Multiple-criteria Decision-making Process Research Articles

Bottled water quality ranking via the multiple-criteria decision-making process: a case study of two-stage fuzzy AHP and TOPSIS.

Access to healthy drinking water is vital to human health and development. Bottled water consumption has been on the rise in recent years. As several chemical and bacteriological parameters affect bottled water quality, it is difficult to choose the highest-quality bottled water. Numerous studies have proposed the use of multiple-criteria decision-making (MCDM) methods to overcome this problem. Herein, the two-stage fuzzy analytic hierarchy process (FAHP) and technique for order preference by similarity to ideal solution (TOPSIS) method were adopted to rank different brands of bottled water. The FAHP approach allows working at the intervals of judgment rather than absolute values. TOPSIS is a technique for ordering performance based on its similarity to the ideal solution. An expert panel selected and classified the criteria and sub-criteria. A pairwise comparison questionnaire was then developed, and the weights of the criteria and sub-criteria were assigned by water quality experts. The data on the quality of different brands of water were collected from the Iranian bottled water database. The final data analysis and weight determination of each parameter were performed in Excel and R software Programs. Finally, the CCi (value of closeness coefficient) and rank of 71 bottled water brands were calculated, and the best brand was introduced. Among the selected criteria, carcinogenic chemical compounds with the weight of 0.368 were the most important compound in ranking bottled water brands, followed by bacteriologic, pathogenic chemical compounds, chemical compounds important in terms of toxicity, nutritious chemical compounds with a low toxicity level, chemical compounds related to esthetic effects, and chemical compounds without health effects, respectively.

Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making

Most of the existing studies investigate the robot selection problem (RSP) in a multiple criteria decision making (MCDM) manner, from the viewpoint of a single person. This contradicts the reality that the robot selection decision is usually made by a committee or a group of experts with different expertise and concerns. For this reason, this paper proposes a group decision making (GDM) methodology for handling multiple criteria robot selection problem (MCRSP), the working process of which is (i) identifying experts, (ii) implementing the standard MCDM process and (iii) achieving a group consensus. Four objective weight determination methods, namely, Shannon entropy, CRITIC, ideal point and distance-based, are proposed to represent four experts. Experts play the role of think tank in supporting the decision maker who is responsible for MCRSP. In light of that the preference among different experts is uncertain, stochastic multicriteria acceptability analysis is then applied to achieve a holistic evaluation results for identifying good compromise choices. Two illustrative examples are presented to demonstrate the effectiveness and validity of our methodology, and compare the results with those obtained through VIKOR and ELECTRE II.

A low-carbon-orient product design schemes MCDM method hybridizing interval hesitant fuzzy set entropy theory and coupling network analysis

The product low-carbon design is the key to decrease carbon emissions in manufacturing. The multiple criteria decision-making (MCDM) method has been widely used in solving the design schemes preference choosing problems. However, the existed MCDM method has two primary problems facing the product low-carbon design cases: (i) How to clarify the coupling relationship between low-carbon decision criteria? (ii) How to fuzzily express the low-carbon-orient product design schemes? To solve these problems, we proposed a novel MCDM method for product low-carbon design. It combines the coupling network analysis with the interval hesitant fuzzy set entropy theory into MCDM process. We used a case study of injection molding machine low-carbon design to verify the proposed MCDM method. It turns out that the proposed MCDM method can help us make more rational and equitable decisions among alternative low-carbon schemes.

Insights into tolerability constraints in multi-criteria decision making: Description and modeling

A perfect combination of fact-based and value-based judgments is what multiple criteria decision-making (MCDM) issues pursue. Tolerability constraints, an important part of value-based judgments, are easily observable and have drawn attention in theory. However, their practical applications in the MCDM process are still in their infancy. This paper aims to fill the gap between theory and application. A framework for tolerability constraints in the context of non-independent criteria is proposed and operated for the first time. In this framework, global tolerability constraints on the overall decision-making process and local constraints on a single criterion are considered at the same time. Furthermore, mandatory/ sufficient requirements are regarded as different aspects of tolerability constraints. Two cases are given to illustrate the application of the proposed framework. In short, the proposed framework deepens the understanding of MCDM issues and extends its application, especially with respect to tolerability requirements.

Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis

Emergency departments (EDs) have an important position in healthcare system environment because of their serviceability to the patients with several illnesses and injuries at all hours of the day and night. Therefore, efficient management of these departments by executives and service providers is only possible with effective tools such as simulation. The concept of multiple criteria decision-making (MCDM) is applicable by computer simulation for decision analysis and performance evaluation of an ED. This study proposes an integration of computer simulation, MCDM methods of interval type-2 fuzzy analytic hierarchy process (IT2FAHP), and ELECTRE for an ED system in a university hospital. Due to the high uncertainty in ED operations, interval type-2 fuzzy sets (IT2FSs) are used in MCDM process. Consequently, the integrated method can be used as a useful tool to evaluate the ED performance and select the best scenario considering different numbers of nurses and doctors for three shifts. [Received 30 July 2013; Revised 9 February 2015; Accepted 14 June 2015]

Application of risk-based multiple criteria decision analysis for selection of the best agricultural scenario for effective watershed management

Effective watershed management requires the evaluation of agricultural best management practice (BMP) scenarios which carefully consider the relevant environmental, economic, and social criteria involved. In the Multiple Criteria Decision-Making (MCDM) process, scenarios are first evaluated and then ranked to determine the most desirable outcome for the particular watershed. The main challenge of this process is the accurate identification of the best solution for the watershed in question, despite the various risk attitudes presented by the associated decision-makers (DMs). This paper introduces a novel approach for implementation of the MCDM process based on a comparative neutral risk/risk-based decision analysis, which results in the selection of the most desirable scenario for use in the entire watershed. At the sub-basin level, each scenario includes multiple BMPs with scores that have been calculated using the criteria derived from two cases of neutral risk and risk-based decision-making. The simple additive weighting (SAW) operator is applied for use in neutral risk decision-making, while the ordered weighted averaging (OWA) and induced OWA (IOWA) operators are effective for risk-based decision-making. At the watershed level, the BMP scores of the sub-basins are aggregated to calculate each scenarios’ combined goodness measurements; the most desirable scenario for the entire watershed is then selected based on the combined goodness measurements. Our final results illustrate the type of operator and risk attitudes needed to satisfy the relevant criteria within the number of sub-basins, and how they ultimately affect the final ranking of the given scenarios. The methodology proposed here has been successfully applied to the Honeyoey Creek-Pine Creek watershed in Michigan, USA to evaluate various BMP scenarios and determine the best solution for both the stakeholders and the overall stream health.

Selection of dispatching rules with fuzzy ANP approach

Nowadays, the requirement of flexibility in manufacturing has been increased rapidly because of severe market competition in which customer demand should be met in a right time and in a right way. Although there can be many reasons which affect productivity of the flexible manufacturing system (FMS), one of the crucial one is a scheduling policy which depends upon dispatching rules selection. Selection of a best dispatching rule can only be based on one or two criteria such as processing time, due date, or manufacturing system information in traditional methods such as mathematical programming, simulation, and heuristic algorithms. In addition, traditional methods have some weakness such as the following: first one is that dispatching rules do not allow the use of multiple criteria for evaluating process, second one is related with not considering most of the manufacturing system information, and the last one deals with selection decision not being a dynamic structure. In this study, an analytical network process model based on benefit, opportunity, cost, and risk has been developed in order to eradicate these weaknesses. The model is based on a multiple-criteria decision-making process which contains different performance criteria, details of FMS information, a company's strategic criteria, and different well-known dispatching rules. In addition, that fuzziness of information was also considered in the evaluation process.

Integration of the surrogate worth trade-off method and the analytic hierarchy process

The field of multiple-criteria decision making (MCDM) has developed into one of the fastest growing fields in the last ten years. This paper outlines the MCDM process and then presents two of the more well-known methods: the analytic hierarchy process and the surrogate worth trade-off method. These methods will be presented in terms of the types of problems for which they are best suited along with advantages and disadvantages possessed by each. Finally, an integrated approach will be proposed which attempts to capture the advantages of both methods in order to develop one robust solution strategy.