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Taleizadeh A.A.,Iran University of Science and Technology | Taleizadeh A.A.,Raja University | Niaki S.T.A.,Sharif University of Technology | Aryanezhad M.-B.,Iran University of Science and Technology | Shafii N.,University of Porto
Information Sciences | Year: 2013

Multi-periodic inventory control problems are mainly studied by employing one of two assumptions. First, the continuous review, where depending on the inventory level, orders can happen at any time, and next the periodic review, where orders can only be placed at the beginning of each period. In this paper, we relax these assumptions and assume the times between two replenishments are independent random variables. For the problem at hand, the decision variables (the maximum inventory of several products) are of integer-type and there is a single space-constraint. While demands are treated as fuzzy numbers, a combination of back-order and lost-sales is considered for the shortages. We demonstrate the model of this problem is of an integer-nonlinear-programming type. A hybrid method of fuzzy simulation (FS) and genetic algorithm (GA) is proposed to solve this problem. The performance of the proposed method is then compared with the performance of an existing hybrid FS and simulated annealing (SA) algorithm through three numerical examples containing different numbers of products. Furthermore, the applicability of the proposed methodology along with a sensitivity analysis on its parameters is shown by numerical examples. The comparison results show that, at least for the numerical examples under consideration, the hybrid method of FS and GA shows better performance than the hybrid method of FS and SA. © 2012 Elsevier Inc. All rights reserved.


Taleizadeh A.A.,Iran University of Science and Technology | Taleizadeh A.A.,Raja University | Sadjadi S.J.,Iran University of Science and Technology | Niaki S.T.A.,Sharif University of Technology
European Journal of Industrial Engineering | Year: 2011

Production systems with scrapped and rework items have recently become an interesting subject of research. While most attempts have been focused on finding the optimal production quantity in a simple production system, little work appears on a joint production environment. In this research, two joint production systems in a form of multiproduct single machine with and without rework are studied where shortage is allowed and backordered. For each system, the optimal cycle length, the backordered and production quantities of each product are determined such that the cost function is minimised. Proof of the convexity of the involved objective functions of each model is provided and numerical illustrations are given to demonstrate the applicability of the proposed models. Furthermore, the results obtained by solving the models with and without rework of defective items are compared. Sensitivity analysis and some managerial insights based on the numerical illustration are provided at the end. Copyright © 2011 Inderscience Enterprises Ltd.


Taleizadeh A.A.,Raja University | Shafii N.,University of Porto | Meibodi R.G.,Shahid Beheshti University | Jabbarzadeh A.,Raja University
International Journal of Advanced Manufacturing Technology | Year: 2010

In this paper, the chance-constraint joint single vendor-single buyer inventory problem is considered in which the demand is stochastic and the lead time is assumed to vary linearly with respect to the lot size. The shortage in combination of back order and lost sale is considered and the demand follows a uniform distribution. The order should be placed in multiple of packets, the service rate limitation on each product is considered a chance constraint, and there is a limited budget for the buyer to purchase the products. The goal is to determine the re-order point and the order quantity of each product such that the chain total cost is minimized. The model of this problem is shown to be an integer nonlinear programming type and in order to solve it, a particle swarm optimization (PSO) approach is used. To assess the efficiency of the proposed algorithm, the model is solved using both genetic algorithm and simulated annealing approaches as well. The results of the comparisons by a numerical example, in which a sensitivity analysis on the model parameters is also performed, show that the proposed PSO algorithm performs better than the other two methods in terms of the total supply chain costs. © 2010 Springer-Verlag London Limited.


Hemmati H.,Raja University | Rafiei V.,Raja University | Rafiei V.,University of East London
Life Science Journal | Year: 2012

In the present conditions, financial accounting is completely territories, following recent high profile accounting failures at Enron and other firms. The debate is deregulated. This study was done to explore whether such regulation is the costs and efforts. The results of analyses contributed to the following results: Even though more laws have been passed, this has not stopped great accounting frauds from resulting in instability in capital market and they have hampered the increase of wealth of our society.


Raoofpanah H.,Raja University | Hassanlou K.,Raja University
Life Science Journal | Year: 2013

Risk measurement is one of the main stages in Project Risk Management. It quantifies risks and assesses their impact on project's outcomes (time, cost and quality). Monte Carlo simulation, as the best practice, has been used to developed several models to analyze and quantify risks in projects. Bayesian Networks (BNs), as a powerful technique for decision support under uncertainty, have attracted a lot of attention in different fields. This paper aims to use BN capabilities to introduce a new approach for project cost risk modeling. The new approach explicitly quantifies uncertainty in project cost and also provides an appropriate method for modeling complex relationships and factors in projects such as: causal relation between variables, common causal factors, formal use of expert judgments, and learning from data to update previous beliefs and probabilities.

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