INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)
Year:2010 | Volume:21 | Issue:1|Papers Count:12

When a control chart shows an out-of-control condition, a search begins to identify and eliminate the root cause(s) of the process disturbance. The time when the disturbance has manifested itself to the process is referred to as change point. Identification of the change point is considered as an essential step in analyzing and eliminating the disturbance source(s) effectively. When a process control is based on a quality characteristic vector, identification of the change point alone would not help practitioners to an effective elimination of the source(s) contributing to the out-of-control condition. This paper provides a control scheme based on artificial neural networks to identify the change point in a mean vector when the change type is monotonic and at the same time allows one to perform effective diagnostic analysis to identify the variable(s) responsible for the change in a bivariate process. To the best of our knowledge, this the first time that an artificial neural networks scheme is presented to identify the change point and simultaneously perform diagnostic analysis in a multivariate environment when a monotonic change appears in the mean vector. Average run length criterion is used as a vehicle to investigate the performance of the proposed scheme numerically under different correlation structure. Simulation results indicate effective performance for the proposed scheme.

In this paper, three-stage assembly flowshop scheduling is considered with respect to minimizing bi-objectives, namely mean flow time and mean tardiness. This problem is a model of production systems, which several production operations are done simultaneously and independently, and then produced components are collected and transferred to an assembly stage for the final operation. In this model, by considering sequence-dependent setup time and components transformation times in order to make a real situation for the considered model, a lower bound (LB) is introduced to completion times of all the jobs. Further, due dates are generated randomly in a determined interval for some examples. To validate the proposed model, the Lingo 8.0 software and an enumeration technique that is coded in MATLAB are used. Comparison between the results of the Lingo 8.0 and enumeration technique shows that in larger problems (say n>8, where n is the number of jobs) the results obtained by Lingo do not have the good efficiency and cannot be compared with the enumeration technique in terms of computational time and deviation from the minimum objective function. For example, in some large problems, the objective function values obtained by the Lingo 8.0 software have 20% deviation from their minimum. Therefore, to solve such a hard problem, a meta-heuristic method is required as future research.

Optimization of maintenance resources to maximize the system availability is a major concern in different manufacturing systems. Therefore, a lot of effort is put to construct optimization models to reach the maximum availability level and to reduce the costs of lack of availability. However, despite these efforts, data uncertainty in the real world problems was neglected in proposed models which results models with sophisticated nonlinear constraints. In this article the budget uncertainty model of optimization is considered and its counterpart in resources allocation for maintained systems is presented. The proposed model takes into account the degrees of conservativeness to introduce a robust model of optimization. This model is capable of maximizing the availability for the maintained system under certain conditions. The capability of the model in controlling the effects of uncertainty is investigated by means of simulation studies and reasonable results concluded.

Brands have been increasingly considered as primary capital for businesses. Thus, the concept and measurement of brand equity has interested academicians and practitioners for more than a decade, primarily due to the importance in today's marketplace of building, maintaining, and using brands to obtain a definite competitive advantage. To now, there have been three different perspectives for considering brand equity: the customer-based perspective, the financial perspective, and the combined perspective. In this research paper, we develop a quantified model for evaluating the brand equity of products. The proposed model uses BSC as a performance measurement system for evaluating the brand equity, and the Fuzzy analytic network process (FANP), which allows measurement of the inner dependency among the BSC perspectives, as well as Fuzzy analytic hierarchical process (FAHP), which is based on the independence between the perspectives in order to computing the weights of BSC perspectives and their key indicators. As well, the model uses DEA for computing and determines the relative efficiency of products brands.

Optimizing the service card in multi-depot, multi-product and multi-level vehicle routing problem is one of the most important and at the same time, the most complex problems in the transportation problems contexts. Numerous exact heuristic and meta-heuristic algorithms have been developed for different vehicle routing problems. In this paper, a binary linear programming (BLP) based model for optimizing the service card in multi-depot, multi-product and multi-level vehicle routing problems with the aim of minimizing the total distribution costs has been proposed. In other words, the purpose of this paper is to determine the optimum transportation for each product in vehicle routing problems for every route, grape and level with minimum distribution costs. Then, an effective algorithm has been developed to solve the problem. The proposed algorithm using the limited resource leveling based on the Lagrange coefficients; transform the multi-product problem into multiple single-product problems. When each mathematical model for single product vehicle routing problems is solved, the assigned capacity and the required capacity values for each product are compared and then the available and total capacity is calculated. Finally, resource leveling and service card optimization processes are applied to all single-product problems. Therefore, after several steps if no considerable improvement is observed in the solution, then the optimal solution is achieved for the original problem. Design of experiments and validation tests used in the paper indicate the optimal utilization of the limited resources and a considerable reduction in the total distribution costs. Validity of the proposed algorithm against the binary linear programming model has been verified at 95% and computational time has been reduced by 5 times.

Rapid technology changes environments, speed of the execution activities of organization should be increase. In this paper, we seek on identification and analysis of structure and environmental factors using analytical network process in projects. It tests in Alopan Company and validates by face validity approach