A fuzzy superiority and inferiority ranking based approach for IT service management software selection

Abstract Purpose – Information technology service management (ITSM) has become a major IT department management system in organizations. Successful implementation of ITSM depends on select adequate ITSM software. Evaluation and selection of the ITSM solution or software packages is complicated and time-consuming decision-making problem. This paper aims to present an approach for dealing with such a problem. Design/methodology/approach – This approach introduces functional, non-functional requirements and novel fuzzy out-ranking evaluation method for ITSM software selection. The presented approach breaks down ITSM software selection criteria into two broad categories, namely, functional (service strategy, service design, service transition, service operation, continual service improvement according to Information Technology Infrastructure Library V3) and non-functional requirements (quality, technical, vendor, implementation) including totally 46 selection criteria. A novel fuzzy superiority and inferiority ranking (FSIR) was developed and made applicable for ITSM software selection based on identified criteria. Findings – The proposed approach is applied to IT services company to select and acquire ITSM software, and the provided numerical example illustrates the applicability of the approach for this choice. The approach can facilitate firms to achieve suitable ITSM software and have a precise acquisition decision; however, the limitation of dependency on experts’ competence and proficiency in the both ITSM field and IT technical issues exists. Research limitations/implications – The approach can facilitate firms to achieve suitable ITSM software and have a precise acquisition decision; however, the limitation of dependency on experts’ competence and proficiency in the both ITSM field and IT technical issues exists. Practical implications – Facilitating of ITSM implementation through its handy software selection is the major impact of current research. Originality/value – A facile FSIR-based approach for software selection has been customized to contribute to the current literature in the ITSM field. Facilitating of ITSM implementation through its handy software selection is the major impact of current research

The effects of online commentary on users\u27 information processing in the context of online discussion forums

The airline industry employs advanced e-commerce technologies to retain frequent customers and attract new passengers, but it is likely that not all airlines have clear knowledge about how many gaps should be filled between the status quo and an ideal website. This study proposes an integrated model for evaluating airlines’ websites in terms of the perspectives of “marketing mix 4Ps” and “website quality”. In order to verify the practicality and usefulness of this model, an empirical study of the Taiwanese airline industry is offered to illustrate the application of the proposed model. The model first applies the DEMATEL method to cope with the interdependencies between criteria. It then converts the criteria’s cause and effect relations into a visual structural map where the ANP method can help compute the weight of criteria. Finally, it uses the modified VIKOR method to rank e-commerce websites of the five Taiwanese airlines. Overall, the results show that the Taiwanese airlines still have a great deal of room to improve their websites. This proposed model not only provides helpful information for airlines to understand their websites’ quality level, but also contributes to industrial applications in terms of providing some worthwhile recommendations for building an ideal website

A new outranking method for multicriteria decision making with complex Pythagorean fuzzy information

[EN]This article contributes to the advancement and evolution of outranking decision-making methodologies, with a novel essay on the ELimination and Choice Translating REality (ELECTRE) family of methods. Its primary target is to unfold the constituents and expound the implementation of the ELECTRE II method for group decision making in complex Pythagorean fuzzy framework. This results in the complex Pythagorean fuzzy ELECTRE II method. By inception, it is intrinsically superior to models using one-dimensional data. It is designed to perform the pairwise comparisons of the alternatives using the core notions of concordance, discordance and indifferent sets, which is then followed by the construction of complex Pythagorean fuzzy concordance and discordance matrices. Further, the strong and weak outranking relations are developed by the comparison of concordance and discordance indices with the concordance and discordance levels. Later, the forward, reverse and average rankings of the alternatives are computed by the dint of strong and weak outranking graphs. This methodology is supported by a case study for the selection of wastewater treatment process, and by a numerical example for the selection of the best cloud solution for a big data project. Its consistency is confirmed by an effectiveness test and comparison analysis with the Pythagorean fuzzy ELECTRE II and complex Pythagorean fuzzy ELECTRE I methodsPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Multi-criteria evaluation of renewable energy alternatives for electricity generation in a residential building.

The residential sector is well known to be one of the main energy consumers worldwide. The purpose of this study is to select the best renewable energy alternatives for electricity generation in a residential building by using a new integrated fuzzy multi-criteria group decision-making method. In renewable energy decision-making problems, the preferences of experts and decision-makers are generally uncertain. Furthermore, it is challenging to quantify the reel performance of renewable energy alternatives using a set of exact values. Fuzzy logic is commonly applied to deal with those uncertainties. The method proposed in this paper combines different methods. First, the Delphi method is used in order to select a preliminary set of renewable energy alternatives for electricity generation as well as a preliminary set of criteria (economic, environmental, social, etc.). Then, the questionnaire is used to study the renewable energy alternatives preferences of the residents of the residential building’. Later, the FAHP (Fuzzy Analytical Hierarchy Process) is implemented to obtain the weighs of the criteria taking into consideration uncertainties in expert's judgments. Finally, the FPROMETHEE (Fuzzy Preference Ranking Organization Method for Enrichment Evaluation) global ranking is performed in order to get a complete ranking of the renewable energy alternatives taking into account uncertainties related to the alternatives' evaluations. The originality of this paper comes from the application of the proposed integrated Delphi- FAHP- FPROMETHEE methodology for the selection of the best renewable energy alternatives for electricity generation in a residential building. A case study has validated the effectiveness and the applicability of the proposed method. The results reveal that the proposed integrated method helps to formulate the problem and is particularly effective in handling uncertain data. It facilitates the selection of the best renewable energy alternatives in a manner that is participatory, comprehensive, robust, and reliable

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Improved two-phase solution strategy for multiobjective fuzzy stochastic linear programming problems with uncertain probability distribution

Multiobjective Fuzzy Stochastic Linear Programming (MFSLP) problem where the linear inequalities on the probability are fuzzy is called a Multiobjective Fuzzy Stochastic Linear Programming problem with Fuzzy Linear Partial Information on Probability Distribution (MFSLPPFI). The uncertainty presents unique difficulties in constrained optimization problems owing to the presence of conflicting goals and randomness surrounding the data. Most existing solution techniques for MFSLPPFI problems rely heavily on the expectation optimization model, the variance minimization model, the probability maximization model, pessimistic/optimistic values and compromise solution under partial uncertainty of random parameters. Although these approaches recognize the fact that the interval values for probability distribution have important significance, nevertheless they are restricted by the upper and lower limitations of probability distribution and neglected the interior values. This limitation motivated us to search for more efficient strategies for MFSLPPFI which address both the fuzziness of the probability distributions, and the fuzziness and randomness of the parameters. The proposed strategy consists two phases: fuzzy transformation and stochastic transformation. First, ranking function is used to transform the MFSLPPFI to Multiobjective Stochastic Linear Programming Problem with Fuzzy Linear Partial Information on Probability Distribution (MSLPPFI). The problem is then transformed to its corresponding Multiobjective Linear Programming (MLP) problem by using a-cut technique of uncertain probability distribution and linguistic hedges. In addition, Chance Constraint Programming (CCP), and expectation of random coefficients are applied to the constraints and the objectives respectively. Finally, the MLP problem is converted to a single-objective Linear Programming (LP) problem via an Adaptive Arithmetic Average Method (AAAM), and then solved by using simplex method. The algorithm used to obtain the solution requires fewer iterations and faster generation of results compared to existing solutions. Three realistic examples are tested which show that the approach used in this study is efficient in solving the MFSLPPFI

Development of a Repair Technology Selection Tool for Aerospace Components

Negli ultimi anni sono state sviluppate nuove soluzioni di tipo end-life per prodotti. In particolare, ci sono opzioni di riutilizzo a recupero di valore aggiunto e opzioni di recupero dei materiali. Tuttavia di solito la selezione di tecniche di riparazione, fatta da operatori, non è ottimale, in quanto si tratta di un problema decisionale complesso a causa della presenza di diversi criteri contrastanti, quindi vi è la necessità di uno strumento o tool che aiuti il processo decisionale. La prima fase della presente tesi è iniziata con una ricerca sullo stato dell’arte della teoria del Remanufacturing e delle tecniche MDCM, per identificare il gap nella letteratura. Una volta Identificato il gap, è stato sviluppato un framework ed un algoritmo decisionale integrato basato su Fuzzy AHP e TOPSIS. Questi ultimi due metodi sono alla base del programma software sviluppato tramite Microsoft Excel VBA. Attraverso il case study, il programma e il suo algoritmo è stato testato e validato da un esperto in collaborazione con Rolls Royce plc. Questa tesi si propone di aggiungere alla letteratura un esempio di tool per la selezione di tecniche di riparazione per ogni fase del processo di riparazione. Tuttavia, questo può essere anche usato come primo passo per lo sviluppo di altri tool in altri settori o nello sviluppo di programmi che prendano in considerazione più criteri. In the last years new end life solutions have been developed for products. In particular there are value-added recovery reuse options, and material recovery options. However usually the selection of repair technique, done by operators, is not optimal as it is a complex decision making problem and it needs to consider several different and contrasting criteria, hence there is a need of a tool that aids the decision making process. The first stage of the thesis started with a significant research review on Remanufacturing and MDCM techniques, in order to identify the gap in literature. From the identified gap, a decision making framework and algorithm based on Fuzzy AHP and TOPSIS was developed, that is the basis of the software tool created through Microsoft Excel VBA. Through the case study, the tool and its algorithm has been tested and validated by an expert in collaboration with Rolls Royce plc. This thesis adds to the literature an example of tool that helps the selection of repair techniques for each stages of the repair process. However this can also be used as first step for developing other tools in other fields or in tools that take in account more criteria

Incident Prioritisation for Intrusion Response Systems

The landscape of security threats continues to evolve, with attacks becoming more serious and the number of vulnerabilities rising. To manage these threats, many security studies have been undertaken in recent years, mainly focusing on improving detection, prevention and response efficiency. Although there are security tools such as antivirus software and firewalls available to counter them, Intrusion Detection Systems and similar tools such as Intrusion Prevention Systems are still one of the most popular approaches. There are hundreds of published works related to intrusion detection that aim to increase the efficiency and reliability of detection, prevention and response systems. Whilst intrusion detection system technologies have advanced, there are still areas available to explore, particularly with respect to the process of selecting appropriate responses. Supporting a variety of response options, such as proactive, reactive and passive responses, enables security analysts to select the most appropriate response in different contexts. In view of that, a methodical approach that identifies important incidents as opposed to trivial ones is first needed. However, with thousands of incidents identified every day, relying upon manual processes to identify their importance and urgency is complicated, difficult, error-prone and time-consuming, and so prioritising them automatically would help security analysts to focus only on the most critical ones. The existing approaches to incident prioritisation provide various ways to prioritise incidents, but less attention has been given to adopting them into an automated response system. Although some studies have realised the advantages of prioritisation, they released no further studies showing they had continued to investigate the effectiveness of the process. This study concerns enhancing the incident prioritisation scheme to identify critical incidents based upon their criticality and urgency, in order to facilitate an autonomous mode for the response selection process in Intrusion Response Systems. To achieve this aim, this study proposed a novel framework which combines models and strategies identified from the comprehensive literature review. A model to estimate the level of risks of incidents is established, named the Risk Index Model (RIM). With different levels of risk, the Response Strategy Model (RSM) dynamically maps incidents into different types of response, with serious incidents being mapped to active responses in order to minimise their impact, while incidents with less impact have passive responses. The combination of these models provides a seamless way to map incidents automatically; however, it needs to be evaluated in terms of its effectiveness and performances. To demonstrate the results, an evaluation study with four stages was undertaken; these stages were a feasibility study of the RIM, comparison studies with industrial standards such as Common Vulnerabilities Scoring System (CVSS) and Snort, an examination of the effect of different strategies in the rating and ranking process, and a test of the effectiveness and performance of the Response Strategy Model (RSM). With promising results being gathered, a proof-of-concept study was conducted to demonstrate the framework using a live traffic network simulation with online assessment mode via the Security Incident Prioritisation Module (SIPM); this study was used to investigate its effectiveness and practicality. Through the results gathered, this study has demonstrated that the prioritisation process can feasibly be used to facilitate the response selection process in Intrusion Response Systems. The main contribution of this study is to have proposed, designed, evaluated and simulated a framework to support the incident prioritisation process for Intrusion Response Systems.Ministry of Higher Education in Malaysia and University of Malay