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Table of Contents

The current study aimed at identifying the obstacles in front of the implementation of Iran's strategic program of Ministry of Sciences, Researches and Technology according to the goals of the 2026 vision agenda. Statistical population was management professors of governmental universities of Tehran with total of 195 professors, of which a sample of 110 professors were chosen by means of Cochran formula in 2010-2011 academic years. The method used was descriptive survey. Quadruple dimensions Likret questionnaire which was prepared by Delphi technique was used for collecting information. And techniques of descriptive and analytical statistics were used to analyze findings. The test for hypotheses examination is nonparametric goodness evaluation test. Results of findings show that: successive changes of high-ranking managers, ignorance of quality promotion of human resources in accomplishment of strategic program, structural centralization in universities and lack of risk-taking institutes to commercialize the technology is seen as major obstacle in front of fulfillment of the 1404 vision agenda goals. Also samples were analyzed by Kruskal Wallis formula and results shows that there is no significant relation between scientific level, ages, and background of professors
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In this paper, we study the problem of multi-criteria ABC inventory classification using an efficient artificial immune algorithm (AIA) to partially alter the traditionally incomprehensive attitude of single objective consideration of inventory control problems. Therefore, we simultaneously endeavor to investigate two different subjects. First, we incorporate various criteria such as annual dollar usage, lead time, criticality, commonality, obsolescence and substitutability into the problem of ABC inventory classification. This method is regarded in lieu of mere consideration of the annual dollar usage criteria in the traditional ABC inventory classification. Second, the proposed AIA delays the algorithm convergence due to its restraining mechanism; meanwhile, it alleviates the problem of premature convergence of existing genetic algorithm to end up with more precise ABC inventory classification. Finally, we draw an analogy between the results obtained from both algorithms applied to a real case study present in the literature. The superiority and effectiveness of our AIA is inferred from all the results obtained in various situations
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In this paper, we examine the phenomenon of Short-term overreaction and the existence of price limits on the Tehran Stock Exchange (TSE) which is based on a sample of listed stocks on the TSE for the period 2003-2008. An event study methodology is used in which the event is defined as an increase or decrease in the stock price that activates the price limit for one, two or three days. The findings confirm the occurrence of short-term overreactions on the TSE during the period under investigation, and that the price reversals cannot be attributed to the size effect.
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Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. This paper deals with the hybrid flow shop scheduling problems in which there are sequence dependent setup times, commonly known as the SDST hybrid flow shops. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. A particle swarm optimization algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes a novel particle swarm optimization algorithm approach to the scheduling of a SDST hybrid flow shop. An overview of the hybrid flow shops and the basic notions of a PSO are first presented. Subsequently, the details of a NPSO approach are described and implemented. The results obtained are compared with those computed by Random Key Genetic Algorithm presented previously.
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The main objective of this paper is identification and categorization elements and areas of forming and outbreak individual’s tacit knowledge and finding approaches for capturing individual’s knowledge. This is a research article providing an insight about tools that can be used for create and capturing tacit knowledge. To confirm the validity of model a questionnaire was designed, applied and then analyzed by some statistical methods. The paper provides a model that can be applied to Iranian organizations practically. Validity of this model is confirmed by polling the opinion of knowledge staff, data analyzing and statistical test. The paper may be beneficial for training and creating knowledge and organizational learning. The paper may be of high value to researchers in the knowledge management field and to practitioners involved with KM adoption in the organizations. It gives practical advises for creating and capturing tacit knowledge.
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Vendor-managed inventory (VMI) system is a mechanism where the supplier creates the purchase orders based on the demand information exchanged by the retailer/customer. In this paper, the performance of the traditional and VMI system is compared by using EOQ model. Mathematical modeling is applied and total inventory cost in the supply chain is used as the performance measure. The supply chain is considered in two levels, i.e., buyer and supplier, with the assumption that the supplier faces n buyers and more products as the contract party. Results of proposed model of VMI are clearly better than traditional model. In order to make the model more applicable to real-world production and inventory control problems, we expand this model by assuming a multi-product economic order quantity problem with limited warehouse-space and capital limitation. Under this condition, we formulate the problem as a non-linear integer programming model and propose a genetic algorithm to solve it. Moreover, design of experiments is employed to calibrate the parameters of the algorithm for different problem sizes. At the end, we present a numerical example to demonstrate the application of the proposed methodology.
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Integrating flexible job-shop scheduling problem (FJSP) with preventive maintenance (PM) is pondered in this paper. Minimizing the makespan for scheduling part and minimizing the system unavailability for maintenance part are simultaneously under consideration. For doing it, the assignment of n jobs on m machines in production side and executing the PM actions at appropriate time intervals in maintenance part are carried out at the same time. Also, for carrying out the maintenance side, reliability model is employed. Moreover, number of maintenance actions and maintenance intervals are not fixed in advanced. In order to ensure of obtained results, two multi-objective evolutionary algorithms (NSGA-II and NRGA) are compared. Besides, these genetic algorithms were hybridized with both well-known composite dispatching rule (CDR) and active scheduling and then compared as a two new evolutionary algorithms named CDRNSGA-II and CDRNRGA. Furthermore, the algorithms are compared with more than 4800 test instances.
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