A bi-objective score-variance based linear assignment method for group decision making with hesitant fuzzy linguistic term sets

Decision makers usually prefer to express their preferences by linguistic variables. Classic fuzzy sets allowed expressing these preferences using a single linguistic value. Considering inevitable hesitancy of decision makers, hesitant fuzzy linguistic term sets allowed them to express individual evaluation using several linguistic values. Therefore, these sets improve the ability of humans to determine believes using their own language. Considering this feature, in this paper a method upon linear assignment method is proposed to solve group decision making problems using this kind of information, when criteria weights are known or unknown. The performance of the proposed method is illustrated in a numerical example and the results are compared with other methods to delineate the models efficiency. Following a logical and well-known mathematical logic along with simplicity of execution are the main advantages of the proposed method

An Outsourcing Expert System for Governing Organizations

Making the right decision about doing activities in-house vs. outsourcing is one of the important management decisions. The considerable effect of this decision on organizational performance and responsiveness is supported by theoretical and empirical evidence. In this study, using the experts’ knowledge extraction and modeling, we have designed a logical framework for deciding about outsourcing in governing organizations and accordingly an expert system has been developed. As an applied research a descriptive approach and case study method have been used. Objectives, inevitable circumstances, requirements, background conditions and facilitators have been identified as the underlying components of the system. The system provides a recommendation for each activity and prioritizes them based on readiness for outsourcing. An implementation in IT department of a sample organization is provided and the results are analyzed.  In the end, the proposed system for activities of IT Management was carried out in one of the organizations and consequently the obtained outputs and results were analyzed

Stochastic and Markov chain approach to optimize re-manufacturing and outsourcing in the closed loop supply chain.

The turbulent and dynamic environment of today's business world has become increasingly challenging for organizations operating in different business areas. In such a situation, in order to get rid of these conditions, moving forward and towards the perspective of the new horizons of prosperity and survival is the dream of many of them. In the meantime, according to the specific circumstances and requirements that govern each institution or organization, and in order to achieve their desired productivity, they use certain strategies and programs that outsourcing is one of these strategies. Today, organizations are outsourcing to boost competitive ability and profit and focus on their competitive edge. In this research, a mathematical programming approach is proposed to optimize the issue of outsourcing in the supply chain. In this approach, at first the mathematical model of the problem showed and then in order to solve the problem of the theory of Markov chains described. The objective function of the problem involves minimizing the cost of purchasing, outsourcing and lost demand. In order to solve the problem, three genetic metamorphic algorithms, gray wolves and antlion have been used. After examining the numerical expressions, Gray wolf's algorithm has the highest level of performance. In order to expand the applied dimensions of research in real-world conditions, a company (MPEICO) that manufactures insulators is considered as the case study.

How to relevantly characterize hydraulic properties of saline and sodic soils for water and solute transport simulations

Proper sets of soil hydraulic parameters are important for modeling water flow and solute transport. Using field saline water and laboratory tap water, this study systematically analyzed and compared laboratory saturated hydraulic conductivity, K-ls and soil water retention curve, SWRC of saline soil within three soil multi-layered profiles to find out the adequate initial hydraulic parameter set of a hydrological model. Falling and constant head methods were applied on three undisturbed samples per pedogenic horizon. SWRC were established on the same samples, using both saline and tap water. Results showed the spatial variability of hydraulic properties in vertical and horizontal dimensions when comparing soil profiles' properties. The constant head method with saline water resulted in the highest K-ls values followed by the falling head method with saline water, the constant head method with fresh water, and the falling head method with fresh water. The differences in K-ls were up to two orders of magnitude when comparing all datasets. Fresh water reduced soil water retention significantly. A significant correlation between the hydraulic properties and some physical and chemical properties suggests that the effects of soil texture, structure, calcium carbonate and gypsum content, and CEC (clay mineralogy) were larger than those of salt content. The change in hydraulic properties and the dependency on the procedure use may be attributed to aggregate breakdown and plugging of macropores, clay heterogeneity and dispersion, calcium carbonate and gypsum dissolution, all damaging soil structure. Simulation runs illustrated that hydraulic properties obtained with saline water resulted in better model performance than when using the conventional procedures with fresh/tap water. A proper set of initial hydraulic parameter values is an important step in model calibration and thus must be characterized correctly. Therefore, to hydraulically characterize saline soil, procedures should be standardized. Moreover, the approach and results of this study may provide a reference for characterization of soil hydraulic properties of saline and sodic soils effectively