Journal of Operational Research in its Applications
Year:2018 | Volume:15 | Issue:1 (56) |Papers Count:10

In this article, we analyzed the efficiency of food and beverage companies that were accepted in Securities and Exchange Organization, using a nonlinear model. This model eliminates the weak points of basic Data Envelopment analysis Models in high computing, lack of integration and too many numbers of zero weights. Taking into account that in this study we developed the basic Data Envelopment Analysis model, the research method is developmental in terms of purpose. The indices of the study were obtained from previous studies and in order to confirm these indices, the opinions of 5 stock market and university experts were considered. The data for the present study and the actual value of these indices was obtained from codal.ir website according to each company’s financial situation. In this study, by making use of coding in Lingo software, the efficiency of selected companies was calculated in CCR and two common weight methods. In order to validate the suggested model, by calculating Spearman correlation coefficient, it was indicated that the suggested model has significant correlation with two other models and it was confirmed in terms of validation. The average of proposed model is also equal to 0.81 that is reduced in comparison to the CCR model and shows the distinguishability of the model in comparison to the basic CCR model. Results show that the proposed model in addition to the elimination of weak points of the classical models, including high computing, lack of integration and too many numbers of zero weights, has a high authenticity and pretty good distinguishability.

With respect to importance of the conjugate gradient methods for large-scale optimization, in this study a descent three-term conjugate gradient method based on an extended modified secant condition is proposed. In the proposed method, objective function values are used in addition to the gradient information. Also, it is established that the method is globally convergent without convexity assumption on the objective function. Numerical experiments show reasonable computational efficiency of the suggested method.

In recent years supply chains have attracted the attention of many researchers and industrialists. Within today's competitive market, producers are not only concerned with their internal conditions but the selection of the best markets together with the best providers. Functioning of the supply chain depends on the vast chain of supplies for providing customer needs, which include ease of customer reach, on-time delivery and the required capacity within the chain for suitable performance. Functioning of the supply chain leaves the company boarder's behind. The main materials, parts, sub-assemblies and the final products and their distribution are provided through different channels. In this research Analytical Hierarchical Protocol (AHP) of the important factors in evaluation and ranking of spare parts manufacturers of National Iranian Drilling Company were identified. The important indicators were identified by experts and through pair comparison. These factors were: delivery, services, costs, quality, company capabilities, flexibility, reliability and social commitment. The statistical population for this research was all of the managers, heads and section leaders of the national Iranian Drilling company. One hundred and twenty (120) of these according to cocran formula were randomly selected. According to the results guarantee and after sale services indicators had the highest importance and the reliability indicator had the lowest importance in the evaluation of the providers.

Fair premium pricing is one of the main concerns in insurance texts. In this paper, by considering the demand functions, costs of claims and values at risks in homogenous risk groups of cargo insurance policies, we determine the optimum premiums. Higher prices lead to have higher income from policy, meanwhile the number of customers will be reduced. Therefore, optimizing the prices is necessary. In this paper, the estimated demand functions are exponential. Hence, the proposed price optimization problem is nonlinear with a nonlinear constraint. The constrain leads to having higher prices for bad risks and lower prices for good ones. In addition, the proposed model makes it possible to control the average of the values at Risks. Calculations show that for these datasets, the values of elasticities are lower for good risks. Moreover, increasing the average of values at risks reduces the optimum prices and increases the income. Meanwhile, increasing the average of values at risks for higher than special values, does not increase the income.

In recent years, with the high-paced development of technology, diversity seeking of the customers, and the rise of global rivalry, the traditional systems are not sufficient anymore to respond to the demands of the customers. In order to supply the requests of the customers, overtaking the rivals, and a longer survival in the area of production, companies need new production systems so that while the quality of their products, the costs of production as well as the prices of the products be decreased. A new production system is called mixed model assembly line (MMAL). Mixed-model assembly lines are a type of production line wherein a variety of product models with common base characteristics are assembled on it. In this paper we intend to study these kinds of lines with the goal of minimizing the total utility work cost and total idle cost in conditions in which operation time is considered as a stochastic parameter. For having a stochastic modeling, the chance constraint approach has been applied. First, we use the GAMS software to check the feasibility of the model, then because of NP-hardness nature of the problem, the improved harmony search (IHS) algorithm is proposed to solve it. Finally, having an attention to the results, it can be deduced in comparison with the exact answer from the Gams software that the results which were gotten from IHS algorithm have trivial calculation mistakes and present acceptable responses that signify the efficiency of the algorithm.

The present study evaluates two problems of single allocation hub-covering problem with star structure including two problems of maximal p-hub covering and hub covering by considering the flow transfer costs. The star structure is as there is a central hub with definite location and other hubs are connected directly to the central hub. In the first problem, the goal is selection of p-hub locations and allocation of each customer to at most one hub as total transferred demand between customers be maximum. The purpose of the second problem is minimizing the sum of constant costs of construction of hubs and flow transfer costs between the network nodes as complete covering is created in the network. In the two problems, connection of customers to hub centers and connection of hubs to the central hub is as the source to destination distance by considering discount factor to connect hub and central hub lower or equal to the predefined value. After presenting the math model in two problems, linearization is performed, and then Lagrange liberation is applied to find the right boundaries. In addition, in the second problem, valid inequalities equal to two constraints of problem are presented. Finally, the results of solution of linear, non-linear models, and using Lagrange liberation are evaluated and compared. The evaluation of these results on CAB data set shows that the linear models are better than non-linear models in terms of optimal value of objective function and implementation time. Based on the results, the bounds of Lagrange liberation are closer to the optimal solution of problems.

In dealing with undesirable outputs, applying weak disposability assumption of outputs is a common way in data envelopment analysis. Two different approaches are proposed in the literature for modeling weak disposability axiom that have created two different types of sets of production possibilities. In this paper, the technologies created with uniform and distinct abatement factors for decision making units are investigated in network DEA framework. The related DEA models for efficiency evaluation and target setting are introduced and compared. It is shown by numerical examples that the proposed model defined with distinct abatement factors has more discriminatory power than the same model defined with uniform abatement factor.

Transportation problems are usually considered in large-scale networks, where finding the optimal solution of these problems is so time-consuming and costly. Therefore, a useful method to solve the large-scale network problems is dividing them into some smaller sub-problems. In this paper, for the first time, the origin-destination (o-d) matrix estimation problem is considered through a mixed planar approach wherein the travel demands between o-d pairs are estimated in a large-scale network. A decomposition method is proposed in three phases. In each phase, the solutions of some smaller problems in comparison with the original one are estimated. In the first phase, the travel demands between the main nodes are estimated while in the second one, the flow patterns in all inner networks corresponding to the main nodes are determined. In last phase, the travel demands between primary o-d pairs are estimated using the obtained information from steps one and two.