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Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users' experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce and study different kinds of fuzzy soft β-neighborhoods called fuzzy soft β-adhesion neighborhoods and to analyze some of their properties. Further, the concepts of soft β-adhesion neighborhoods are investigated and the related properties are studied. Then, we present new kinds of lower and upper approximations by means of different fuzzy soft β-neighborhoods. The relationships among our models (i.e., Definitions 3.9, 3.12, 3.15 and 3.18) and Zhang models [48] are also discussed. Finally, we construct an algorithm based on Definition 3.12, when k = 1 to solve the decision-making problems and illustrate its applicability through a numerical example. [ABSTRACT FROM AUTHOR]

The paper presents an interactive programming approach to find the compromise optimal solution of the multi-level multi-objective linear programming problem. The solving process can be divided into analysis stage and decision-making stage. In the analysis stage, an evaluation function is constructed to express the difference between the objective and ideal values. In the decision-making stage, decision maker can compare the objective values with ideal values. If there has unsatisfied objective values, decision maker can make a concession of the satisfied values to improve the unsatisfied values. When the satisfactory degree of decision maker in upper levels have been mat, the problem of lower levels will be in solving. A characteristic of the proposed method comes from its continuous interaction with the decision maker. Finally, a numerical example is solved by this algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a novel complex neutrosophic soft expert set (CNSES) concept. The range of values of CNSES is extended to the unit circle in the complex plane by adding an additional term called the phase term which describes CNSES's elements in terms of the time aspect. CNSES is a hybrid structure of soft sets and single-valued neutrosophic sets (SVNSs) defined in a complex setting where the experts' opinions are included, thus making it highly suitable for use in decision-making problems that involve uncertain and indeterminate data where the time factor plays a key role in determining the final decision. Based on this new concept we define some concepts related to this notion as well as some basic operations namely the complement, union, intersection, AND and OR. The basic properties and relevant laws pertaining to this concept such as the De Morgan's laws are also verified. Lastly, we propose an algorithm to solve complex neutrosophic soft expert decision-making problem by converting it from the complex state to the real state and subsequently provided the detailed decision steps. This study is supported by the comparison with other existing methods. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study designed to compare the processes followed by practitioners of three design methods: the algorithm of inventive problem solving, axiomatic design, and environment-based design. Prior literature has postulated the complementary nature of these design methods, and in some cases, has provided case studies of their mutual application on a design problem. However, prior studies have not focused on the detailed activities used in each method to examine the similarities and differences in the outputs of the activities. In this study, a series of three one-day and three three-day design exercises were conducted simultaneously by three international research groups, each focusing on one method. The objectives of this study were to examine the early stages of the design process that deal with macro activities: problem analysis, problem synthesis, and design evaluation and decision making. Several micro design activities were conducted within these, depending on the design method: clarification of requirements, gathering information on existing technologies, initial conceptualization of an assembly of technologies, the identification of system contradictions/coupling, and the solution of contradictions. The objectives of this comparative study were to establish, from observations of practitioners - rather than from a theoretical point of view - the differences and complementarities between the design methods. The problems presented to designers covered a range of design tasks that spanned multiple disciplines, multiple levels of openendedness/specificity of the task, and various levels of inventiveness required. The comparison showed the complementary nature of the design methods, highlighted their respective strengths, and suggested the outlines of an integrated method based on the main benefit of each. [ABSTRACT FROM AUTHOR]