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Showing 11 results for Subject: Optimization Techniques

Amineh Zadbood, Kazem Noghondarian, Zohreh Zadbood,
Volume 24, Issue 2 (6-2013)

Response surface methodology is a common tool in optimizing processes. It mainly concerns situations when there is only one response of interest. However, many designed experiments often involve simultaneous optimization of several quality characteristics. This is called a Multiresponse Surface Optimization problem. A common approach in dealing with these problems is to apply desirability function approach combined with an optimization algorithm to determine the best settings of control variables. As the response surfaces are often nonlinear and complex a number of meta-heuristic search techniques have been widely for optimizing the objective function. Amongst these techniques genetic algorithm, simulated annealing, tabu search and hybridization of them have drawn a great deal of attention so far. This study presents the use of harmony search algorithm for Multiresponse surface optimization. It is one of the recently developed meta heuristic algorithms that has been successfully applied to several engineering problems. This music inspired heuristic is conceptualized from the musical process of searching for a perfect state of harmony. The performance of the algorithm is evaluated by an example from the literature. Results indicate the efficiency and outperformance of the method in comparison with some previously used methods.
Taha Hosseinhejazi, Majid Ramezani, Mirmehdi Seyyed-Esfahani, Ali Mohammad Kimiagari,
Volume 24, Issue 2 (6-2013)

control of production processes in an industrial environment needs the correct setting of input factors, so that output products with desirable characteristics will be resulted at minimum cost. Moreover, such systems havetomeetset of qualitycharacteristicstosatisfycustomer requirements.Identifyingthemosteffectivefactorsindesignoftheprocesswhichsupportcontinuousandcontinualimprovement isrecentlydiscussedfromdifferentviewpoints.Inthisstudy, we examined the quality engineering problems in which several characteristics and factors are to be analyzed through a simultaneous equations system. Besides, the several probabilistic covariates can be included to the proposed model. The main purpose of this model is to identify interrelations among exogenous and endogenous variables, which give important insight for systematic improvements of quality. At the end, the proposed approach is described analytically by a numerical example.
Mahdi Bashiri, Masoud Bagheri,
Volume 24, Issue 3 (9-2013)

The quality of manufactured products is characterized by many controllable quality factors. These factors should be optimized to reach high quality products. In this paper we try to find the controllable factors levels with minimum deviation from the target and with a least variation. To solve the problem a simple aggregation function is used to aggregate the multiple responses functions then an imperialist competitive algorithm is used to find the best level of each controllable variable. Moreover the problem has been better analyzed by Pareto optimal solution to release the aggregation function. Then the proposed multiple response imperialist competitive algorithm (MRICA) has been compared with Multiple objective Genetic Algorithm. The experimental results show efficiency of the proposed approach in both aggregation and non aggregation methods in optimization of the nonlinear multi-response programming.
Yahia Zare Mehrjerdi,
Volume 25, Issue 3 (7-2014)

Abstract It is the purpose of this article to introduce a linear approximation technique for solving a fractional chance constrained programming (CC) problem. For this purpose, a fuzzy goal programming model of the equivalent deterministic form of the fractional chance constrained programming is provided and then the process of defuzzification and linearization of the problem is started. A sample problem is presented for clarification purposes.
Seyed Mojtaba Jafari Henjani, Valeriy Severin,
Volume 25, Issue 3 (7-2014)

The paper is devoted to solution of some problems in nuclear power station generating unit intellectual control systems using genetic algorithms on the basis of control system model development, optimizations methods of their direct quality indices and improved integral quadratic estimates. Some mathematical vector models were obtained for control system multicriterion quality indices with due consideration of stability and quality indices criteria, this increasing the reliability of optimal control system synthesis. Optimal control systems with fuzzy controllers were synthesized for nuclear reactor, steam generator and steam turbine, thus allowing comparison between fuzzy controllers and traditional PID controllers. Mathematical models built for nuclear power station generating unit control systems, including nuclear reactor, steam generator, steam turbine and their control systems interacting under normal operational modes, which permitted to perform parametrical synthesis of system and to study various power unit control laws. On the basis of power unit control system models controllers were synthesized for normal operational modes.
Mr Sachin Mahakalkar, Dr Vivek Tatwawadi, Mr Jayant Giri, Dr Jayant Modak,
Volume 26, Issue 1 (3-2015)

Response surface methodology (RSM) is a statistical method useful in the modeling and analysis of problems in which the response variable receives the influence of several independent variables, in order to determine which are the conditions under which should operate these variables to optimize a corrugated box production process. The purpose of this research is to create response surface models through regression on experimental data which has been reduced using DA to obtain optimal processing conditions. Studies carried out for corrugated sheet box manufacturing industries having man machine system revealed the contribution of many independent parameters on cycle time. The independent parameters include anthropometric data of workers, personal data, machine specification, workplace parameters, product specification, environmental conditions and mechanical properties of corrugated sheet. Their effect on response parameter cycle time is totally unknown. The developed model was simulated and optimized with the aid of MATLAB R2011a and the computed value for cycle time is obtained and compared with experimental value. The results obtained showed that the correlation R, adjusted R2 and RMS error were valid.
Dr. Yahia Zare Mehrjerdi, Amir Ebrahimi Zade, Dr. Hassan Hosseininasab,
Volume 26, Issue 3 (9-2015)

Abstract One of the basic assumptions in hub covering problems is considering the covering radius as an exogenous parameter which cannot be controlled by the decision maker. Practically and in many real world cases with a negligible increase in costs, to increase the covering radii, it is possible to save the costs of establishing additional hub nodes. Change in problem parameters during the planning horizon is one of the key factors causing the results of theoretical models to be impractical in real world situations. To dissolve this problem in this paper a mathematical model for dynamic single allocation hub covering problem is proposed in which the covering radius of hub nodes is one of the decision variables. Also Due to NP-Hardness of the problem and huge computational time required to solve the problem optimally an effective genetic algorithm with dynamic operators is proposed afterwards. Computational results show the satisfying performance of the proposed genetic algorithm in achieving satisfactory results in a reasonable time. Keywords: hub location problem, dynamic hub covering problem, flexible covering radius, dynamic genetic algorithm.


Farid Khoshalhan, Ali Nedaie,
Volume 27, Issue 1 (3-2016)

There are many numerous methods for solving large-scale problems in which some of them are very flexible and efficient in both linear and non-linear cases. League championship algorithm is such algorithm which may be used in the mentioned problems. In the current paper, a new play-off approach will be adapted on league championship algorithm for solving large-scale problems. The proposed algorithm will be used for solving large-scale solving support vector machine model which is a quadratic optimization problem and cannot be solved in a non polynomial time using exact algorithms or optimally using traditional heuristic ones in large scale sizes. The efficiency of the new algorithm will be compared to traditional one in terms of the optimality and time measures. The superiority of the algorithm can be compared versus older version.

Seyed Babak Ebrahimi, Seyed Morteza Emadi,
Volume 27, Issue 4 (12-2016)

Empirical studies show that there is stronger dependency between large losses than large profit in financial market, which undermine the performance of using symmetric distribution for modeling these asymmetric. That is why the assuming normal joint distribution of returns is not suitable because of considering the linier dependence, and can be lead to inappropriate estimate of VaR. Copula theory is basic tool for multivariate modeling, which is defined by using marginal and dependencies between variables joint distribution function. In addition, Copulas are able to explain and describe of complex multiple dependencies structures such as non-linear dependence. Therefore, in this study, by combining symmetric and asymmetric GARCH model for modeling the marginal distribution of variables and Copula functions for modeling financial data and also use of DCC model to determine the dynamic correlation structure between assets, try to estimate the Value at Risk of investment portfolio consists of five active index In Tehran Stock Exchange. The results demonstrate excellence of GJR-GARCH(1,1) with the distribution of t-student for marginal distribution. t-Copula model, estimates the Value at Risk model less than the Gaussian Copula in all cases.

Hossein Mirshojaee, Behrooz Masoumi, Esmaeel Zeinali,
Volume 28, Issue 1 (3-2017)

    Given the increasing number of documents, sites, online sources, and the users’ desire to quickly access information, automatic textual summarization has caught the attention of many researchers in this field. Researchers have presented different methods for text summarization as well as a useful summary of those texts including relevant document sentences. This study selects extractive method out of different summarizing methods (e.g. abstract method). Extractive method involves summarizing text through objective extraction of some parts of a text like word, sentence, and paragraph. A summarization issue would be unsolvable by exact methods in a reasonable time with considering documents with high amount of information (NP complete). These kinds of issues are usually solved using metaheuristic methods. A biogeography-based optimization algorithm (BBO), which is a new metaheuristic method in the domain of extractive text summarization, is used in this article. 

Dr. Shadan Sadighbehzadi, Dr. Zohreh Moghaddas, Dr. Amirreza Keyghobadi, Dr. Mohsen Vaez-Ghasemi,
Volume 29, Issue 4 (12-2018)

Natural disasters and crisis are inevitable and each year impose destructive effects on human as injuries and damage to property. In natural  disasters and after the outbreak of the crisis, demand for logistical goods and services increase. Effective distribution of emergency aid could have a significant role in minimizing the damage and fatal accident. In this study, a three-level relief chain including a number of suppliers in fixed locations, candidate distribution centers and affected areas at certain points are considered. For this purpose a mixed integer nonlinear programming model is proposed for open transportation location routing problem by considering split delivery of demand. In order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed mode. The objectives of the proposed model include total cost minimization, minimization of the maximum travel time of
vehicles and minimization of unmet demands. In order to solve the problem of the proposed model, fuzzy multi-objective planning is used. For efficiency and effectiveness of the proposed model and solution approach, several numerical examples are studied. Computational results show the effectiveness and efficiency of the model and the proposed approach.

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