No abstract

In the last 20 years, rapid and significant developments have occurred in communication and information technologies. In parallel with these developments, the importance of smartphones has increased. In addition, many smartphone manufacturers have launched and continue to launch a number of new models with many features. People who want to buy a new smartphone have difficulties selecting the best smartphone among the numerous models available on the technology markets. Therefore, smartphone selection has become a complex multi-criteria decision-making (MCDM) problem for people. Hence, decision-making processes will be facilitated by using MCDM methods, and these will provide the most appropriate decision. In this paper, the best smartphone among the 28 alternatives determined by the person who will buy them are selected by using three main criteria and 17 sub-criteria with the help of a two-phased MCDM approach. In the first phase, 28 smartphone alternatives are ranked using the analytic network process (ANP). In the second phase, a model that includes the best four alternatives of ANP is created. Afterwards, the best smartphone is selected using the generalised Choquet integral (GCI) method according to this model. Finally, the findings and the results are given. OPSOMMINGGedurende die afgelope twintig jaar het daar vinnige en noemenswaardige ontwikkeling in die kommunikasie en informasie tegnologie geskied. In parallel hiermee het die belangrikheid van slimtelefone toegeneem. Daarmee saam stel slimtelefoon-vervaardigers gereeld nuwe modelle met nuwe funksies vry. Dit is dus moeilik vir 'n potensiële kliënt om die beste seleksie uit die groot verskeidenheid tot hul beskikking te maak. Slimtelefoonseleksie is 'n ingewikkelde multi-kriteria besluitnemingsprobleem. Die besluitnemingsproses word dus gefasiliteer deur gebruik te maak van multi-kriteria besluitnemingsmetodes. Hierdie artikel bepaal die beste slimtelefoon vanuit agt-en-twintig alternatiewe deur gebruik te maak van drie hoof kriteria en sewentien subkriteria met die hulp van 'n twee-ledige multi-kriteria besluitnemingsbenadering. Eerstens word die agt-entwintig alternatiewe met behulp van die analitiese netwerk proses gerangskik en daaruit word 'n model, wat uit die vier beste alternatiewe bestaan, geskep. Laastens word die beste slimtelefoon gekies deur die veralgemeende Choquet integraal metode op die model toe te pas.

In this article, a new fitness assignment scheme to evaluate the Pareto-optimal solutions for multi-objective evolutionary algorithms is proposed. The proposed DOmination Power of an individual Genetic Algorithm (DOPGA) method can order the individuals in a form in which each individual (the so-called solution) could have a unique rank. With this new method, a multi-objective problem can be treated as if it were a singleobjective problem without drastically deviating from the Pareto definition. In DOPGA, relative position of a solution is embedded into the fitness assignment procedures. We compare the performance of the algorithm with two benchmark evolutionary algorithms (Strength Pareto Evolutionary Algorithm (SPEA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2)) on 12 unconstrained bi-objective and one tri-objective test problems. DOPGA significantly outperforms SPEA on all test problems. DOPGA performs better than SPEA2 in terms of convergence metric on all test problems. Also, Pareto-optimal solutions found by DOPGA spread better than SPEA2 on eight of 13 test problems.

Depending on today's emerging technology, manufacturers produce various automobile models. It is a complicated situation to select the best automobile from various brands and models. It is difficult to select an automobile by using many criteria. Using multi-criteria decision making methods in the solution of many problems such as the automobile selection which has many criteria eliminates the complexity and contributes to the solution of the problem. Fuzzy TOPSIS method is used to obtain the best result in a decision problem. In this study, fuzzy TOPSIS method is used to solve the best automobile selection problem of a person who plans to buy an automobile from three alternative automobiles in the same segment. The criteria of the best automobile selection problem are determined by searching the literature and by considering the opinions of the person who plans to buy the automobile. The alternative automobiles are evaluated by three experts, and the best automobile is selected by using the fuzzy TOPSIS method.

Due to its widespread use in machining, reducing power consumption in the turning process is one of the key factors for a sustainable production process. Nickel-based superalloys are preferred in variable applications due to their superior mechanical properties. This study aims to investigate the effects of process parameters on power consumption in turning of Haynes 242 nickel-based superalloy. In this context, three levels of Box-Behnken design combined with the Response Surface Method (RSM) and genetic algorithm (GA) were applied to find the optimum parameter values used in the estimation of the minimum power consumption to create the regression model. First, the Box-Behnken experimental design was created based on 3 different levels of tool nose radius (0.4,0.6 and 0.8 mm), depth of cut (0.2,0.4 and 0.6 mm), and feed rate (0.1,0.2 and 0.3 mm/rev.). Then, the power consumption of each test measured by AdvantEdge™ based on the obtained experimental sets. Then, GA was used for power consumption estimation by utilizing the mathematical estimation model obtained from RSM. Finally, the estimated values obtained by both methods were compared. Both statistical and simulation results show that low feed rate and depth of cut are needed to minimize power consumption.