Ievgen V. Sidenko

Decision-Making Based on Fuzzy Estimation of Quality Level for Cargo Delivery

This chapter presents the proposed approach and algorithms for designing hierarhical decision support systems (DSS) based on fuzzy logic with flexible rule base. Special case of changing the structure of the input data’s vector for DSS in transport logistics is considered by authors. The main idea is a correction of fuzzy rule base of fuzzy DSS when different decision-makers can decrease dimension of the vector of DSS’s input coordinates according to their own priorities and criteria. Simulation results confirm the effectiveness and appropriateness of editing fuzzy knowledge bases rules for DSS which solve the problems of transport logistics.

Comparative Analysis of Evaluation Algorithms for Decision-Making in Transport Logistics

The analysis of existing methods and approaches for solving transport logistics problems was performed in this paper, particularly, for optimal choice of transport company. In the working process the complex of decision making criteria was formed and the hierarchical structure of decision support system (DSS) for corresponding tasks was made. Thereby the list of different-type methods (classical and fuzzy) for synthesis of developed DSS was defined. A comparative analysis of the application of fuzzy analytic hierarchy process and the method based on fuzzy inference was held for synthesis DSS for the optimal choice of transport company. The final results prove the effectiveness and reasonability of using fuzzy modeling in problems of transport logistics.

Fuzzy decision support systems in marine practice

The article is a review of the perspective methods and approaches to the design of fuzzy decision support systems (DSS) with the application of discrete fuzzy inference engine. The authors also developed a two-stage method for fuzzy rule base (RB) correction in the case of changing the structure of the input vector. In addition, the fuzzy DSS with a hierarchical structure for the best selection of the marine delivery company was developed. Simulation results confirm the effectiveness and feasibility of fuzzy DSS structure with variable input coordinates vector, in particular, in marine practice.

Fuzzy Decision Making System for Model-Oriented Academia/Industry Cooperation: University Preferences

This paper discusses the effective models of cooperation of universities and IT companies, as well as the hierarchic approach towards projecting certain decision making support systems (DSS) based on fuzzy logic. Special attention is paid to fuzzy DSS as an advisor in choosing the most appropriate cooperation model for a certain department of universities eager to become partners within the frames of future cooperation with a certain IT company. The article features hierarchic structure, results of rule bases and DSS software based on the approximation of fuzzy systems with discrete output. It also contains the results of imitational DSS modeling based on the elaborated DSS developing the most rational model of cooperation for a university party of the cooperation of the “University—IT company” type.

Intelligent Decision Support System for Selecting the University-Industry Cooperation Model Using Modified Antecedent-Consequent Method

This work is devoted to the analysis and selection of the most rational model of the university/IT-company cooperation (UIC) using intelligent decision support systems (DSSs) in the conditions of input information uncertainty. The modification of a two-cascade method for reconfiguration of the fuzzy DSS’s rule bases is described in details for situations when the volume of input data can be changed. Authors propose an additional observer procedure for checking the fuzzy rule consequents before their final correction. The modified method provides (a) structural reduction of the rule antecedents, (b) correction of the corresponding consequents in an interactive mode and (c) avoiding the results’ deformation in the decision making process with variable structure of input data. Special attention is paid to the hierarchically organized DSSs (with variable input vector and discrete logic output) and to design of the web-oriented instrumental tool (WOTFS-1). The simulation results confirm the efficiency and expediency of using (a) the software WOTFS-1 and (b) modified method of fuzzy rule base’s antecedent-consequent reconfiguration for the efficient selection of the rational model of academia-industry cooperation.

Multi-criteria Decision Making and Soft Computing for the Selection of Specialized IoT Platform

The task of the appropriate selection of the specialized Internet of Things (IoT) platform is very relevant today. The complexity of the selection process is due to (a) the large number of IoT platforms, which are available on the IoT services market, and (b) the variety of services and features, which they offer. In this paper, the multi-criteria decision making (MCDM) and the soft computing approaches for choosing the specialized IoT platform are considered. Authors illustrate solving MCDM problem using the linear convolution method with simple ranking approach to forming weight coefficients for criteria. MCDM methods have some limitations: (a) the need to take into account weight coefficients of the criteria; (b) the composition of the Pareto-optimal set of alternative decisions; (c) the lack of ability to change the dimension of the vector of alternatives and criteria in real time; (d) significant impact of weight coefficients that the expert determines on the result. Thus, the authors propose to use the soft computing approach, in particular, Mamdani-type fuzzy logic inference engine for selection of the specialized IoT platform. Relevant factors (reliability, dependability, safety, and security of IoT platforms) are considered as the most important ones for decision making in the IoT platform selection processes. In addition, analysis and research of the influence level of various factors on the selection of specialized IoT platform have been carried out. Special cases of choosing the specialized IoT platform with confirmation of the appropriateness of the using soft computing approach are discussed.

Two-stage method of fuzzy rule base correction for variable structure of input vector

The paper analyzes the existing methods and approaches to correcting bases of fuzzy rules of decision support systems (DSS). There was developed and implemented a two-stage method of fuzzy rule base correction for hierarchically-organized DSS with discrete logic output at variable structure of input vector. The appropriate method interactively provides reduction of database rules structure and correction of rules consequents at changing the number of input data, which enables to avoid results deformation in decision making. The feature of this method is the ability to process fuzzy input data with a high level of uncertainty, given in the form of fuzzy sets. Simulation results confirm the effectiveness and feasibility of the two-stage correction method of fuzzy rule bases of hierarchically-organized DSS developed by the authors, including the choice of models of cooperation within the consortium of the “University — IT company”.

Knowledge-Based Decision Support System with Reconfiguration of Fuzzy Rule Base for Model-Oriented Academic-Industry Interaction

In this work the current state of the problem, which consists in choice the rational model of academic-industry interaction such as “University – IT-company” is analyzed. To solve this problem it is developed and researched the intelligent decision support system (DSS) based on fuzzy logic for multi-criterion evaluation the most rational model of academic-industry interaction such as “University – IT-company” in case of changing dimension of input coordinates vector.