Intelligent Computational Argumentation for Evaluating Performance Scores in Multi-criteria Decision Making

Multi-Criteria decision making (MCDM), is a discipline aimed at assisting multiple stakeholders in contemplating a decision paradigm in an uncertain environment. the decision analysis to be performed involves numerous alternative positions assessed under varied criterion. a performance score is assigned for each alternative in terms of every criterion, and it represents satisfaction of the criteria by that alternative. in real applications, performance scores are sometimes hard to determine, and they are often subjective. We have developed an intelligent computational argumentation approach for dealing with the problem of uncertainty in resolving the subjective scores. in this approach, an argumentation tree is developed to assess a performance score for an alternative under a criterion. the argumentation takes into consideration the strength of an argument i.e. the degree of support or attack for that argument, and priorities of stakeholders. a set of fuzzy argumentation rules in a fuzzy association matrix is used to assess the indirect impact of an argument on alternatives. Aggregation of strengths of supporting and attacking; direct and indirect arguments represent a performance score of an alternative for a criterion in the decision-making domain. a decision-making case study for developing a mine detection simulator is used to illustrate the method. © 2010 IEEE

Adding value to satisficing decisions using TOPSIS in service provider selection problems

Selection processes of credible candidates in competitions are often flawed. The flaws may be deliberate when there is corruption. In other cases the flaws occur because of the decision makers’ inadequacies. Many competitors do their best in developing exceptional proposals, but unfairness of the decision makers undermines these efforts. Ideally, undeserving candidates should be disqualified, and deserving ones be allowed to contest. Systematic methods should be used in the proposal evaluation, and the process should be verifiable. This paper discusses scientific methods proposed for use to select a criterion-based worthy competitor in service provider selection problems. The method is a technique for order preference by similarity to ideal solution (TOPSIS). TOPSIS is a mathematically-derived statistical method useful to offset the biases in the selection process. Features that address both added value and reduced costs are incorporated in the TOPSIS selection process. A numerical example is included to demonstrate TOPSIS forte

Prioritizing occupational safety and health indexes based on the multi criteria decision making in construction industries

Background and aims: Critical infrastructures are the most important sectors in nation security, public safety, socioeconomic security and environment in the world. Such incidents make people, environment and industries harmed. The occurrence of accidents is common in the basic infrastructure of all countries in the world. Building industry is inherently facing many problems and ambiguities. Events' rate of construction industry and other industries in Iran is higher than other parts of the world. The aim of this research is prioritization of indicators of health and safety in the small and large construction industry. Methods: Indexes are considered as the main body of the risk management system for all areas of activity. At first, the indicators were selected by experts from a list of detailed performance indicators, Then, these indicators according to key indicators of evaluating the performance of health and safety in the construction industry were weighted and finally ranked and modeled with the SMART (Specific, Measurable, Achievable, Realistic Timeable) criteria, using fuzzy TOPSIS method. Results: The results obtained from the analysis of fuzzy-TOPSIS based on determining the priority of safety-health indicators in both small and large groups of buildings, represented a different range of the 28-fold indicators priority. Safety of adjacent structures and using of PPE in small buildings up to six floors, had the high close rate, while the noise, safety scaffolding, excavation and trucks for reasons of height and vertical spread of structures in the large and over 6 floors buildings had the important priorities. Conclusion: Prioritizing safety and health indicators based on Fuzzy TOPSIS method with SMART criteria can be applied as a method of professionals' health assessment in the engineering field

Prioritizing occupational safety and health indexes based on the multi criteria decision making in construction industries

Background and aims: Critical infrastructures are the most important sectors in nation security, public safety, socioeconomic security and environment in the world. Such incidents make people, environment and industries harmed. The occurrence of accidents is common in the basic infrastructure of all countries in the world. Building industry is inherently facing many problems and ambiguities. Events' rate of construction industry and other industries in Iran is higher than other parts of the world. The aim of this research is prioritization of indicators of health and safety in the small and large construction industry. Methods: Indexes are considered as the main body of the risk management system for all areas of activity. At first, the indicators were selected by experts from a list of detailed performance indicators, Then, these indicators according to key indicators of evaluating the performance of health and safety in the construction industry were weighted and finally ranked and modeled with the SMART (Specific, Measurable, Achievable, Realistic Timeable) criteria, using fuzzy TOPSIS method. Results: The results obtained from the analysis of fuzzy-TOPSIS based on determining the priority of safety-health indicators in both small and large groups of buildings, represented a different range of the 28-fold indicators priority. Safety of adjacent structures and using of PPE in small buildings up to six floors, had the high close rate, while the noise, safety scaffolding, excavation and trucks for reasons of height and vertical spread of structures in the large and over 6 floors buildings had the important priorities. Conclusion: Prioritizing safety and health indicators based on Fuzzy TOPSIS method with SMART criteria can be applied as a method of professionals' health assessment in the engineering field

Dynamic fuzzy multiple criteria decision making for performance evaluation

The paper proposes a dynamic fuzzy multiple criteria decision making (DFMCDM) method. The method considers the integrated weight of the decision makers with the subjective and objective preference and the effect of time weight. In the proposed method, a mathematical programming model is used to determine the integrated weight, and a basic unit-interval monotonic (BUM) function based approach is used to calculate the time weight. In addition, a distance measure of membership function is introduced to effectively measure the degree of difference between the alternatives in the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). Finally, a numerical example is introduced to illustrate the proposed method

Evaluation of combined heat and power (CHP) systems using fuzzy shannon entropy and fuzzy TOPSIS

Combined heat and power (CHP) or cogeneration can play a strategic role in addressing environmental issues and climate change. CHP systems require less fuel than separate heat and power systems in order to produce the same amount of energy saving primary energy, improving the security of the supply. Because less fuel is combusted, greenhouse gas emissions and other air pollutants are reduced. If we are to consider the CHP system as "sustainable", we must include in its assessment not only energetic performance but also environmental and economic aspects, presenting a multicriteria issue. The purpose of the paper is to apply a fuzzy multicriteria methodology to the assessment of five CHP commercial technologies. Specifically, the combination of the fuzzy Shannon's entropy and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach will be tested for this purpose. Shannon's entropy concept, using interval data such as the α-cut, is a particularly suitable technique for assigning weights to criteria — it does not require a decision-making (DM) to assign a weight to the criteria. To rank the proposed alternatives, a fuzzy TOPSIS method has been applied. It is based on the principle that the chosen alternative should be as close as possible to the positive ideal solution and be as far as possible from the negative ideal solution. The proposed approach provides a useful technical–scientific decision-making tool that can effectively support, in a consistent and transparent way, the assessment of various CHP technologies from a sustainable point of view

Intelligent computational argumentation for evaluating performance scores in multi-criteria decision making

Multi Criteria Decision Making (MCDM) is a discipline aimed at assisting multiple stakeholders in contemplating a decision paradigm in an uncertain environment. The decision analysis to be performed involves numerous alternative positions assessed under varied criterion. A performance score is assigned for each alternative in terms of every criterion and it represents satisfaction of the criteria by that alternative. In a collaborative decision making environment, performance scores are either obtained when a consensus can be reached among stakeholders on a particular score or in some cases or controversial when stakeholders do not agree with each other about them. In the previous research an intelligent argumentation system for collaborative decision making was developed. In this thesis; its use is being extended for evaluating performance scores in MCDM. A framework is laid out for using the Intelligent Argumentation approach for resolving controversial performance scores. An application case study of Selection of a Mine Detection Simulation tool is used to illustrate the method. To validate it empirically, a case study to determine division of effort between software quality assurance and software testing, which has a group of 24 stakeholders, is conducted in a hypothetical setup. Its empirical data is collected and analyzed. The analysis serves two basic purposes: 1) to validate capability of the argumentation process in determining the controversial performance scores in MCDM using our intelligent computational argumentation system and to show its effectiveness in capturing rationales of stakeholders and assisting rapid collaborative decision making --Abstract, page iii

Intelligent Multi-Attribute Decision Making Applications: Decision Support Systems for Performance Measurement, Evaluation and Benchmarking

Efficiency has been and continues to be an important attribute of competitive business environments where limited resources exist. Owing to growing complexity of organizations and more broadly, to global economic growth, efficiency considerations are expected to remain a top priority for organizations. Continuous performance evaluations play a significant role in sustaining efficient and effective business processes. Consequently, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency of various industries. Majority of these models focus solely on quantitative criteria omitting qualitative data. However, a thorough performance measurement and benchmarking require consideration of all available information since accurately describing and defining complex systems require utilization of both data types. Most evaluation models also function under the unrealistic assumption of evaluation criteria being dependent on one another. Furthermore, majority of these methodologies tend to utilize discrete and contemporary information eliminating historical performance data from the model environment. These shortcomings hinder the reliability of evaluation outcomes leading to inadequate performance evaluations for many businesses. This problem gains more significance for business where performance evaluations are tied in to important decisions relating to business expansion, investment, promotion and compensation. The primary purpose of this research is to present a thorough, equitable and accurate evaluation framework for operations management while filling the existing gaps in the literature. Service industry offers a more suitable platform for this study since the industry tend to accommodate both qualitative and quantitative performance evaluation factors relatively with more ease compared to manufacturing due to the intensity of customer (consumer) interaction. Accordingly, a U.S. based food franchise company is utilized for data acquisition and as a case study to demonstrate the applications of the proposed models. Compatible with their multiple criteria nature, performance measurement, evaluation and benchmarking systems require heavy utilization of Multi-Attribute Decision Making (MADM) approaches which constitute the core of this research. In order to be able to accommodate the vagueness in decision making, fuzzy values are also utilized in all proposed models. In the first phase of the study, the main and sub-criteria in the evaluation are considered independently in a hierarchical order and contemporary data is utilized in a holistic approach combining three different multi-criteria decision making methods. The cross-efficiency approach is also introduced in this phase. Building on this approach, the second phase considered the influence of the main and sub-criteria over one another. That is, in the proposed models, the main and sub-criteria form a network with dependencies rather than having a hierarchical relationship. The decision making model is built to extract the influential weights for the evaluation criteria. Furthermore, Group Decision Making (GDM) is introduced to integrate different perspectives and preferences of multiple decision makers who are responsible for different functions in the organization with varying levels of impact on decisions. Finally, an artificial intelligence method is applied to utilize the historical data and to obtain the final performance ranking. Owing to large volumes of data emanating from digital sources, current literature offers a variety of artificial intelligence and machine learning methods for big data analytics applications. Comparing the results generated by the ANNs, three additional well-established methods, viz., Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), are also employed for the same problem. In order to test the prediction capability of these methods, the most influencing criteria are obtained from the data set via Pearson Correlation Analysis and grey relational analysis. Subsequently, the corresponding parameters in each method are optimized via Particle Swarm Optimization to improve the prediction accuracy. The accuracy of artificial intelligence and machine learning methods are heavily reliant on large volumes of data. Despite the fact that several businesses, especially business that utilize social media data or on-line real-time operational data, there are organizations which lack adequate amount of data required for their performance evaluations simply due to the nature of their business. Grey Modeling (GM) technique addresses this issue and provides higher forecasting accuracy in presence of uncertain and limited data. With this motivation, a traditional multi-variate grey model is applied to predict the performance scores. Improved grey models are also applied to compare the results. Finally, the integration of the fractional order accumulation along with the background value coefficient optimization are proposed to improve accuracy

The application of geographic information systems (GIS) to armed violent conflicts resolution in the Great Lakes region (GLR) of Central and East Africa.

Doctoral Degree. University of KwaZulu-Natal, Durban.Armed violent conflict is a persistent global problem, and its severity is more prominent in developing countries, including Africa. In the past decades and more recently, the GLR in east Africa has experienced various armed violent conflicts, notably the 1994 Rwandan genocide, a protracted civil war in Uganda, the Burundi ethnic conflicts, sporadic persistent cross-border ethnic conflicts in Tanzania and an unending guerrilla and civil war in the Democratic Republic of Congo (DRC). Many efforts have been made through conventional approaches, notably negotiations, peace talks, peacekeeping operations (PKO), and peace stabilization, to address these conflicts but sustainable peace remains a challenge and elusive. Most of these conventional approaches emphasize on economic and political aspects and tend to ignore the spatial component in peace talks and decisions making. GIS has been recognized as an invaluable tool in the resolution of armed violent conflicts in other parts of the world. GIS has the capability of integrating, synthesizing, and modelling spatial data, which can assist in policy and decision-making. However, GIS by itself cannot resolve any conflict, but it is a decision support system that can assist different stakeholders in sustainable peace negotiations. This study aims to explore the application of GIS to armed violent conflicts resolution in the GLR. It is built upon an array of qualitative and quantitative approaches aimed at identifying the origin and evolution of armed violent conflicts; patterns and dynamics of present conflict zones and areas that are currently not experiencing conflicts but may be prone to future armed violent conflicts in GLR in east Africa. In an attempt to trace the origin and evolution of persistent armed violent conflicts in the GLR, and the application of GIS in conflict resolution and peacebuilding, an extensive literature review was conducted. To detect past arm conflict clusters, hotspots, and areas at risk to future outbreaks of armed violent conflicts, GIS spatial analytical techniques were employed, including geocoding, autocorrelation analysis (Moran's I), Hotspot (Getis-Ord Gi*) analysis, and predictive modelling. While geocoding, cluster, and hot spot analyses were performed in ArcMap GIS software to assess the spatial distribution and patterns of armed violent conflicts in the GLR from 1998 – 2017, Microsoft Excel was used to develop a predictive Conflict Risk Model (CRM) for the probability of armed conflicts occurring from 2018 -2038. Thereafter, a conflict risk equation was developed from the CRM to predict areas at risk of future armed conflict outbreak. In response to the absence of a combined spatial data hub in the GLR, a new regional file geodatabase was created in ArcMap, ArcCatalog 10.4 using data from various referenced, survey and institutional sources. As part of a comprehensive plan to bring sustainable peace in the GLR, this study has identified the Hima –Tutsi empire ideology and the presence of mineral resources in the region as significant factors explaining the origin and evolution of persistent armed violence in the GLR. The study also highlights the application of GIS to identify and assess the spatial distribution, clusters, hot and risk spots of armed conflicts in the GLR and as a decision support tool for armed conflict resolution. From 1998-2017, armed violent conflicts were prevalent in the whole country of Burundi, eastern DRC and northern Uganda. During the same period, there was a significant clustering of armed violent conflict in the GLR at 99% confidence (p < 0.01), however eastern DRC emerged as the area with the highest armed conflicts hot spots at 99% confidence. In general, the predictive CRM analysis revealed a 66% probability of armed conflict occurring in the GLR between 2018 and 2038, with DRC predicted to be the most at risk (81%) and Tanzania the least at risk (50%). Together with the newly created regional file geodatabase, these results provide a framework for armed conflict resolution and roadmap for the possibility of sustainable peacebuilding in the GLR. Areas of future research in the GLR include the development of a geodatabase at country level, the socio-economic and environmental impact of armed conflicts in the GLR, and the development of a robust conflict risk model in the GLR and Africa as a continent. Such a robust conflict risk model including local, regional, and international stakeholders, should assist in proactively, rather than reactively identifying and managing armed violent conflicts in region