The purpose of this study is to develop a fuzzy Analytic Network Process (ANP) model that has the ability to evaluate employee performance in different strategies. A team of experts in the field of strategic human resource management and senior management of an organization engaged in steel production were involved in the study. The data collection tool was a questionnaire that was designed based on the criteria of organization's performance appraisal system. The results showed that in cost leadership strategy, compliance of work hierarchy, quantity of work and the ability to make important decisions constituted the highest coefficients, while in the focus strategy, participate in group work, power of supervision and administration and decision making ability had the highest importance. In differentiation strategy, innovation and creativity, quality and offering constructive suggestions received higher ratings than other criteria. Finally, the developed model was used to evaluate the performance of a sample employee.

One of the most promising strategies that will smooth out the maintainability issues of the software is refactoring. Due to lack of proper design approach, the code often inherits some bad smells which may lead to improper functioning of the code, especially when it is subject to change and requires some maintenance. A lot of studies have been performed to optimize the refactoring strategy which is also a very expensive process. In this paper, a component based system is considered, and a Fuzzy Multi Criteria Decision Making (FMCDM) model is proposed by combining subjective and objective weights to rank the components as per their urgency of refactoring. Jdeodorant tool is used to detect the code smells from the individual components of a software system. The objective method uses the Entropy approach to rank the component having the code smell. The subjective method uses the Fuzzy TOPSIS approach based on decision makers’ judgement, to identify the critically and dependency of these code smells on the overall software. The suggested approach is implemented on component-based software having 15 components. The constitute components are ranked based on refactoring requirements.

An enormous amount of heat is released at the contact surface during machining of a component/partdue to friction, rubbing action and cutting forces generated. Cutting fluids are generally applied to provide lubrication and cooling at the tool and workpiece interface. They also play a beneficial role in machining operations, and enhance job shop’s productivity, tool life and quality of the finished parts/products. In addition to these, they act as an important contributor in optimizing a machining operation. However, a cutting fluid suitable for a particular machining requirement may not be equally good for other applications and hence, there is a need for selection of the appropriate type of cutting fluid with a view to facilitate superlative and uncomplicated machining operation. Several factors, such as material ofthe cutting tool, operator’s safety, compatibility with the machine tool, reliability and rancidity of the cutting fluid, and cost may all combine to limit the effectiveness or applicability of a cutting fluid. Therefore, the present study explores the potentiality of a modified similarity-based method, which is a multi-criteria decision making tool, in solving cutting fluid selection problems. To illustrate the procedural steps of this method, two real time problems are solved. The results obtained highly corroborate with the opinions of the experts in the related field, demonstrating the applicability of the said method.

Social networks can provide sellers across the world with invaluable information about the structure of possible influences among different members of a network, whether positive or negative, and can be used to maximize diffusion in the network. Here, a novel mathematical monopoly product pricing model is introduced for maximization of market share in noncompetitive environment. In the proposed model, a customer’s decision to buy a product is not only based on the price, quality and need time for the product but also on the positive and negative influences of his/her neighbors. Therefore, customers are considered heterogeneous and a referral bonus is granted to every customer whose neighbors also buy the product. Here, the degree of influence is directly related to the intensity of the customers’ relationships. Finally, using the proposed model for a real case study, the optimal policy for product sales that is the ratio of product sale price in comparison with its cost and also the optimal amounts of referral bonus per customer is achieved.

This paper deals with the degree constrained k-cardinality minimum spanning tree (k-MSTPD) problem defined on a connected, edge weighted and undirected graph. The aim of this problem is to determine the least weighted spanning tree with exactly k vertices such that except the root vertex, no other vertex in the resultant spanning tree exceeds the specified degree limit. The k-MSTPD problem has many practical applications for the design of electric, communication, and transportation networks. The problem is then formulated as a zero-one programming. In this paper, an exact algorithm known as Lexi-search algorithm (LSA) is developed to tackle the k-MSTPD problem. Furthermore, the developed LSA is programmed in Matlab and tested on some benchmark instances as well as on random instances and the respective results are reported. The obtained experimental results showed that the developed LSA takes significantly less time to find the optimal solutions.

This paper deals with a pricing and production-distribution model for a deteriorating item in a two-echelon supply chain. The profit function for the manufacturer and retailer in the integrated supply chain is derived. The manufacturer's production batch size is regulated to an integer multiple of the discrete delivery lot quantity to the retailer. The objective is to maximize the total profit per unit time by finding the optimal selling price, production lot size, total cycle time, number of deliveries, and delivery lot size, simultaneously. Based on the notion of optimal interval, we outline an effective algorithm for finding the optimal solution. Finally, the authors present a numerical example to illustrate the theoretical results of the model. Sensitivity analysis for the optimal solution with respect to major parameters is also carried out. The results show that, when the deterioration rate increases, both the optimal production lot size and cycle time decrease. It is interesting to note that an increase in the deterioration rate also tends to reduce the delivery lot size without affecting the number of deliveries per production batch. Also, the optimal interval for N does not change when deterioration rate changes. Reductions in the inventory cycle times for both parties demonstrate the negative effects of deterioration on the supply chain.

In this study, an ensemble algorithm has been proposed, called Quasi-Oppositional Symbiosis Organisms Search (QOSOS) algorithms, by incorporating the quasi-oppositional based learning (QOBL) strategy into the newly proposed Symbiosis Organisms Search (SOS) algorithm for solving unconstrained global optimization problems. The QOBL is incorporated into the basic SOS algorithm due to the balance of the exploration capability of QOBL and the exploitation potential of SOS algorithm. To validate the efficiency and robustness of the proposed Quasi-Oppositional Symbiosis Organisms Search (QOSOS) algorithms, it is applied to solve unconstrained global optimization problems. Also, the proposed QOSOS algorithm is applied to solve two real world global optimization problems. One is gas transmission compressor design optimization problem and another is optimal capacity of the gas production facilities optimization problem. The performance of the QOSOS algorithm is extensively evaluated and compares favorably with many progressive algorithms.

The revolutionary alterations and conversions occurring in information and communication technologies, have triggered an increase in the electronic commerce applications. Airline tickets are one of the most popular items purchased on the internet. The airline websites have become a big distribution channel for the companies to sustain their competitiveness. At this moment, the competition is increasing as airlines try to acquire and retain customers in the airline industry. To acquire and retain customers in such a highly competitive market, it is important for airlines to understand their relative levels of quality in terms of critical elements affecting their competitive advantages. In this study, an integrated two-stage multi-criteria decision-making techniques were used for the measurement of the performance of the airline websites using the Entropy Weight Method and the Grey Relational Analysis approach. The performance of 11 airline companies’ websites operating in Turkey was evaluated in terms of seven criteria. The data of quality website from airlines websites were taken more than 30 trails on various occasions on different periods of times. The data has been taken from 1 December 2016 to 31 December 2016. The weights of the attributes were calculated by Entropy Weight Method, the evaluation of the alternatives using the Grey Relational Analysis method were given ranking of websites.

Application of Artificial Neural Network (ANN) in modeling of combined cycle power plant (CCPP) with dry cooling tower (Heller tower) has been investigated in this paper. Prediction of power plant output (megawatt) under different working conditions was made using multi-layer feed-forward ANN and training was performed with operational data using back-propagation. Two ANN network was constructed for the steam turbine (ST) and the main cooling system(MCS). Results indicate that the ANN model is effective in predicting the power plant output with good accuracy.

Multi-criteria decision making approach is one of the most troublesome tools for solving the tangled optimization problems in the machining area due to its capability of solving the complex optimization problems in the production process. Turning is widely used in the manufacturing processes as it offers enormous advantages like good quality product, customer satisfaction, economical and relatively easy to apply. A contemporary approach, MOORA coupled with PCA, was used to ascertain an optimal combination of input parameters (spindle speed, depth of cut and feed rate) for the given output parameters (power consumption, average surface roughness and frequency of tool vibration) using L27 orthogonal array for turning on ASTM A588 mild steel. Comparison between MOORA-PCA and TOPSIS-PCA shows the effectiveness of MOORA over TOPSIS method. The optimum parameter combination for multi-performance characteristics has been established for ASTM A588 mild steel are spindle speed 160 rpm, depth of cut 0.1 mm and feed rate 0.08 mm/rev. Therefore, this study focuses on the application of the hybrid MCDM approach as a vital selection making tool to deal with multi objective optimization problems.