Showing 3 results for Rafiei
M. Aghdasi, F. Mokhatab Rafiei and G. Moslehi,
Volume 17, Issue 1 (7-1998)
Abstract
This paper presents a summary of the results from the simulation of a given two-stages production system which uses JIT. The system consists of an assembly line with two automated assembly cells and two assembly stock points and one manufacturing cell with three manufacturing stock points for storing reserved parts and one receiving stock. Carts with fixed capacity are used for handling the parts. A kanban is attached to each cart. The minimum number of kandbans required to operate the system without shortage in stock are estimated. The effects of changing the demands mean and its variation on the number of carts are also investigated. Finally, a different simulation model is developed for the same production system using conventional job shop decipline and some main characteristics have been compared to the results, from the first model.
F. Mokhatab-Rafiei and M. Moattar Hosaini,
Volume 17, Issue 2 (4-1998)
Abstract
This paper considers the Economic Lot Scheduling Problem, that is, the problem of scheduling several products on a single facility so as to minimize holding and setup costs. Combination of frequency and timing as well as production quantity make this problem Np-hard. A heuristic is developed to obtain a good solution to ELSP. The proposed heuristic makes use of the Simulated Annealing Technique. This heuristic gives a sharper upper bound upper bound to holding and setup costs.
M. Rabbani, F. Taghiniam, H. Farrokhi-Asl , H. Rafiei ,
Volume 35, Issue 2 (2-2017)
Abstract
In this paper, the solution of a non-linear model of Cell Manufacturing (CM) in certain and dynamic conditions is
studied, considering intracellular and extracellular costs, cell constructing costs, the cost of restoration and the cost of equipment
transportation per distance travelled. Since the number of cells in each stage of production is important, by optimizing the
number of cells, additional costs can be minimized. Therefore, the main objective of this study is to investigate the optimal
number of cells located. Bio-geographical Based Optimization (BBO) algorithm is applied in the CM for the first time in the
literature and the obtained results from this algorithm are compared with the results of well-known genetic algorithm. The results
shows the good performance of genetic algorithm. Finally, the conclusion and future research are provided.
