An Interactive Fuzzy Satisficing Method for Fuzzy Random Multiobjective 0-1 Programming Problems through Probability Maximization Using Possibility

In this paper, we focus on multiobjective 0-1 programming problems under the situation where stochastic uncertainty and vagueness exist at the same time. We formulate them as fuzzy random multiobjective 0-1 programming problems where coefficients of objective functions are fuzzy random variables. For the formulated problem, we propose an interactive fuzzy satisficing method through probability maximization using of possibility

Multiple-Criteria Optimization for Environment Development System : Constrained Case

This paper deals with the multiple-criteria optimization problem for environment development systems, where the global amounts of each exogenous factor are limited. The model of an environment development system, which was introduced by R. Kulikowski, is described by a system of nonlinear differential equations which include interconnected n endogenous factors and m×n exogenous factors. The problem of multiple-criteria optimization for an environment development system is formulated. A main difficulty of multiple-criteria optimization is that it is no longer clear what one means by an optimal solution. A possible remedy for this situation is to introduce an objective function which is expressed as some function of various criterions. Given the specific objective function, we first optimize the system with respect to another criterion which is a linear combination of the given criteria. For the special case when the systems have similar nonlinearities, the solution of the linear combination problem is obtained in an explicit manner, in terms of the weighting factors in the linear combination functional. A search procedure is then used to determine the optimum values of these weighting factors for the specified objective function

Particle Swarm Optimization Combining Diversification and Intensification for Nonlinear Integer Programming Problems

In this research, focusing on nonlinear integer programming problems, we propose an approximate solution method based on particle swarm optimization proposed by Kennedy et al. And we developed a new particle swarm optimization method which is applicable to discrete optimization problems by incoporating a new method for generating initial search points, the rounding of values obtained by the move scheme and the revision of move methods. Furthermore, we showed the efficiency of the proposed particle swarm optimization method by comparing it with an existing method through the application of them into the numerical examples. Moreover we expanded revised particle swarm optimization method for application to nonlinear integer programming problems and showed more effeciency than genetic algorithm. However, variance of the solutions obtained by the PSO method is large and accuracy is not so high. Thus, we consider improvement of accuracy introducing diversification and intensification

A Neural Network Approach for Non-contact Defect Inspection of Flat Panel Displays

AbstractThis paper proposes a neural network-based approach for the inspection of electrical defects on thin film transistor lines of flat panel displays. The inspection is performed on digitized waveform data of voltage signals that are captured by a capacitor-based non-contact sensor by scanning over thin film transistor lines on the surface of the mother glass of flat panels. The sudden deep falls (open circuits) or sharp rises (short circuits) on the captured noisy waveform are classified and detected by employing a four-layer feed-forward neural network with two hidden layers. The topology of the network comprises an input layer with two units, two hidden layers with two and three units, and an output layer with one unit; a standard sigmoid function as the activation function for each unit. The network is trained with a fast adaptive back-propagation algorithm to find an optimal set of associated weights of neurons by feeding a known set of input data. The ambiguity of the threshold definition does not arise in this method because it uses only local features of waveform data at and around selected candidate points as inputs to the network, unlike the existing thresholding-based method, which is inherently prone to missed detections and false detections

An Interactive Fuzzy Satisficing Method for Multiobjective Stochastic Integer Programming Problems through Simple Recourse Model

Two major approaches to deal with randomness or impression involved in mathematical programming problems have been developed. The one is called stochastic programming, and the other is called fuzzy programming. In this paper, we focus on multiobjective integer programming problems involving random variable coefficients in constraints. Using the concept of simple recourse, such multiobjective stochastic integer programming problems are transformed into deterministic ones. As a fusion of stochastic programming and fuzzy one, after introducing fuzzy goals to reflect the ambiguity of the decision maker's judgments for objective functions, we propose an interactive fuzzy satisficing method to derive a satisficing solution for the decision maker by updating the reference membership levels

Long-term Operation Planning of District Heating and Cooling Plants Considering Contract Violation Penalties

Urban district heating and cooling (DHC) systems operate large freezers, heat exchangers, and boilers to stably and economically supply hot and cold water, steam etc., based on customers demand. We formulate an operation-planning problem as a nonlinear integer programming problem for an actual DHC plant. To reflect actual decision making appropriately, we incorporate contract-violation penalties into the running cost consisting of fuel and arrangements expenses. Since a yearly operation plan is necessary for check whether the minimum gas consumption contract is fulfilled or not, we need to solve long-term operation-planning problems. To fast and approximately solve long-term operation-planning problems, we propose a decomposition approach using coarse (monthly) approximate operation-planning problems

Computational Methodsfor Two-Level 0-1 Programming Problemsthrough Distributed Genetic Algorithms, Journal of Telecommunications and Information Technology, 2010, nr 2

In this paper, we consider a two-level 0-1 programming problem in which there is not coordination between the decision maker (DM) at the upper level and the decision maker at the lower level. We propose a revised computational method that solves problems related to computational methods for obtaining the Stackelberg solution. Specifically, in order to improve the computational accuracy of approximate Stakelberg solutions and shorten the computational time of a computational method implementing a genetic algorithm (GA) proposed by the authors, a distributed genetic algorithm is introduced with respect to the upper level GA, which handles decision variables for the upper level DM. Parallelization of the lower level GA is also performed along with parallelization of the upper level GA. The proposed algorithm is also improved in order to eliminate unnecessary computation during operation of the lower level GA, which handles decision variables for the lower level DM. In order to verify the effectiveness of the proposed method, we propose comparisons with existing methods by performing numerical experiments to verify both the accuracy of the solution and the time required for the computation