Journal of Operational Research in its Applications
Year:2020 | Volume:17 | Issue:3 (66)|Papers Count:8

The fuzzy primal-dual simplex method is a new and efficient method for solving linear programming problems with fuzzy variables. This algorithm is based on duality results and, similar to the dual simplex method, begins with dual feasibility and proceeds to primal feasibility. An important difference between the dual simplex method and the primal-dual method is that in the primal-dual algorithm, it is not required that the dual feasible solution to be basic. In this paper, we develop the primal-dual simplex method for solving fuzzy multiobjective linear programming problems. To this end, we utilize the fuzzy weighted sum scalarization method to present a fuzzy single objective optimization problem related to the fuzzy multiobjective linear programming problem. Then, by partitioning the weights of the weighted sum problem, we generalize the single objective primal-dual algorithm to fuzzy multiobjective problems. By using the presented algorithm, we can find a set of fuzzy Pareto optimal solutions. Presenting a set of fuzzy Pareto optimal solutions to the decision maker, enables him\her to select the best solution based on his\her preferences. Finally, we apply the proposed algorithm for solving a three-objective optimization problem with fuzzy variables and compare the results with some existing methods.

Nowadays the most important and most fundamental centers of higher education affair are universities and their subsets. These higher education centers are institutions with a specific purpose that their performance quality assessment about the optimal usage from sources and more achievement of goals is a necessity. Also, the performance quality assessment of these centers is one of the requirements of their dynamic, so that the absence of a continuous evaluation process causes their recession. Meanwhile identifying strength and weak points and minimizing input resources, the current situation can be improved by the performance quality assessment. Hence, in this research due to the importance of the assessment of universities and their subsets performance quality, a new evaluation model was proposed based on an input efficiency profile model called improved input efficiency profile model that improves the performance of input efficiency profile model. Also, by presenting a numerical example together analyzing conclusions and validation has been shown that this proposed model has necessary reliability.

The collocation method is very common in solving different types of differential equations. A main difficulty of this method is that its coefficient matrix becomes ill-conditioned when the degree of approximation increases. This can cause numerical troublesome and decreases the accuracy of the solution. In this study, three methods are proposed based on the combination of Bernstein collocation and optimization methods for approximate solutions of initial and boundary value problems involving linear differential equations with variable coefficients. In these methods, the approximate solution of the problem is obtained using the solution of a constrained linear least squares problem or a linear programming problem. To investigate the effectiveness of the methods, experimental problems of the different orders are considered and the results are compared with the results reported from other methods. Studies show that the proposed methods are accurate, efficient and have good numerical stability.

This paper studies multi objective stochastic optimization models with chance constraints. The use of random variables as input parameters in mathematical models is one of the conventional approaches to model various problems under uncertainty. Also, the chance constraints allow the decision maker to reject the corresponding constraint with the probability of up to a specified value. One of the challenges of such models is the chance constraints and these models cannot be solved directly. The given chanced constraints must first be converted to a deterministic case and then solved by applying the available techniques. One of the most important methods for converting the chance constraints into the deterministic one is to use the distribution function of the random variables that should be available to decision makers but usually there is no exact distribution function of a random variable in the real problems. For this reason, this paper proposes a sampling-based approach to convert chance constraints to deterministic ones, which meets the chance constraints with the greatest probability. The weighted sum method is used to solve the multi objective deterministic model. Finally a numerical example is presented to illustrate the performance of the proposed method.

An important way to implement green supply chain management is to revise the purchase method. In the past decade, how to determine the most suitable suppliers in the supply chain as a strategic factor, has been of interest to many. In the selection of lean suppliers, the supplier features include low cost, high quality and in agile supplier selection, features are speed, flexibility and quality. This study has been conducted in two phases. In the first phase, suppliers are ranked by using the combination of AHP and VIKOR and green criteria. Then, in the second stage, in order to assign the order to selected suppliers in the first phase, the model framework was designed, which included four goals of minimizing supply chain cost, minimizing delivery time (agile), minimizing waste products (lean) and maximizing attention to environmental issues. In the following, the multi objective problem is converted to a single objective problem by using a comprehensive L-P metric approach. Several numerical examples have been devised to describe the adequacy of this strategy. The implemented algorithm shows good results in convenient computational time.

The optimization of pricing financial assets quantitatively is an emerging discipline that attempts to model the impact of biases that investors in asset prices. This article provides an overview of the theoretical foundations and challenges and offers some solutions in this field. The paper is divided into two parts. In the first part of the paper, an overview of the selected literature is presented on key theoretical foundations. Why is this new financial field emerging? What subjects does he study? In the second part, the paper outlines a general plan provides a comprehensive set of resources on approaches to optimize the effectiveness of investment in quantitative modeling the behavior of financial market offers. Recent evidence of financial decision-making under uncertainty suggests that people do not act like neoclassical models. For this reason, investors often act as behavioral biases that have not been described in neoclassical models. Because behavioral bias in investors is a rather vague concept and it is very difficult to define and quantify numerically. Therefore, investor behavioral bias is another constraint that optimizes financial decision making. The problem of optimizing current financial asset pricing models does not take into account the effect of behavioral bias on portfolio valuation. The aim of this study was to optimize financial assets pricing subject to behavioral biases in the measurement of cognitive psychology, factors related to emotional and irrational factors will be discussed.

Today, multi-objective optimization algorithms are used as powerful tools for solving several problems. In any multi-objective optimization algorithm, diversity and convergence are two of the most important factors that need to be improved. Diversity and convergence are functions of exploration, exploitation, and selection operators. Effective algorithms should be used by different operators to achieve a robust optimization algorithm. In this study, a multi-objective optimization algorithm is proposed to enhance the diversity and convergence for solving the I-beam engineering problem. The proposed algorithm uses a proposed bi-directional mutation algorithm to exploit search space and uses the proposed probabilistic crossover algorithm to explore the search space. In this study, the hyper volume metric has been used to evaluate convergence and diversity. In the final section of this study, the overall performance of the proposed algorithm is compared with algorithms such as SPEA, NSGAII, NSPSO, and AWPSO in order to solve the I-beam designing problem. The results obtained from multi-objective optimization algorithms for solving I-beam designing problem indicate the superiority of the proposed algorithm in comparison to other known algorithms.

According to the energy crisis at the present time, one of the important challenges in societies, especially developing countries such as Iran is the optimal usage of energy that has the important role in the progress and sustainable development of countries. Therefore, the purpose of the present study is to investigate the energy efficiency and its priority in Iran manufacturing industries and its changes between 84-93. To do this, first, based on inputs and outputs that are identified according to the theoretical models, the energy efficiencies of industries with Data Envelopment Analysis (DEA) and the CCR model are computed and with the Anderson and Peterson model, the priority of industries respect to the energy efficiency is determined. The results have shown among 134 industries about 10 industries just among the period under consideration was efficient and more than half of the industries have experienced energy productivity.