Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment

Sustainability assessments require the management of a wide variety of information types, parameters and uncertainties. Multi criteria decision analysis (MCDA) has been regarded as a suitable set of methods to perform sustainability evaluations as a result of its flexibility and the possibility of facilitating the dialogue between stakeholders, analysts and scientists. However, it has been reported that researchers do not usually properly define the reasons for choosing a certain MCDA method instead of another. Familiarity and affinity with a certain approach seem to be the drivers for the choice of a certain procedure. This review paper presents the performance of five MCDA methods (i.e. MAUT, AHP, PROMETHEE, ELECTRE and DRSA) in respect to ten crucial criteria that sustainability assessments tools should satisfy, among which are a life cycle perspective, thresholds and uncertainty management, software support and ease of use. The review shows that MAUT and AHP are fairly simple to understand and have good software support, but they are cognitively demanding for the decision makers, and can only embrace a weak sustainability perspective as trade-offs are the norm. Mixed information and uncertainty can be managed by all the methods, while robust results can only be obtained with MAUT. ELECTRE, PROMETHEE and DRSA are non-compensatory approaches which consent to use a strong sustainability concept, accept a variety of thresholds, but suffer from rank reversal. DRSA is less demanding in terms of preference elicitation, is very easy to understand and provides a straightforward set of decision rules expressed in the form of elementary “if … then …” conditions. Dedicated software is available for all the approaches with a medium to wide range of results capability representation. DRSA emerges as the easiest method, followed by AHP, PROMETHEE and MAUT, while ELECTRE is regarded as fairly difficult. Overall, the analysis has shown that most of the requirements are satisfied by the MCDA methods (although to different extents) with the exclusion of management of mixed data types and adoption of life cycle perspective which are covered by all the considered approaches

Spatial-Intelligent Decision Support System for Sustainable Downstream Palm Oil Based Agroindustry within the Supply Chain Network: A Systematic Literature Review and Future Research

Oil palm plantations as one of the sexiest commodities; produce a high yield of oil and fat that can be used in various sectors. The prospect of oil palm and its derivative products is good, but there are obstacles and problems faced that are mainly related to sustainability issues in oil palm plantations and its downstream process. Therefore, it is important to study the decision-making process that are needed to develop sustainable palm oil agroindustry. This paper aims at providing a comprehensive literature review for decision support system for sustainable agroindustry. Totally, 186 scientific publication articles from 2005 to 2019 were reviewed and synthesized. The reviewed articles were categorize based on the keywords of palm oil sustainability, geographic information system (GIS), and decision support system (DSS). The research gap and pointers for future research that are identified is the lack of sustainability aspect inclusion on decision-making process. We also identified the lack discussion of integrated spatial and intelligent tools through DSS for better, faster, and smarter decision-making process. In the end part of the paper, a pointer for possible future research was develop in terms of combination through spatial-intelligent system applying business analytics for sustainable agroindustry

A Framework for Detecting the Proper Multi-Criteria Decision-Making Method Taking into Account the Characteristics of Third-Party Logistics, the Requirements of Managers, and the Type of Input Data

The aim of the paper is to propose a framework for improving the process of choosing an appropriate multi-criteria decision-making (MCDM) method in the selection of a third-party logistics provider (3PLP). A systematic combining process was used, along with two literature reviews, one empirical survey and a case study. A four-step framework was proposed, starting with identifying common criteria that are harmonised to the 3PLP selection process, followed by analysing all aspects of the 3PLP selection problem and the selected MCDM methods in view of common criteria, finishing with a decision tree, divided into seven branches that orient the decision-maker towards his decisions. The paper also contributes to the theory by identifying and evaluating criteria that characterise 3PLP decision-making and by suggesting a suitable order of criteria. A numerical example was implemented to evaluate a proposed framework

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

Evaluation of Intrusion Detection System Security by Using a Hierarchy of Determinant Attributes

Intrusion detection system (IDS) has become an essential component of computer security in the organization. IDS can provide multiple benefits for the effective information security, at the same the evaluation of IDS must be concerned for the management. The overall objective of decision-making as regards information security products (for instance IDS) is to select the most effective as well as the most cost-efficient among the competitive ID systems. The paper introduces the evaluation model of existing IDS. The main attributes of competing ID systems (NIDS and HIDS) performance are analysed in a multi- criteria hierarchy with the inclusion of the main types of decision-makers and their objective as determinant factors

EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"

info:eu-repo/semantics/publishedVersio

Decision Making Analysis of Video Streaming Algorithm for Private Cloud Computing Infrastructure

The issue on how to effectively deliver video streaming contents over cloud computing infrastructures is tackled in this study. Basically, quality of service of video streaming is strongly influenced by bandwidth, jitter and data loss problems. A number of intelligent video streaming algorithms are proposed by using different techniques to deal with such issues. This study aims to propose and demonstrate a novel decision making analysis which combines ISO 9126 (international standard for software engineering) and Analytic Hierarchy Process to help experts selecting the best video streaming algorithm for the case of private cloud computing infrastructure. The given case study concluded that Scalable Streaming algorithm is the best algorithm to be implemented for delivering high quality of service of video streaming over  the private cloud computing infrastructure

Assessment of the relationship between Knowledge Management and Effectiveness of Intelligent Decision Support System (IDSS) in Iranian Banks

This study has developed a comprehensive integrated research model to determine the impacts of KM on the effectiveness of IDSS and on the quality of decisions that are made using IDSS. Partial least squares (PLS)-based structural equation modelling is employed to test the theoretical model. Results suggest that the use of KM techniques can enhance a bank’s performance if intelligent tools are integrated with the decision support system and appropriately utilized to improve decision quality