An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The reciprocal preference relation (RPR) is a powerful tool to represent decision makers’ preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the ‘classical’ RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed

Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics

The file attached to this record is the author's final peer reviewed version.In linguistic decision making problems there may be cased when decision makers will not be able to provide complete linguistic preference relations. However, when estimating unknown linguistic preference values in incomplete preference relations, the existing research approaches ignore the fact that words mean different things for different people, i.e. decision makers have personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this paper we propose a consistency-driven methodology both to estimate the incomplete linguistic preference values and to obtain the personalized numerical meanings of linguistic values of the different decision makers. The proposed incomplete linguistic preference estimation method combines the characteristic of the personalized representation of decision makers and guarantees the optimum consistency of incomplete linguistic preference relations in the implementation process. Numerical examples and a comparative analysis are included to justify the feasibility of the PISs based incomplete linguistic preference estimation method

Multiplicative Consistency Ascertaining, Inconsistency Repairing, and Weights Derivation of Hesitant Multiplicative Preference Relations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This article investigates multiplicative consistency ascertaining, inconsistency repairing, and weights derivation for hesitant multiplicative preference relations (HMPRs). First, the completely multiplicative consistency and weakly multiplicative consistency of HMPRs are defined. Based on them, 0-1 mixed programming models and simple algebraic operations are proposed to ascertain the multiplicative consistency of HMPRs. Then, some goal programming models are developed to generate the weights from consistent HMPRs and to revise inconsistent HMPRs. An integrated procedure to manage the multiplicative consistencies of HMPRs is designed. The proposed methods are also extended to accommodate incomplete HMPRs, and to estimate missing values. Finally, some numerical examples, a comparative analysis with existent approaches, and a simulation analysis are included to illustrate the practicality and effectiveness of the developed models

Preference elicitation from pairwise comparisons in multi-criteria decision making

Decision making is an essential activity for humans and often becomes complex in the presence of uncertainty or insufficient knowledge. This research aims at estimating preferences using pairwise comparisons. A decision maker uses pairwise comparison when he/she is unable to directly assign criteria weights or scores to the available options. The judgments provided in pairwise comparisons may not always be consistent for several reasons. Experimentation has been used to obtain statistical evidence related to the widely-used consistency measures. The results highlight the need to propose new consistency measures. Two new consistency measures - termed congruence and dissonance - are proposed to aid the decision maker in the process of elicitation. Inconsistencies in pairwise comparisons are of two types i.e. cardinal and ordinal. It is shown that both cardinal and ordinal consistency can be improved with the help of these two measures. A heuristic method is then devised to detect and remove intransitive judgments. The results suggest that the devised method is feasible for improving ordinal consistency and is computationally more efficient than the optimization-based methods. There exist situations when revision of judgments is not allowed and prioritization is required without attempting to remove inconsistency. A new prioritization method has been proposed using the graph-theoretic approach. Although the performance of the proposed prioritization method was found to be comparable to other approaches, it has practical limitation in terms of computation time. As a consequence, the problem of prioritization is explored as an optimization problem. A new method based on multi-objective optimization is formulated that offers multiple non-dominated solutions and outperforms all other relevant methods for inconsistent set of judgments. A priority estimation tool (PriEsT) has been developed that implements the proposed consistency measures and prioritization methods. In order to show the benefits of PriEsT, a case study involving Telecom infrastructure selection is presented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Finding blind spots:Investigating identity data matching in transnational commercialized security infrastructures and beyond

This dissertation analyzes the interconnections between data matching technologies, identification practices, and transnational commercialized security infrastructures, particularly in relation to migration management and border control. The research was motivated by a curiosity about the intersection between identity data matching and the challenges authorities encounter when identifying individuals, especially the “blind spots” caused by incomplete data, aliases, and uncertainties. The dissertation addresses the following main research question: “How are practices and technologies for matching identity data in migration management and border control shaping and shaped by transnational commercialized security infrastructures?”The dissertation begins by presenting an overview of the literature regarding the connections between data matching technology, which is used across various sectors, and its interrelationships with the internationalization, commercialization, securitization, and infrastructuring of identification infrastructure. This overview highlights a noticeable gap in the understanding of how data matching influences the meaning of the interconnected data and shapes relationships between organizations that use it. To address this gap, Chapter 3 proposes a methodological framework for using data matching as both a research topic and a resource for answering specific sub-questions related to specific aspects of data matching.Chapter 4 emphasizes the significance of data models in information systems for categorizing individuals and establishing connections between different data models for accurate matching. The analysis of this aspect of data matching is made possible by introducing the “Ontology Explorer”, which serves as a novel method for examining the knowledge and assumptions embedded within data models. By applying this method to analyze national and transnational data infrastructures for population management, this method is shown to reveal authorities’ imaginaries on people-on-the-move. In this way, the method demonstrates the importance of data categories in data models, as they are crucial for data matching while also offering valuable insights into how authorities enact people in different ways.Following that, the dissertation investigates how identity data matching is employed to re-identify applicants within a government migration and asylum agency in The Netherlands. Chapter 5 introduces the concept of re-identification, which involves the ongoing utilization and integration of data from various sources to establish whether multiple sets of identity data pertain to a single individual. This chapter uses insights gathered from interviews with personnel from the agency to investigate the integration of data matching tools for re-identification. The chapter shows that striving to minimize data friction in re-identification through data matching can have unintended consequences and additional burdens for the agency’s personnel.Lastly, this dissertation examines the evolution of a commercial data matching system employed for identification and security, adopting a sociotechnical approach. Chapter 6 introduces heuristics that are then used to identify moments that emphasize the design contingencies of the data matching system. Through the examination of fieldwork data collected from the company that created the system, the chapter highlights the reciprocal influences between the system’s design and the actors and entities involved. The system experienced adaptive and contingent changes from a generic data matching system to a specialized tool for identification and security because of such influences. In a broader sense, the chapter brings attention to the interrelationships among software suppliers, integrators, and customers, and the circulation and use of knowledge and technology for matching identity data across organizations

An Investigation on Benefit-Cost Analysis of Greenhouse Structures in Antalya

Significant population increase across the world, loss of cultivable land and increasing demand for food put pressure on agriculture. To meet the demand, greenhouses are built, which are, light structures with transparent cladding material in order to provide controlled microclimatic environment proper for plant production. Conceptually, greenhouses are similar with manufacturing buildings where a controlled environment for manufacturing and production have been provided and proper spaces for standardized production processes have been enabled. Parallel with the trends in the world, particularly in southern regions, greenhouse structures have been increasingly constructed and operated in Turkey. A significant number of greenhouses are located at Antalya. The satellite images demonstrated that for over last three decades, there has been a continuous invasion of greenhouses on all cultivable land. There are various researches and attempts for the improvement of greenhouse design and for increasing food production by decreasing required energy consumption. However, the majority of greenhouses in Turkey are very rudimentary structures where capital required for investment is low, but maintenance requirements are high when compared with new generation greenhouse structures. In this research paper, life-long capital requirements for construction and operation of greenhouse buildings in Antalya has been investigated by using benefit-cost analysis study

Knowledge Capturing in Design Briefing Process for Requirement Elicitation and Validation

Knowledge capturing and reusing are major processes of knowledge management that deal with the elicitation of valuable knowledge via some techniques and methods for use in actual and further studies, projects, services, or products. The construction industry, as well, adopts and uses some of these concepts to improve various construction processes and stages. From pre-design to building delivery knowledge management principles and briefing frameworks have been implemented across project stakeholders: client, design teams, construction teams, consultants, and facility management teams. At pre-design and design stages, understanding the client’s needs and users’ knowledge are crucial for identifying and articulating the expected requirements and objectives. Due to underperforming results and missed goals and objectives, many projects finish with highly dissatisfied clients and loss of contracts for some organizations. Knowledge capturing has beneficial effects via its principles and methods on requirement elicitation and validation at the briefing stage between user, client and designer. This paper presents the importance and usage of knowledge capturing and reusing in briefing process at pre-design and design stages especially the involvement of client and user, and explores the techniques and technologies that are usable in briefing process for requirement elicitation