Complete enumeration of all sequences to establish global optimality is not feas- ible as the search space; for a general job-shop scheduling problem. Ⅱ_G has an upper bound of (n!)~m. Since the early fifties a great deal of research attention has been focused on solving Ⅱ_g, resulting in a wide variety of approaches such as branch and bound, simulated annealing, tabu search, etc. However, limited suc- cess has been achieved by these methods due to the shear intractability of this generic scheduling problem. Recently, much effort has been concentrated on using neural networks to solve Ⅱ_G as they are capable of adapting to new environments with little human intervention and can minimc thought processes.
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