Journal of Operational Research in its Applications
Year:2013 | Volume:10 | Issue:2 (37)|Papers Count:8

Steel undoubtedly is one of the most important and strategic commodity of the country. Despite the cheap labor and energy that is referred as one of the comparative advantages of Iran's economy, just because of improper use, we can not take advantage from this benefit. This article is to identify and prioritize the improvement strategies of energy consumption in industry, using multi-criteria decision making techniques. Iran's alloy steel is used as the case study. In this research in order to promote the energy efficiency, the Deming cycle has integrated with multi-criteria decision making techniques. In this model the causes of high consumption index were determined first and then benefiting from library studies and asking the experts' opinions the criteria and effective qualitative factors in evaluation of energy consumption improvement strategies in alloy steel industry of Iran was identified. Using questioners the value of decision making criteria was calculated based on Entropy method. Afterwards the obtained solutions were ranked based on multi-criteria decision making techniques.

One important issue in the field of supplier selection process is firm's constraints. Most industry manufacturing lines don't have the ability of multiple productions simultaneously. If the production was flexible, the work of production lines must be changed. However, these changes need the time and cost for supplier. Also, delivery time of raw materials is delayed for firms or plants. To solve this problem, this paper is presented an innovative idea for stone industry. So, any material is allocated to a supplier for achieving the highest possible profit using the obtained weights from selection methods (Analytical Hierarchy Process) and Putting weight on the allocation methods table (Hungarian algorithm). The results indicate that a plant or a firm can use the presented model when they are facing to the constraints of supplying and also, they don't have the responding ability to all needs simultaneously.

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who have access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in an intelligent framework called bat-neural network (BNN) to predict stock price. The capability of BNNMAS evaluated by applying on two sets of Tehran Stock Exchange data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like Generalized Regression Neural Network (GRNN). The results show that BNN significantly performs better than others, so it can be considered as a suitable tool for predicting stock price especially in a long term periods.

In this paper, a model of game theory in relation to behavior management and employees' different reasons in no institutional differences has been studied. In this model, administrators can use the tools, training, restructuring, redesigning jobs, control jobs, etc. The low productivity of staff within the organization is indifferent to prevent each of these tools; however, for administrative expenses to be incurred. In contrast, if the employee does not perform actions with indifference, indifferent managers, agencies, clients, and business partners, which will result in cost; these costs will put the survival of the organization at risk. Players, the manager and staff are indifferent to interact with each other. Director of strategy may be to find a strategy to minimize its cost, the minimum amount of staff indifference and maximize the productivity of the organization. In this scheme, indifferent staff director of strategy, the objective function of optimal strategies for each player and then manager of the parties with respect to the strategy of the game and the parameters of the model are obtained.

The purpose of this study is to present a method for evaluating the efficiency of universities and faculties from the viewpoint of Knowledge Creation by using DEA. In this paper, input indices such as human and physical resources and output indices such as explicit and implicit knowledge have been used. These indices have been concluded from surveying people and studying. The sample of this research consists of fifteen engineering departments of Amirkabir University of Technology. The data of the departments has been acquired from referring to the available documents and interviewing people in charge. In order to solve the DEA model and obtain results, DEA Master Software has been served. The conclusions drawn from this paper can lead us to the ranking of departments from the viewpoint of Knowledge Creation, frequency distribution diagram of faculties and the development potential for reaching the border of efficiency for each of them.

Likely most challenge in sciences and especially management is decision making in different positions. Meanwhile, the organization and management theories have very wide diversity, and this caused confusion of manager and analysts in selection and deployment of appropriate theory when facing real world issues. Since each theorist has gone its way and exclusively from their perspective and without regard to others' ideas, has made his theory, management theories jungle has evolved from the ideas that if haven’t proper map and guide, we may get experience confusion and misdirection. Different theories for a special problem providing various and different approaches, confusion of decision makers and managers is begin of this point that what theory is appropriate. Hence for decision makers is vital that consciously know weaknesses and strength any of organization and management theories and on this based to during exposure real world issues select appropriate theory. In this paper, a goal data envelopment analysis method with high power of resolution and proper weights distribution between the indices is use for measuring efficiency of theories. In this paper, 11 indices to title effective indices in select of the appropriate theory are identifying. In measuring efficiency rate this theories by using data envelopment analysis, was considered this indices to title outputs of organization and management theories. These indices are use in assessment and determine efficiency of 18 organization and management theories. The final results suggest that contingency management and system theories, are efficient and bureaucracy theory is inefficient in the current situation.

The issues of decision-making, the ultimate goal is to achieve appropriate and efficient decision. Given today's mining complex conditions and multiple choices, decisions, procedures, have been very useful. Open pit mines equipment selection due to high investment levels for varying conditions, an important issue is that if the wrong decision is taken, it can exploit the mine deal with that question. Multi-criteria decision-making methods for ease of use and perfect results are more fortunate among managers and engineers enjoy this. In this study, Linear Assignment (LA), Technique for order Preference by Similarity to ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) as the most important methods of multiple Attribute decision making (MADM), to select the most appropriate drilling machine for mine of SANGAN has been used. Based on the results of the most appropriate machine for drilling machine Sandvic DI100 model was selected.

Data envelopment analysis (DEA) is a non- parametric tool to measure (estimate) efficiency of a group of units having the same activity. Unlike the parametric method that the units are commonly evaluated from an inaccessible frontier, in this method the frontier is available in the true world. Efficiency obtained from DEA is a relative measure and not the true value. In other words the DEA-Scores are an estimation of true efficiency scores. Since the population distribution is unknown, the accuracy of this estimation is questioned. The bootstrap simulation introduced by Efron (1979) can be used to improve this estimation. In this paper we provide a full description of bootstrap procedure in non-parametric and probe the drawback of Ebadi (2011) that Regardless of some shortcomings of this method, the bootstrap is employed. Ebadi used the nave bootstrap to improve the estimated efficiency scores. Efron (1979) mentioned that one must be careful in using this method in non-parametric frontier models, but Ferrier and Hirschberg (1997) used the nave bootstrap in a different way. Moreover, in Ebadi (2011) there was no mention of the background investigation and reader thinks all is done by the author. This is done by Ferrier and Hirschberg (1997) that later because of some drawbacks was rejected by Siamr and Wilson (1998a).