Showing 3 results for A. Beheshti
M.r. Amin Naseri, I. Nakhaee, and M. A. Beheshti Nia,
Volume 26, Issue 2 (1-2008)
Abstract
In this paper, the problem of batch scheduling in a flexible flow shop environment is studied. It is assumed that machines in some stages are able to process a number of jobs simultaneously. The applications of this problem can be found in various industries including spring and wire manufacturing and in auto industry. A mixed integer programming formulation of the problem is presented and it is shown that the problem is NP-Hard. Three heuristics will then be developed to solve the problem and a lower bound is also developed for evaluating the performance of the proposed heuristics. Results show that heuristic H3 gives better results compared to the others.
M. Salimi, M. Jamshidian, A. Beheshti, and A. Sadeghi Dolatabadi,
Volume 26, Issue 2 (1-2008)
Abstract
The mechanical behavior of cold rolled sheets is significantly related to residual stresses that arise from bending and unbending processes. Measurement of residual stresses is mostly limited to surface measurement techniques. Experimental determination of stress variation through thickness is difficult and time-consuming. This paper presents a closed form solution for residual stresses, in which the bending-unbending process is modeled as an elastic-plastic plane strain problem. An anisotropic material is assumed. To validate the analytical solution, finite element simulation is also demonstrated. This study is applicable to analysis of coiling-uncoiling, leveling and straightening processes.
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.
