New Einstein Hybrid Aggregation Operators for Intuitionistic Fuzzy Sets and Applications in Multi-Criteria Decision-Making

The fusion of multidimensional intuitionistic fuzzy information plays an important part in decision making processes under an intuitionistic fuzzy environment. In this chapter, it is observed that existing intuitionistic fuzzy Einstein hybrid aggregation operators do not follow the idempotency and boundedness. This leads to sometimes illogical and even absurd results to the decision maker. Hence, some new intuitionistic fuzzy Einstein hybrid aggregation operators such as the new intuitionistic fuzzy Einstein hybrid weighted averaging (IFEHWA) and the new intuitionistic fuzzy Einstein hybrid weighted geometric (IFEHWG) were developed. The new IFEHWA and IFEHWG operators can weigh the arguments as well as their ordered positions the same as the intuitionistic fuzzy Einstein hybrid aggregation operators do. Further, it is validated that the defined operators are idempotent, bounded, monotonic and commutative. Then, based on the developed approach, a multi-criteria decision-making (MCDM) procedure is given. Finally, a numerical example is conducted to demonstrate the proposed method effectively.

Fuzzy Parallel Machine Scheduling Problem Under Fuzzy Job Deterioration and Learning Effects With Fuzzy Processing Times

This chapter presents a mixed integer non-linear programming (MINLP) model for a fuzzy parallel machine scheduling problem under fuzzy job deterioration and learning effects with fuzzy processing times in order to minimize fuzzy makespan. The uncertainty of parameters such as learning/deterioration effects and processing times in a scheduling problem makes the solution of the problem uncertain. Fuzzy sets can be used to encode uncertainty in parameters. In this chapter, possibilistic distributions of fuzzy parameters and possibilistic linear programming approaches are used in order to create a solution method for MINLP model of fuzzy parallel machine scheduling problem.

Fuzzy Optimization and Decision Making

Selection of the best out of several strategies is always a difficult task. Fuzzy criteria allow a better approach to deal with such situations. Fuzzy optimization is one of the best tools in decision making. This chapter covers the concept of fuzziness, fuzzy sets, fuzzy membership and the features of membership functions. Also is described is the classification of fuzzy optimization. Then, decision making and various models for decision making under fuzzy environments are discussed. Standard examples of fuzzy optimization-based decision-making are included to describe the recent trends. This chapter may help researchers to explore different aspects of fuzzy optimization in decision-making.

Reliability Analysis for Environment Systems Using Dual Hesitant Fuzzy Set

In this chapter, we deal with dual hesitant fuzzy set theory and compute the fuzzy reliability with lifetime components of different electronic systems, such as series and parallel systems from a Markov chain technique. In dual hesitant fuzzy sets, we have membership and non-membership degree function whereas hesitant fuzzy sets only have membership function. In this chapter we also discuss the Weibull distribution and reliability function of the proposed systems. A numerical example is also given in the end of proposed algorithm.

Multi-Objective Optimization of ECG Process Applying Soft Computing Techniques

The present research work focuses on the selection of significant machining parameters depending on the nature-inspired algorithm while machining alumina-aluminum interpenetrating phase composites during electrochemical grinding. Control parameters like electrolyte concentration (C), voltage (V), depth of cut (D) and electrolyte flow rate (F) have been considered for experimentation. The response data are initially trained and tested by using Artificial Neural Network. The contradictory responses like higher material removal rate (MRR), lower surface roughness (Ra), lower overcut (OC) and lower cutting force (Fc) are ensured individually by employing Firefly Algorithm. A multi-response optimization for all the responses is done initially by using the Genetic algorithm. Finally, in order to obtain a single set of parametric combination for all the output simultaneously fuzzy based Grey Relational Analysis technique is adopted. These natures driven soft computing techniques corroborates well during the parametric optimization of the electrochemical grinding process.

Design, Comparison, and Evaluation of Controllers for Direct Current Servomotors

The results and comparison of controller performance based on fuzzy logic and neural networks with the purpose of improving the performance of PID controllers currently used in servomotors is presented. The performance comparisons will be made with no load and with load (consisting of a robotic type rotational link). The results show that as the number of links in a robot increases, the precision of the movements desired from it decreases, affecting the tasks that require a high degree of precision, so the design of controllers like those presented in this chapter is required. This work is the basis for implementing improvements in the performance of DC servomotor control systems in general.

Trajectory Planning and Control Algorithms of Mobile Robots for Static Environments

In this chapter, different types of trajectory control and planning algorithms for mobile robots in static environments are analyzed and assessed. To this end, a mobile robot is made to plan and follow a route between two arbitrary points in an autonomous way. This work goes in depth into the discrete space techniques and those based on search trees. First, kinematics, trajectory planning and contour maps, robot control, etc. are reviewed. Second, computer simulations that validate these theoretical results are also designed and implemented. Finally, the strengths and weaknesses of each trajectory planning methodology are assessed.

Application of Renewable Energy System With Fuzzy Logic

There has been rapid surge in energy consumption owing to the industrialization and the growing population. There has been a shift from agrarian economy to the industrial economy. This transformation has led to increased energy consumption in tandem with the emissions associated with it. Thus, the energy consumption has led to environmental concerns. Therefore, the planning and modeling of energy resources has become critical to economic growth and should be efficiently done for securing the health of the environment as well. Looking at the importance of modeling and planning, the present chapter is an attempt to explore the fuzzy based models used for the renewable systems and in particular the wind energy systems. It has been found that the fuzzy based models have been used extensively for installation of wind farms, for optimization of the parameters related to wind systems and for the site selection of the different wind energy farms.

Risks in Sustainable Food Supply Chain Management

Sustainability is the important factor in the food sector, due to the large demand worldwide. Sustainability in food sector is not accepted globally as per the growing demand of food. Because of business risks, uncertainty, government policy, technology, innovation, etc. So, in this article we will discuss about the risks in adoption of sustainable food supply chain management (SFSCM) and ranking the risks by using Fuzzy Analytic Hierarchy Process (FAHP) technique. We acknowledged various SFSCM related risks and suitable correlation among the identified risks. Ranking the risks by using Fuzzy AHP approach based on their priorities. Nine risks were identified from literature survey and expert's views. Risks like safety, technology, and legal and monetary of food, etc., are barriers in successful adoption of sustainability in the food sector. The risks related some terms which were found according to Indian culture and lifestyle of Indians.

Fuzzy Sumudu Transform Approach to Solving Fuzzy Differential Equations With Z-Numbers

Uncertain nonlinear systems can be modeled with fuzzy differential equations (FDEs) and the solutions of these equations are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this book chapter, the solutions of FDEs are approximated by utilizing the fuzzy Sumudu transform (FST) method. Here, the uncertainties are in the sense of fuzzy numbers and Z-numbers. Important theorems are laid down to illustrate the properties of FST. This new technique is compared with Average Euler method and Max-Min Euler method. The theoretical analysis and simulation results show that the FST method is effective in estimating the solutions of FDEs.