نشریه تصمیم گیری و تحقیق در عملیات
سال چهارم شماره 1 (بهار 1398)

Quality loss function is common techniques in robust design terminology that consider the deviation of output from ideal point and variability as well. Mostly in practice, processes are affected by external uncontrollable factors that causes output of process to be far from ideal points with variability around its exact value. In this research, the common Taguchi quality loss function is applied to propose a new robust optimization model that able to choice optimal results of input variables. In this model, the quality loss function is expanded and a nonlinear optimization model is introduced in order to minimize the effect of environmental noise variables. At the end, a numerical example is presented to show the applicability of the proposed model for investigating the best levels of input variables in noisy process.

Nonlinear dynamical systems modeling is one of the real challenges of the real world due to the nonlinear and variable nature of time. In this paperAn Online Self-organizing Takagi-SugenoNeuro-Fuzzy System(OSO-NFS) for dynamic Nonlinear System Identification is proposed. OSO-NFS is built based on radial basis function(RBF). The algorithm has the ability to adaptive adjustment of system’s parameter and continuous evolution of the system’s structure. Structure identification and parameters estimation are performed simultaneously. The OSO-NFS starts with no hidden neuron. In structural learning, the proposed OSO-NFS uses two-step algorithm to create the suitable number of rules. A pruning algorithm is used for detecting inactive hidden units and removing them as learning progresses. The weighted recursive least square (WRLS) algorithm is used to adjust all the consequent parameters. Finally, two benchmark examples of nonlinear system identification are demonstrated to show the effectiveness of the proposed method, comparing with the other methods . The accuracy of this modeling has been compared with the other methods according to two criteria of the number of neurons (rules) and the root mean square error. According to the results, the average percentage of improvement of the answers in the number of rules obtained in comparison to the chosen method in the modeling of these two systems in both the noise and non-noise modes in the first example is 42.35% and in the second example is 29% .

Image segmentation is one of the fundamental problems in the image processing, which identifies the objects and other structures in the image. One of the widely used methods for image segmentation is image thresholding that can separate pixels based on the specified thresholds. Otsu method calculates the thresholds to divide two or multiple classes based on between-class variance maximization and within-class variance minimization. However, increasing the number of thresholds, surging the computational time of the segmentation. To combat this drawback, the combination of Otsu and the evolutionary algorithm is usually beneficial. In this paper, we proposed a hybrid method based on employing CSA and Otsu for multilevel thresholding. The obtained results compared with the combination of the Otsu method with three other evolutionary algorithms consisting of improved Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and also the fuzzy version of FA. Our evaluation on the five benchmark images shows competitive/ improved results both in time and uniformity.

In recent decades, the theory of robust optimization has been introduced as a powerful tool for optimizing uncertain processes. Regarding the Uncertainty of the glycemic load of consumed food, the main purpose of this article is to provide an optimal Iranian diet using a robust optimization to adjust the glycemic load in patients with type 2 diabetes. Diabetes type 2 is a devastating disease, in addition to cardiovascular disease, infectious and kidney diseases, causes insulin resistance and cancer and drugs of cholesterol-lowering have an increased risk of cardiovascular complications and incidence of cancer. Indeed, adjustment of nutrition is important to prevent and control or reduce the complications of diabetes. In this paper, due to the uncertainty of the glycemic load of foods, with collecting necessary nutritional information, the Iranian diet model is determined and analyzed by robust optimization method. According to this, 75 cases of food (42 Iranian food, 10 Foodstuffs for breakfast, 20 types of fruits and Fruit juices and 3 types of dairy products) have been studied locally. The benefits of this model are the ability to adapt according to the person's tastes and opinion of the nutritionist with minimizing changes for the current diet of the individual.

Deviation between raw estimation of size, time, and cost of projects and real amount of them after execution projects, will lead to several difficulties for contractors. This fact demonstrates an essential to apply scientific methods to increase accuracy of the early estimations. There are several approaches to calculate cost of projects and one of them is algorithmic modeling. This study developed an algorithmic cost model to estimate size, time, and cost of engineering services project by benchmarking from Constructive Cost Model (COCOMO), which is an empirical model for calculating the cost of software. This model uses size and cost drivers to increase accuracy of estimation. Developed model has been run for a case study and by historical data from previous projects validated.

Strategy is most resources for organizational long term improvement and if it don’t deployment successfully, this process is ineffective even if appropriate strategies are released. The most stage in strategic deployment is resource allocation for implement of strategic initiatives. With regard to resource restriction in deployment of strategy, ranking of strategic objectives with cause and effect relation in strategy map is important .In this paper after drawing of strategy map with DEMATEL method and identifying cause and effect relations between strategic objectives as network relations, the weight of each criteria is defined with analytic network process and strategic objectives in the National Iranian Oil Refining & Distribution Company are ranked and according to this ranking strategic initiative are assigned.

In today’s highly competitive business environment, which is known by characteristics of low profitability, high customer expectations for high-quality products and minimum waiting time, make companies efforts to transform constraints into opportunities for gaining competitive advantage by optimizing their business processes. In such a situation, appropriate supplier selection can play a key role in the efficiency and effectiveness of the organization and have a direct impact on reducing costs, profitability, and flexibility of a company. The purpose of this research is to provide a supplier selection model with simultaneous consideration of two sources of inventory control and pricing in the supply chain. To assess the validity and reliability of the model, the actual data of the Seven Diamond Industries Company including input materials (Hot Roll) and products (galvanized sheets) have been used. The proposed model is coded in the GAMS software and its results have been analyzed.