Showing 5 results for M. Rabbani
S. M. T. Fatemi Ghomi, M. Rabbani,
Volume 18, Issue 1 (4-1999)
Abstract
Considering the network structure is one of the new approaches in studying stochastic PERT networks (SPN). In this paper, planar networks are studied as a special class of networks. Two structural reducible mechanisms titled arc contraction and deletion are developed to convert any planar network to a series-parallel network structure.
In series-parallel SPN, the completion time distribution function can be calculated only by means of multiplication and convolution operations. For the first time, series-parallel networks are studied on the basis of the structural viewpoint. These networks belong to planar networks class. A key theorem provides capability of application of these mechanisms for non series-parallel planar networks
M. Rabbani,
Volume 21, Issue 1 (7-2002)
Abstract
In most stochastic inventory models, such as continuous review models and periodic review models, it has been assumed that the stockout period during a cycle is small enough to be neglected so that the average number of cycles per year can be approximated as D/Q, where D is the average annual demand and Q is the order quantity. This assumption makes the problem more tactable, but it should not be adopted when the beck order and lost sales penalty costs are relatively small. In this paper, considering a continuous review inventory model, we relax the above assumption and we explicitly take into account the stockout period when computing the expected cycle length. Further, we consider the effect of using exact number of cycles rather than using approximate of cycles in a continuous review inventory model.
Keywords: Inventory control, Stochastic demand, Continuous review, Inventory cycle
M. Rabbani, K. Rezaie, M. M. Lotfi and M. Abadi,
Volume 23, Issue 1 (7-2004)
Abstract
In this paper, a new method for developing a lower bound on exact completion time distribution function of stochastic PERT networks is provided that is based on simplifying the structure of this type of network. The designed mechanism simplifies network structure by arc duplication so that network distribution function can be calculated only with convolution and multiplication. The selection of duplicable arcs in this method differs from that of Dodin’s so that it must be considered a different method. In this method, best duplicable arcs are adopted using a new mechanism. It is proved that duplicating numbers is minimized by this method. The distribution function of this method is a lower bound on exact network distribution function and an upper bound on distribution function of Dodin’s and Kleindorfer’s methods. After the algorithm for the method is presented, its efficiency is discussed and illustration examples will be used to Compare numerical results from this method with those from exact network distribution and Dodin’s method.
R. Tavakkoli-Moghaddam, M. Rabbani, and M.a. Beheshti,
Volume 27, Issue 1 (7-2008)
Abstract
This paper presents a nonlinear mixed-integer programming model to minimize the stoppage cost of mixed-model assembly lines. Nowadays, most manufacturing firms employ this type of line due to the increasing varieties of products in their attempts to quickly respond to diversified customer demands. Advancement of new technologies, competitiveness, diversification of products, and large customer demand have encouraged practitioners to use different methods of improving production lines. Minimizing line stoppage is regarded as a main factor in determining the sequence of processing products. Line stoppage results
in idleness of operators and machines, reduced throughput, increased overhead costs, and decreased overall productivity. Due to the complexity of the model proposed, which belongs to a class of NP-hard problems, a meta-heuristic method based on a genetic algorithm (GA) is proposed to obtain near-optimal solutions in reasonable time, especially for large-scale problems. To show the efficiency of the proposed GA, the computational results are compared with those obtained by the Lingo software.
M. Rabbani, R. Tavakkoli-Moghaddam, and H. Vahdan,
Volume 27, Issue 2 (1-2009)
Abstract
This paper presents a discounted cash-flow approach to an inventory model for deteriorating items with the
two-parameter Weibull distribution. According to our proposed model, two shortages are considered: back-orders and lost-sales,
in which the back-order rate is a varying function of the time when the shortage happens. In general, the demand rate is a linear function of the selling price. The objective of this model is to determine the optimal pricing policy and the optimal throughput time in such a way that the total net present value of profits is maximized in the given planning horizon. Finally, a numerical example is provided to solve the model presented using our proposed three-stage approach.
