به گروه آموزشی مهندسی صنایع و آینده پژوهی خوش آمدید، یکی از برترین مراکز علمی و پژوهشی در حوزه مهندسی صنایع و آینده پژوهی. این دانشکده با بهره‌گیری از اعضای هیئت علمی برجسته، امکانات آموزشی پیشرفته و فضای پژوهشی پویا، بستری مناسب برای توسعه دانش و مهارت‌های تخصصی فراهم کرده است.
هدف ما در گروه آموزشی مهندسی صنایع و آینده پژوهی، تربیت دانش‌آموختگانی توانمند، خلاق و متعهد است که بتوانند در عرصه‌های علمی، صنعتی و اجتماعی نقش مؤثری ایفا کنند. برنامه‌های آموزشی ما با تأکید بر به‌روزترین منابع علمی، پژوهش‌های کاربردی و تعامل مستمر با صنعت، دانشجویان را برای ورود به بازار کار و ادامه تحصیل در مقاطع بالاتر آماده می‌سازد.
Computers and Operations Research (03050548)36(8)pp. 2450-2461
In this paper, steel-making continuous casting (SCC) scheduling problem (SCCSP) is investigated. This problem is a specific case of hybrid flow shop scheduling problem accompanied by technological constraints of steel-making. Since classic optimization methods fail to obtain an optimal solution for this problem over a suitable time, a novel iterative algorithm is developed. The proposed algorithm, named HANO, is based on a combination of ant colony optimization (ACO) and non-linear optimization methods. The solution construction in HANO is broken up into two phases. The first phase determines the discrete variables (corresponding to job-machine assignment and sequencing), while the second phase determines the continuous ones (corresponding to timing of the jobs on their assigned machines) through a non-linear optimization method. The efficiency of HANO is compared with a heuristic algorithm as a real case used at Mobarakeh Steel Company (MSC), the biggest steel factory in the Middle East. In addition, the proposed algorithm is compared with Genetic Algorithm, as a search method for both discrete and continuous variables, through solving several instances. Numerical results reveal the higher efficiency of the proposed approach compared with the heuristic one used at MSC. Furthermore, the efficiency of HANO is compared with GA to show that HANO enjoys a better performance in more than 95% of the cases while in the remaining 5%, its performance efficiency shows no difference. © 2008 Elsevier Ltd. All rights reserved.
