Developing A Group Decision Support System (gdss) For Decision Making Under Uncertainty

Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decisionmakers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multiparticipant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian iii Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California‘s Sacramento-San Joaquin Delta decision making problem. The implications of GDSS‘ outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-maker

Analysis of Subsea Energy Supply Systems for Improved Maintenance Using ANFIS and TOPSIS

The maintenance plan for the subsea energy supply system during the operation was optimized by firstly training the Adaptive Neuro-Fuzzy Inference System (ANFIS) model with historical data of process variables of the system such as voltage, current, power, and pressure in MATLAB software and then predicting the optimum output of the process using the trained model, which showed a good prediction of operational data after two cycles of computational analysis. The outputs from the trained model, coupled with expert opinions on historical data, were used to develop a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria algorithm to select the best maintenance strategy. The reliability-centred maintenance, with a performance score of 0.811, ranked best amongst the maintenance strategies under the studied scenario. The result shows that the procedure could be applied in condition monitoring of operational subsea energy supply systems to predict impending faults through deviation error and prevent failure by the application of an appropriate maintenance strategy. Keywords: ANFIS, TOPSIS, Maintenance plan, Fault prediction and Subsea power supply systems DOI: 10.7176/ISDE/12-2-02 Publication date: April 30th 202

Using Pythagorean Fuzzy Sets (PFS) in Multiple Criteria Group Decision Making (MCGDM) Methods for Engineering Materials Selection Applications

The process of materials’ selection is very critical during the initial stages of designing manufactured products. Inefficient decision-making outcomes in the material selection process could result in poor quality of products and unnecessary costs. In the last century, numerous materials have been developed for manufacturing mechanical components in different industries. Many of these new materials are similar in their properties and performances, thus creating great challenges for designers and engineers to make accurate selections. Our main objective in this work is to assist decision makers (DMs) within the manufacturing field to evaluate materials alternatives and to select the best alternative for specific manufacturing purposes. In this research, new hybrid fuzzy Multiple Criteria Group Decision Making (MCGDM) methods are proposed for the material selection problem. The proposed methods tackle some challenges that are associated with the material selection decision making process, such as aggregating decision makers’ (DMs) decisions appropriately and modeling uncertainty. In the proposed hybrid models, a novel aggregation approach is developed to convert DMs crisp decisions to Pythagorean fuzzy sets (PFS). This approach gives more flexibility to DMs to express their opinions than the traditional fuzzy and intuitionistic sets (IFS). Then, the proposed aggregation approach is integrated with a ranking method to solve the Pythagorean Fuzzy Multi Criteria Decision Making (PFMCGDM) problem and rank the material alternatives. The ranking methods used in the hybrid models are the Pythagorean Fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and Pythagorean Fuzzy COPRAS (COmplex PRoportional Assessment). TOPSIS and COPRAS are selected based on their effectiveness and practicality in dealing with the nature of material selection problems. In the aggregation approach, the Sugeno Fuzzy measure and the Shapley value are used to fairly distribute the DMs weight in the Pythagorean Fuzzy numbers. Additionally, new functions to calculate uncertainty from DMs recommendations are developed using the Takagai-Sugeno approach. The literature reveals some work on these methods, but to our knowledge, there are no published works that integrate the proposed aggregation approach with the selected MCDM ranking methods under the Pythagorean Fuzzy environment for the use in materials selection problems. Furthermore, the proposed methods might be applied, due to its novelty, to any MCDM problem in other areas. A practical validation of the proposed hybrid PFMCGDM methods is investigated through conducting a case study of material selection for high pressure turbine blades in jet engines. The main objectives of the case study were: 1) to investigate the new developed aggregation approach in converting real DMs crisp decisions into Pythagorean fuzzy numbers; 2) to test the applicability of both the hybrid PFMCGDM TOPSIS and the hybrid PFMCGDM COPRAS methods in the field of material selection. In this case study, a group of five DMs, faculty members and graduate students, from the Materials Science and Engineering Department at the University of Wisconsin-Milwaukee, were selected to participate as DMs. Their evaluations fulfilled the first objective of the case study. A computer application for material selection was developed to assist designers and engineers in real life problems. A comparative analysis was performed to compare the results of both hybrid MCGDM methods. A sensitivity analysis was conducted to show the robustness and reliability of the outcomes obtained from both methods. It is concluded that using the proposed hybrid PFMCGDM TOPSIS method is more effective and practical in the material selection process than the proposed hybrid PFMCGDM COPRAS method. Additionally, recommendations for further research are suggested

Assessment of Energy Systems Using Extended Fuzzy AHP, Fuzzy VIKOR, and TOPSIS Approaches to Manage Non-Cooperative Opinions

Energy systems planning commonly involves the study of supply and demand of power, forecasting the trends of parameters established on economics and technical criteria of models. Numerous measures are needed for the fulfillment of energy system assessment and the investment plans. The higher energy prices which call for diversification of energy systems and managing the resolution of conflicts are the results of high energy demand for growing economies. Due to some challenging problems of fossil fuels, energy production and distribution from alternative sources are getting more attention. This study aimed to reveal the most proper energy systems in Saudi Arabia for investment. Hence, integrated fuzzy AHP (Analytic Hierarchy Process), fuzzy VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) and TOPSIS (Technique for Order Preferences by Similarity to Idle Solution) methodologies were employed to determine the most eligible energy systems for investment. Eight alternative energy systems were assessed against nine criteria—power generation capacity, efficiency, storability, safety, air pollution, being depletable, net present value, enhanced local economic development, and government support. Data were collected using the Delphi method, a team of three decision-makers (DMs) was established in a heterogeneous manner with the addition of nine domain experts to carry out the analysis. The fuzzy AHP approach was used for clarifying the weight of criteria and fuzzy VIKOR and TOPSIS were utilized for ordering the alternative energy systems according to their investment priority. On the other hand, sensitivity analysis was carried out to determine the priority of investment for energy systems and comparison of them using the weight of group utility and fuzzy DEA (Data Envelopment Analysis) approaches. The results and findings suggested that solar photovoltaic (PV) is the paramount renewable energy system for investment, according to both fuzzy VIKOR and fuzzy TOPSIS approaches. In this context our findings were compared with other works comprehensively.This research was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. (RG-7-135-38). The authors, therefore, acknowledge with thanks DSR technical and financial support

Access network selection schemes for multiple calls in next generation wireless networks

There is an increasing demand for internet services by mobile subscribers over the wireless access networks, with limited radio resources and capacity constraints. A viable solution to this capacity crunch is the deployment of heterogeneous networks. However, in this wireless environment, the choice of the most appropriate Radio Access Technology (RAT) that can Tsustain or meet the quality of service (QoS) requirements of users' applications require careful planning and cost efficient radio resource management methods. Previous research works on access network selection have focused on selecting a suitable RAT for a user's single call request. With the present request for multiple calls over wireless access networks, where each call has different QoS requirements and the available networks exhibit dynamic channel conditions, the choice of a suitable RAT capable of providing the "Always Best Connected" (ABC) experience for the user becomes a challenge. In this thesis, the problem of selecting the suitable RAT that is capable of meeting the QoS requirements for multiple call requests by mobile users in access networks is investigated. In addressing this problem, we proposed the use of Complex PRoprtional ASsesment (COPRAS) and Consensus-based Multi-Attribute Group Decision Making (MAGDM) techniques as novel and viable RAT selection methods for a grouped-multiple call. The performance of the proposed COPRAS multi-attribute decision making approach to RAT selection for a grouped-call has been evaluated through simulations in different network scenarios. The results show that the COPRAS method, which is simple and flexible, is more efficient in the selection of appropriate RAT for group multiple calls. The COPRAS method reduces handoff frequency and is computationally inexpensive when compared with other methods such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW) and Multiplicative Exponent Weighting (MEW). The application of the proposed consensus-based algorithm in the selection of a suitable RAT for group-multiple calls, comprising of voice, video-streaming, and file-downloading has been intensively investigated. This algorithm aggregates the QoS requirement of the individual application into a collective QoS for the group calls. This new and novel approach to RAT selection for a grouped-call measures and compares the consensus degree of the collective solution and individual solution against a predefined threshold value. Using the methods of coincidence among preferences and coincidence among solutions with a predefined consensus threshold of 0.9, we evaluated the performance of the consensus-based RAT selection scheme through simulations under different network scenarios. The obtained results show that both methods of coincidences have the capability to select the most suitable RAT for a group of multiple calls. However, the method of coincidence among solutions achieves better results in terms of accuracy, it is less complex and the number of iteration before achieving the predefined consensus threshold is reduced. A utility-based RAT selection method for parallel traffic-streaming in an overlapped heterogeneous wireless network has also been developed. The RAT selection method was modeled with constraints on terminal battery power, service cost and network congestion to select a specified number of RATs that optimizes the terminal interface utility. The results obtained show an optimum RAT selection strategy that maximizes the terminal utility and selects the best RAT combinations for user's parallel-streaming for voice, video and file-download

A Decision Tool for Supplier Selection That Takes into Account Power and Performance

Companies select their suppliers to provide required performance while being successful partners. An important aspect of collaboration is the power relationship between the company and its suppliers. Although the significance of power in supplier selection is acknowledged, published work rarely includes assessment of power. An empirical study on selecting suppliers for new product developments in a major European diesel engine manufacturing company, supported by three smaller studies with electronic engineering companies, frames overall questions regarding the importance of incorporating power into supplier selection and how this might be achieved. This research proposes an approach that assesses both performance and power and integrates the assessment results by modelling the relative effects of power and performance. It positions the suppliers into six scenarios (ideal, satisfying, tolerable, unfavourable, risky and tough) which depict to what extent a supplier is ‘suitable’ to work with. A reverse analysis reviews the relationship when several suppliers appear suitable. An assessment method is developed incorporating both subjective and objective data for qualitative and quantitative criteria. It combines two decision making methods, AHP and TOPSIS, with triangular fuzzy numbers. Multiple judgements from several decision makers are synthesised. This method is adapted for performance assessment of single, group and cross-group suppliers. Weights are calculated for the criteria, and combined with calculations of supplier performance against each criterion to provide an overall assessment and supplier profile. Power is quantified against a set of power determinants and power relations (supplier dominance, buyer dominance and balanced) are determined. The effects of supplier perceptions (objective, optimistic and pessimistic) are estimated in the calculation. The proposed approach involves complex calculations and a prototype software tool is developed with graphical interfaces. The tool includes performance criteria and power determinants collected from literature and allows users to define new ones. Application to an agriculture case enables the sustainable performance of suppliers (farmers) to be evaluated and compared

Efficiency in BRICS banking under data vagueness:a two-stage fuzzy approach

This study analyzes the efficiency levels of the banking industry in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 2010 to 2014, using an integrated two-stage fuzzy approach. Very often the reliability of data collected from BRICS is questionable. In this research, we first use fuzzy TOPSIS to capture vagueness in the relative efficiency of BRICS banking over time. In the second stage, we adopt fuzzy regressions based on different rule-based systems to enhance the power of significant socioeconomic, regulatory, and demographic variables to predict banking efficiency. These variables are previously identified by using bootstrapped truncated regressions with conditional α-levels, as proposed by Wanke, Barros, and Emrouznejad (2015a). The results reveal that efficiency in the banking industry is positively associated with country gross savings and the GINI index ratio, but negatively associated with relatively high inflation ratios. Fuzzy regressions proved far more accurate than bootstrapped truncated regressions with conditional α-levels. We derive policy implications

Energy Efficient Network Selection in Heterogeneous Network using TOPSIS

Due to integration of heterogeneous wireless technology demand of seamless communication is increases. Various approaches have been proposed for network selection in vertical handoff. Here we propose a new energy efficient network selection algorithm by considering three networks (CDMA, WIMAX &WLAN) for network selection .In this work, based on the power consumption, traffic class and current battery level of each network interface card , the mobile terminal lifetime is to be calculated. As the lifetime of mobile terminal decreases, we reduce the number of attributes and networks by eliminating the particular network form network list. AHP (Analytic hierarchical process) and TOPSIS (Technique for order of Preference by similarity to the Ideal Solution) are used for network selection