Immune System Based Modelling For Optimality In Flowshop Scheduling - Ghosh, Indranil;Laha, Dipak;Dan, Pranab K.
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The flowshop scheduling is one of the most well-studied production scheduling problems, that has gained wide attention in academic fields. Since a FSP with makespan criteria has been proved to be NP-hard in strong sense, producing good quality solutions by some heuristic techniques is very difficult due to large combinatorial search space. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In such situations it is pragmatic to find a near optimal solution which can be obtained rather quickly. To overcome…mehr

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Produktbeschreibung
The flowshop scheduling is one of the most well-studied production scheduling problems, that has gained wide attention in academic fields. Since a FSP with makespan criteria has been proved to be NP-hard in strong sense, producing good quality solutions by some heuristic techniques is very difficult due to large combinatorial search space. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In such situations it is pragmatic to find a near optimal solution which can be obtained rather quickly. To overcome this difficulty an artificial immune system (AIS) based algorithm is proposed to generate good solutions within considerable time span. The AIS is an intelligent stochastic problem-solving technique, which has been used in different optimization problems as reported in literature. It is a computational system inspired by theoretical immunology, observed immune functions, principles, and mechanisms in order to solve different engineering problems.
Autorenporträt
Indranil Ghosh is currently working in a research project at Indian Statistical Institute. He has obtained his M.Tech degree in Industrial Engineering & Management. His research area includes Machine Learning, Pattern Recognition, Artificial Intelligence, Image Processing, Manufacturing Systems Engineering.