An extended compromise ratio method for fuzzy group multi-attribute decision making with SWOT analysis

Strengths, weaknesses, opportunities and threats (SWOT) matrix is one of the most used tools for strategic planning, specially in the stage of extracting strategies. While the use of SWOT analysis is quite common and popular, it still continuous to have certain structural problems such that: the lack of considering uncertain and boolean (two sided) factors, lack of prioritization of the factors and too many extractable strategies. In this paper, we deal with the analytic hierarchy process (AHP) to find weights of SWOT groups and weights of sub-factors within each groups; and we make use of the technique for order preference by similarity to ideal solution (TOPSIS) methodology to determine strategies priority. In addition, in order to deal with uncertainty and vagueness, we incorporate axiomatic fuzzy set (AFS) theory instead of traditional fuzzy set theory. AHP and TOPSIS are applied to the light of AFS theory. The advantages of dealing with AFS theory in the proposed approach are that: it can directly be applied to the data, the features of the data-sets can be mixed; however, the traditional fuzzy set theory deals with fuzzy numbers and it’s clear that different fuzzy numbers will lead to different results; hence the inconsistency of previous methodologies, described in the literature, copes with the methodology proposed in this paper.

Compromise ratio method for fuzzy multi-attribute group decision making

Trust and reputation systems represent a significant trend in decision support including selection of best match cloud providers to process Big Data. Reputation is often considered as a collective measure of trustworthiness based on the referrals or ratings from members in a community. Reputation systems have been applied in various applications such as online service provision. However, reputation models do not reflect user's quality of service (QoS) preferences and thus they might not be satisfied with the recommendations from others. In this paper, we propose a de-centralized reputation-based trust model that incorporates the user QoS preferences to select the best match Cloud Service Provider to process Big Data. Our trust model relies on three multi-attribute decision-making (MADM) methods including Simple Additive Weighting (SAW), Weighted Product Method (WPM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We conducted several experiments using simulated cloud environment to validate our trust model and assess the three MADM methods. The results show that the proposed model is pliable to users' requirements and efficiently evaluate trust of cloud providers.

A survey was undertaken for SWOT (Strength, Weakness, Opportunity and Threats) analysis of rabbit production system in Thrissur and Malappuram districts of Kerala. Sixty rabbit units were selected randomly from the rabbit farmers of the two districts. The rabbit units were classified as small (1-10 doe unit) and medium (>10 doe unit). Thirty farms from each category were selected for SWOT analysis to assess internal and external factors affecting the viability and sustainability of rabbit farms in Kerala. The SWOT factors had Kendall’s ‘W’ value ranging from 0.005 to 0.017 and 0.006 to 0.069 respectively for the small and medium rabbit farms. Based upon outcomes, most important strengths and opportunities could be combined to formulate a functional strategy that can vitalize the rabbit production systems

The hesitant fuzzy β-covering rough set offers stronger representational capabilities than earlier hesitant fuzzy rough sets. Its flexibility makes it more suitable for hesitant fuzzy multi-attribute decision-making (MADM). As a result, it has become a popular research focus in decision analysis and has drawn significant attention from scholars. However, the existing hesitant fuzzy β-covering rough set based on t-norms cannot handle the overlap and correlation between hesitant information well. Addressing this problem, we propose the hesitant fuzzy overlap function and hesitant fuzzy β-covering (I,O) rough set (HFβCIORS) models based on the hesitant fuzzy overlap function. First, we establish the definition of the hesitant overlap function and representable hesitant fuzzy overlap function on a partial order relation. Based on proposed definitions, we provide examples of representable and unrepresentable hesitant fuzzy overlap functions and offer a detailed proof to explain the unrepresentable function. Second, we construct four types of HFβCIORS models and prove some of its important properties. Thirdly, we integrate the HFβCIORS models with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method and apply them to solve MADM problems. The validity of the proposed method is demonstrated through a practical application, and its stability and effectiveness are confirmed via sensitivity and comparative analyses. Based on these validations, our method proves effective in addressing MADM problems, offering reliable decision-making support.

This paper applies a SWOT (strengths, weaknesses, opportunities and threats) analysis to groundwater resources management of a coastal aquifer in northern Greece typical of those subjected to seawater intrusion. SWOT analysis is found to be a useful managerial tool, incorporating interdisciplinary issues – engineering, scientific, legislative, economic and social – which are difficult to quantify, in order to identify the main environmental problem of the study area, which is overexploitation of the groundwater resources (and seawater intrusion as a consequence). Based on the main outcomes of the SWOT model, specific countermeasures are suggested in order to combat the over-pumping of the aquifer's resources which leads to overexploitation of the groundwater potential.

Due to the trends of energy shortage and energy price rise, energy efficiency, which was always ignored over the past decades, becomes a worldwide hot issue and also a significant challenge for most factories. Therefore, it is necessary to incorporate energy-relevant criterion as a key criterion with traditional criteria in the layout planning phase. As a multiattribute decision-making (MADM) problem, the evaluation and selection of facility layout alternatives are often difficult and time consuming since the criteria generally have different units and conflicting features. In this article, a MADM approach which incorporates the advantages of rough set theory, analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) is proposed to solve the facility layout design problem with considering both traditional layout criteria and energy relevant criteria. At first, rough set theory is integrated with AHP to determine the weights for each criterion of alternatives. Then, TOPSIS is applied to get the final alternative ranking. Besides, sensitivity analysis for both decision weights and production rates is performed, and a comparison among different decision-making approaches for the same problem is also studied to demonstrate the rationality of the final decision. Finally, a practical expanding case is studied to validate the proposed approach.

The presented work is an extensive study of the existing non-linear programming (NLP) methods in an uncertain environment of an interval-valued intuitionistic fuzzy soft set (IVIFSS) for solving multi-attribute decision-making (MADM) problems. IVIFSS is an intriguing extension of a fuzzy set (FS) involving both interval-valued intuitionistic fuzzy set (IVIFS) (which considers interval-value of both membership and non-membership elements of an intuitionistic fuzzy set (IFS)) and soft set (SS) (which gives importance to each parameter in an alternative). A comprehensive study projects that the existing NLP method in accordance with the technique for order preference by similarity to ideal solution (TOPSIS) for solving interval-valued intuitionistic fuzzy soft multi-attribute decision-making (IVIFSMADM) problems (decision-making (DM) problems in which assessment of rating of each alternative, over each character is rendered by an IVIFSS) is posing some limitations due to some mathematical incorrect assumptions and hence incorporating ambiguous results in real-life applications. Henceforth, an attempt has been made to properly understand the root cause of the posed shortcoming and suggested a new NLP method for solving IVIFSMADM problems, and also to validate this proposed NLP method a real-life problem is solved successfully.

In this paper, a quantitative multicriteria futuristic fuzzy decision hierarchy (MFFDH)-based SWOT (strengths, weaknesses, opportunities and threats) analysis has been carried out to determine priorities among significant factors related to tourism's internal and external environment. In MFFDH model, the fuzzy logic is combined with analytic hierarchy process (AHP) to overcome the uncertainty in decision making process. The utilization of MFFDH with SWOT yields analytically determined priorities for the factors included in SWOT analysis and makes them more commensurable. The hybrid method has been applied in connection with a case study on tourism industry in Agra (India) and result indicates that the factors related to tourism's internal environment (strengths and weaknesses) are relatively more important than external factors (opportunities and threats). In addition, strengths group turned out to have the highest priorities while threats group have the lowest priorities.

This article summarizes the use of SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis and subsequent action planning as a tool of self-assessment to meet CAS (Council for the Advancement of Standards in Higher Education) requirements for systematic assessment. The use of the evaluation results to devise improvements to increase the effectiveness of the program and its services fulfills Part 13: Assessment and Evaluation of the CAS standards. Professional tutors in the reading, writing, and mathematics developmental laboratories of the Learning Resource Center (LRC) of Lincoln University, Pennsylvania welcomed the use of SWOT analysis and developed an evidence-based action plan to increase the effectiveness of the program.

While South Africa has the necessary legislative and policy frameworks in housing and other sectors that could have contributed to the development of co-operative housing approach, little is done to promote it. To that end, this paper provides the SWOT (Strength, Weaknesses, Opportunities and Threats) analysis of 6 housing co-operatives in South Africa. Based on the data generated from the multi-case study conducted and the administration of self addressed questionnaires, a SWOT analysis was done in order to identify the strengths, weaknesses, opportunities and threats to the housing co-operatives, with a view to developing sustainable strategies. Results from the study shows that the housing co-operatives are faced with many threats arising from the external environment and many weaknesses arising from the inadequate internal resources and circumstances. One of the strategies proposed is that there should be formulation and implementation of appropriate policies and legislation beneficial to the housing co-operatives by the government.

Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information

Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is one of the most commonly used tools for strategic planning. It helps organizations gain a better insight of their internal and external business environment when making strategic plans and decisions. Although SWOT analysis has been widely accepted as a tool for strategic planning, the traditional method of conducting SWOT analysis does not prioritize and is likely to hold subjective views that may result in an improper strategic action. One way to make better use of the SWOT framework is to take the customer's perspective in to account which helps the companies to ensure that their perceived capabilities are also recognized and valued by their customer. This work proposes a combination of Back Propagation Neural Network based Importance-Performance Analysis (BPNN-IPA) and SWOT analysis. The 'Importance' is derived by applying BPNN on a customer satisfaction survey and 'Performance' is computed as the mean value of customer's satisfaction rating. The intersection of these two components creates a two-dimensional matrix termed, IPA matrix, which can be identified as strengths and weaknesses of the company. Base on the same principle, opportunities and threats are obtained through the IPA matrix of the company's competitor. The BPNN-IPA yields significant improvement in estimating of attribute importance compared to conventional IPA and regression-based IPA approach, thereby generating effective SWOT analysis. It is expected that this approach will provide more accurate information for strategic planning as it generates prioritized SWOT factors based on the customer's perception.

SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis is one of the most widely used strategic planning and decision making tool. However, it still has some structural problems such as the lack of prioritization of the alternatives, and too many extractable strategies. In this paper, we suggest a new approach to ranking the strategy alternatives via the SWOT analysis by making use of the Axiomatic Fuzzy Set (AFS) theory to find the best description of the alternatives and use the Evidential Reasoning (ER) approach in the light of AFS to rank the alternatives. The advantages of using the hybrid method (AFS theory and ER approach) to rank the alternatives are that: arbitrary “values in between” of the Expected Utilities are avoided; Decision Makers (DMs) do not need to give subjective judgments to the values of the degree of belief but instead, degrees of belief are directly obtained from the database via AFS theory. A comparison analysis with previous methodologies is made, and it is shown than the proposed hybrid methodology provides a far higher flexibility and effectiveness.

The increasing trend of food waste is one of the serious challenges throughout the world. The purpose of this study is to investigate the status of edible oil waste (EOW) from farm to table using the SWOT (strengths, weaknesses, opportunities, and threats) analysis in Iran (2021). First, semi-structured, interviews were conducted with 11 experts in the edible oil industry. Then a cross-sectional study was done on 40 restaurant managers through telephone interviews about the restaurant’s discarded edible oils. Finally, an online study was undertaken among 121 households regarding their waste cooking oil at home. Two categories in five themes with 20 subthemes were explored based on the participant’s perspectives. Lack of up-to-date knowledge and appropriate technology were the main reasons for making waste from farm to factory. EOW is used optimally in other industries. The results showed that 92% of the restaurants sell the EOW for poultry feeds without refinement. The majority of the households [52%] throw EOW in the garbage and 21% dump their discarded oils in the sewage. By SWOT analysis, the challenges of EOW from farm to table were identified. There is no specific policy plan for collecting and recycling EOW. The waste oil used in poultry feed without refinement can enter the human body and the possible oxidation toxicity of this waste can pose public health risks. Policymakers can use the SWOT analysis for setting laws and regulations for EOW to ensure its safe disposal and promote its use for biodiesel to provide a healthy community.