposed methods is demonstrated via several illustrative examples.     

Applying TRIZ and AHP to develop innovative design for automated assembly systems

Innovative design in the development of new product and process has become the core value in most business establishments. These innovative designs are often associated with the long-established trade-off compromises or conflicting performance parameters where speed and reliability or quality and cost are readily acknowledged. The rate of change in technology and the commercial environment suggests that the opportunity for innovative design is accelerating and systematic support for innovation process is needed. This study combines the Russian theory of inventive problem solving (TRIZ) and the analytical hierarchy process (AHP) for designing the automated manufacturing systems. This study applied the contradiction matrix table, 40 innovative principles, and 39 engineering parameters to compromise the trade-off between design contradictions and engineering parameters. The design engineers can acquire more feasible solutions and inspiration through the proposed approach. However, due to vagueness and uncertainty in the decision maker’s judgment, an AHP is employed as a decision support tool that can adequately represent qualitative and subjective assessments under the multiple criteria decision making environment. Moreover, the proposed approach can help decision makers facilitate the selection and evaluation of innovative designs in the presence of intangible attributes and uncertainty. In short, the objectives of this research are to use TRIZ to propose the automated design alternatives under the innovative design consideration and to use an AHP to evaluate and select the best feasible alternative under multiple criteria. A case study of designing automated connector assembly line has been used to demonstrate the applicability of the proposed approach.     

A multi-criteria decision-making methodology on the selection of facility location: fuzzy ANP

Analytical ways to reach the best decisions are the most preferable issues in many business platforms. During the decision processes, besides the measurable variables, there exist qualitative variables, especially if the decision is based on a selection problem. Analytic hierarchy process (AHP) and analytic network process (ANP) are two of the best ways to decide among the complex criteria structure in different levels using qualitative variables. When there are interactions between the criteria in different levels of the hierarchy, then AHP cannot be used because of their one-way direction of hierarchy; the ANP has been developed for this kind of need. In this study, a fuzzy ANP method is developed for a multi-criteria facility location selection problem where the criteria set includes interactions with each other on the hierarchy structure. Besides the fuzzy ANP model development and implementation on facility location, sensitivity analysis was also originally performed to indicate the upper and lower bounds for the importance levels of alternative locations.     

Application of AHP and Taguchi loss functions in evaluation of advanced manufacturing technologies

The purpose of this study is to develop the decision model to help decision makers with their technology selection. Evaluation and selection of a new technology is a multi-criteria decision-making process encompassing various tangible and intangible factors. Thus, these factors should be identified for inclusion in the evaluation process. A thorough analysis of the impact, both positive and negative, of such factors on the organization is also required in the evaluation technique. Therefore, the first step in the development of a decision model for evaluation of technology alternatives is the identification of the pertinent factors. To accomplish this, both risks and benefits of implementing a new technology are identified for inclusion in the evaluation process. Once pertinent risks and benefits are identified, a mechanism for analysis of these factors is developed. Since these factors can be objective and subjective, a hybrid approach that applies to both quantitative and qualitative factors is used in the development of the model. Taguchi loss function is used to measure performance of each technology candidate with respect to the risk and benefit categories. Appropriate Taguchi loss functions are formulated based on the target value and the specification limits set by the decision maker. These loss functions are then used to calculate Taguchi loss scores for each technology alternative. Analytic hierarchy process (AHP) is used to determine the relative importance of the risks and benefits to the decision maker. The weighted loss scores are calculated for each technology alternative by using the relative importance as the weights. The composite weighted loss scores are then calculated and used for ranking of the technology alternatives. The technology with the smallest composite loss score is recommended for adoption. The proposed model provides guidelines for managers to make an informed decision regarding technology selection. In addition, combining Taguchi loss function and AHP provides a novel approach for ranking of the potential technology alternatives for implementation purposes.