INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)
Year:2010 | Volume:20 | Issue:4|Papers Count:9

Inevitable dominance of lean system concepts on operations world in recent decades has motivated lots of companies to implement them. But not considering the preferences and limited resources has caused their failure. This paper aims at presenting a methodology for determining leanness path for companies using their leanness critical success and failure factors. For this first lean indexes importance and performance were measured using linguistic variables and then locating them in strategic lean matrix, critical success and failure were determined. Finally pareto law was used to determine the most efficient factors in these two areas.

The successful application of "revenue management" in airline industry motivated hotel industry management as well as some researchers to apply the concept and techniques of revenue management to enhance the income of hotels. In this paper, we develop a multi objective model for hotel management in stochastic environments. Since demand for rooms, and in some cases the supply, is random we introduce a robust model, in order to manage its uncertain parameters. Therefore, the objectives of the model are robustness as well as income enhancement. Furthermore, some other new assumptions such as overbooking, early (or late) departure of a passenger and different prices are also included in the model.

This paper focuses on performance indicator in supply chain management and common platform. Accordingly, a conceptual model and a decision framework for supply chains based on common platforms have been developed and proposed by using Structural Equation Model (SEM). The model is intended to work as a basis for define performance indicators. Based on the proposed framework, the possibility of choosing the best indicators of common elements is provided, taking into account the supply chain and common platform policies. In order to reach the research objectives, following an extensive study of related works, field research and case study in automotive industry, related knowledge and experience has been extracted. Analyzing the results, modeling is performed and the model has been validated.

Caused by fuzzy risk analysis importance and fuzzy numbers’ similarity usage in this field, generating an appropriate method for finding the most similar fuzzy number by desired fuzzy number is obtained a significant importance. For receiving this goal, different approaches are made for determination of fuzzy similarity amount. In this paper, first a new similarity method between generalized trapezoidal fuzzy numbers is presented. It can combine some of these different approaches for selection the most similar fuzzy number. After that this new method is utilized for completion a new fuzzy risk analysis algorithm.

This paper considers the impact of inflation on the EPQ model for deteriorating items subject to random machine breakdowns and fixed maintenance period. The demand rate is assumed to be a function of inflation and the demand increase due to inflation. The objective is to determine the optimal production uptime that minimizes the present value of expected total costs per unit time consisting of setup, corrective maintenance, inventory carrying, deterioration, and lost sales costs with consider inflation rate over a finite-planning horizon. With consideration complexity of model and using The Taylor series approximations to all exponential terms, finally a relation earned for approximate calculation the optimal production uptime. Also, the effects of inflation, deterioration, machine breakdowns etc. on the optimal production uptime the optimal are studied with the help of numerical example.

In this paper, a method is proposed for Multiple Response Optimization (MRO) by neural networks and uses desirability of each response for forecasting. The used neural network is a feed forward back propagation one with two hidden layers. The numbers of neurons in the hidden layers are determined using MSE criterion for training and test data. The numbers on neurons of the first layer last layer are equal to the numbers of the factors and responses, respectively. After training the network, forecasting phase are done by giving different factor levels to calculate desirability of different experiments. Then total desirability is calculated. The optimal combination is which have the greatest total desirability. Finally, a numerical example is expressed to illustrate the capability of the neural network. The results of the research shows that although determining of suitable neural network is time consuming but have more accuracy than Response Surface Methodology (RSM). Also, the obtained optimal combination from RSM is one of optimal solution by neural network.

The most important goals in marring of a country is to destroy its military and industrial centers. One of the most important and essential measures for preventing the vital damages to these centers or limiting them is to locate accurately these vital and critical industries. Accurate and material location of these centers is one of the most important measures which lead to decrease of future considerable related costs and to increase securely index of them. Therefore by increasing the passive defense capability of these centers the enemy's attacks possibility and destruction effects of possible attacks will be decreased. Through this study a combined model for locating of critical centers is suggested by identifying the effective factors in locating of these critical military centers and emphasizing of passive defense. In developing of the model, Hybrid model, Analytical network process, P-dispersion mathematical model, and an Improved numerical Taxonomy clustering technique (HAPIT) are used. It has been tried that these centers to be considered through three aspects of common, undesirable and military facilities.

The new product development process has not been more challenging than it is today. Change in customers’ needs, pressure of global competition, and fragmentation of markets into smaller segments, rapid and never- ending changes in technological aspects and flexible production, force the NPD teams to introduce new products to their markets as rapidly as possible and reduce its cost with the high level of quality and reliability. In this paper, the critical success factors of agile NPD are introduced by applying the factor analysis method, then the relationships between these factors and agility power, also the degree of effects between them in the new product development process in Iranian automobile industry are modeled by using the Path analysis method. Identification and applying these factors can help the NPD mangers to increase their responsiveness power of new product and capture the competitive markets.