International Journal of Industrial Engineering and Productional Research
Volume:23 Issue: 2, Jun 2012

The science parks have important role in development of technology and are able to make economic growth of the countries. The purpose of this paper is the presentation of a Fuzzy Expert System (FIS) as Intelligent Systems to evaluate the science and technology parks. One of the problems for evaluating Science and Technology parks is to have the high number of criteria and science parks which AHP method and some other MCDM methods that with them have evaluated parks are not suitable practically. Therefore presenting a system which is able to compare this high number of science parks with many criteria is one of the findings of this paper. At the end, we have described a numerical example. This paper is a useful information resource for managers of Science and Technology parks and interested parties to invest and to recognize the science parks better.

In recent years, Many manufacturing industries for promoting their efficiency have tended to use the automatic manufacturing systems. Expanding automatic systems and to increase their complexity are representing the necessity of studying a proper functional quality and using reliable equipment in such systems more than ever. In this direction, the technique of fault tree analysis (FTA), along with using other techniques such as failure mode and effect analysis (FMEA) reveals the incorrect performance states (modes) in system in order to know these modes exactly may prevent their occurance and increase their function quality. In this study, the approaches may increase the reliability of performance in an industrial robot are studied by FTA technique as a case study to show improvement in performance of equipments on automatic systems to reduce their destruction (fault) during the work, and finally access to an automatic manufacturing systems with high reliability.

The Network Design Problem (NDP) is one of the important problems in combinatorial optimization. Among the network design problems, the Multicommodity Capacitated Network Design (MCND) problem has numerous applications in transportation, logistics, telecommunication, and production systems. The MCND problems with splittable flow variables are NP-hard, which means they require exponential time to be solved in optimality. With binary flow variables or unsplittable MCND, the complexity of the problem is increased significantly. With growing complexity and scale of real world capacitated network design applications, metaheuristics must be developed to solve these problems. This paper presents a simulated annealing approach with innovative representation and neighborhood structure for unsplittable MCND problem. The parameters of the proposed algorithms are tuned using Design of Experiments (DOE) method and the Design-Expert statistical software. The performance of the proposed algorithm is evaluated by solving instances with different dimensions from OR-Library. The results of the proposed algorithm are compared with the solutions of CPLEX solver. The results show that the proposed SA can find near optimal solution in much less time than exact algorithm.

A practical application of the spherical involute surface to the forged straight bevel gears is provided and demonstrated in this work. Conjugate (pure involute) theoretical surfaces are developed from the input design parameters. The surfaces are modified to suit the actual application (automotive differential). The unloaded (or low load) tooth contact analysis of modified surfaces is performed to obtain the prediction of the contact pattern. In order to verify the procedure and predictions, actual straight bevel gears are forged by using provided surfaces, and their contact pattern is compared to the predictions. Influence of the misalignments on the gear performance is investigated in order to provide more robust design.

Design of a logistics network in proper way provides a proper platform for efficient and effective supply chain management. This paper studies a multi-period, multi echelon and multi-product integrated forward-reverse logistics network under uncertainty. First, an efficient complex mixed-integer linear programming (MILP) model by considering some real-world assumptions is developed for the integrated logistics network design to avoid the sub-optimality caused by the separate design of the forward and reverse networks. Then, the stochastic counterpart of the proposed MILP model is used to measure the conditional value at risk (CVaR) criterion, as a risk measure, that can control the risk level of the proposed model. The computational results show the power of the proposed stochastic model with CVaR criteria in handling data uncertainty and controlling risk levels.

The ever severe dynamic competitive environment has led to increasing complexity of strategic decision making in giant organizations. Strategy formulation is one of basic processes in achieving long range goals. Since, in ordinary methods considering all factors and their significance in accomplishing individual goals are almost impossible. Here, a new approach based on clustering method is proposed to assist the decision makers in formulating strategies. Having extracted the internal and external factors, after setting long range goals, the factor-goal matrices are generated according to the impact rate of factors on goals. According to created matrices, clusters including goals and factors are formed. By considering individual clusters the strategies are proposed according to the current state of clusters for the organization. By applying this new method the opportunity of considering the impact of all factors and its interactions on goals are not lost. Strategy-factor and strategy-goal matrices are utilized to validate the proposed method. To show the appropriateness and practicality of our approach, particularly in an environment with a large number of interacting goals and factors, we have implemented the approach in Mahmodabad Training Center (MTC) in Iran. The resulting goal-factor, current and dated states of clusters, also, strategy-goal and strategy-factor matrices for model validation and route branch indices for finding out how the organization achieved each goal are reported.

Nowadays, project selection is a vital decision in many organizations. Because competition among research projects in order to gain more budgets and to attain new scientific domain has increased. Due to multiple objectives and budgeting restrictions for academic research projects have led to the use of expert system for decision making by academic and research centers. The existing methods suffer from deficiencies such as solution time inefficiency, ineffective assessment process, and unclear definition of appropriate criteria. In this paper, a fuzzy expert system is developed and improved for decision making in allocating budgets to research projects, by using the analytic network process(ANP). This has led to fewer rules and regulation, faster and more accurate decision-making, fewer calculations, and less system complexity. The rules of the expert system exacted in C# environment, consider all of the conditions and factors affecting the system. We describe the results of proposed model to measure its advantages and compare to existing selection processes for 120 projects. We also discuss the potential of proposed expert system in supporting decision making. The implementation results show that this system is significantly valid in selecting high-priority projects with respect to the known criteria, decision making regarding the determination of the assessment factors, budget allocation, and providing the appropriate initiatives for the improvement of the low-priority projects.

The problem of staff scheduling at a truck hub for loading and stripping of the trucks is an important and difficult problem to optimize the labor efficiency and cost. The trucks enter the hub at different hours a day, in different known time schedules and operating hours. In this paper, we propose a goal programming to maximize the labor efficiency via minimizing the allocation cost. The proposed model of this paper is implemented for a real-world of a case study and the results are analyzed.