Intuitionistic fuzzy optimization technique for Pareto optimal solution of manufacturing inventory models with shortages

2013 ◽  
Vol 228 (2) ◽  
pp. 381-387 ◽  
Author(s):  
Susovan Chakrabortty ◽  
Madhumangal Pal ◽  
Prasun Kumar Nayak
Keyword(s):  
Optimization Technique ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Inventory Models ◽  
Intuitionistic Fuzzy

Pareto Optimal Setpoints for HVAC Networks via Iterative Nearest Neighbor Communication

2013 ◽  
Author(s):  
Matthew S. Elliott ◽  
Christopher J. Bay ◽  
Bryan P. Rasmussen
Keyword(s):  
Nearest Neighbor ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Hvac Systems ◽  
Global Optimum ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Cost Information ◽  
The Cost ◽  
Dynamic Subsystem

HVAC systems in large buildings frequently feature a network topology wherein the outputs of each dynamic subsystem act as disturbances to other subsystems in a well-defined local neighborhood. The distributed optimization technique presented in this paper leverages this topology without requiring a centralized optimizer or widespread knowledge of the interaction dynamics between subsystems. Each subsystem’s optimizer communicates to its neighbors its calculated optimum setpoint, as well as the costs imposed by the neighbor’s calculated set-points. By judicious construction of the cost functions, all of the cost information is propagated through the network, allowing a Pareto optimal solution to be reached. The novelty of this approach is that communication between all plants is not necessary to achieve a global optimum, and that changes in one controller do not require changes to all controllers in the network. Proofs of Pareto optimality are presented, and convergence under the approach is demonstrated with a numerical and experimental example.

Multi-Objective Optimization Algorithm Design and Implementation of Mobile Base Station Site Selection

2010 ◽  
Vol 29-32 ◽  
pp. 2496-2502
Author(s):  
Min Wang ◽  
Jun Tang
Keyword(s):  
Site Selection ◽  
Engineering Application ◽  
Optimal Solution ◽  
Base Station ◽  
Pareto Optimal Solution ◽  
Antibody Concentration ◽  
Pareto Optimal ◽  
Memory Cells ◽  
Multi Objective Optimization ◽  
Multi Objective

The number of base station location impact the network quality of service. A new method is proposed based on immune genetic algorithm for site selection. The mathematical model of multi-objective optimization problem for base station selection and the realization of the process were given. The use of antibody concentration selection ensures the diversity of the antibody and avoiding the premature convergence, and the use of memory cells to store Pareto optimal solution of each generation. A exclusion algorithm of neighboring memory cells on the updating and deleting to ensure that the Pareto optimal solution set of the distribution. The experiments results show that the algorithm can effectively find a number of possible base station and provide a solution for the practical engineering application.

Interactive Decision Making for Fuzzy Random Multiobjective Linear Programming Problems with Variance Covariance Matrices Through Fractile Optimization

2014 ◽  
Vol 18 (3) ◽  
pp. 383-390
Author(s):  
Hitoshi Yano ◽  
Keyword(s):  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Covariance Matrices ◽  
Pareto Optimal ◽  
Membership Functions ◽  
Multiobjective Linear Programming ◽  
Satisfactory Solution ◽  
Interactive Decision Making ◽  
Linear Programming Problems ◽  
Fuzzy Random

In this paper, we focus on fuzzy random multiobjective linear programming problems with variance covariance matrices through fractile optimization, and propose an interactive decision making method to obtain a satisfactory solution. In the proposed method, it is assumed that the decision maker has fuzzy goals for not only permissible probability levels but also the corresponding objective functions. Such fuzzy goals are quantified by eliciting the corresponding membership functions. Using the fuzzy decision, such two kinds of membership functions are integrated, andDf-Pareto optimal solution concept is defined in the integrated membership space. By using the bisection method and the convex programming technique, a satisfactory solution is obtained from among aDf-Pareto optimal solution set through the interaction with the decision maker.

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