Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Learning to play games using a PSO-based competitive learning approach
IEEE Transactions on Evolutionary Computation
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The scheduling of batch process is a kind of NP-complete problem and has been the interest of research for many years. Various methods have been applied to solve such problems and simulated annealing (SA) is the most efficient algorithm. But SA is very slow when the problem size is large. In this work, particle swarm optimization (PSO) was applied to solve the scheduling problem of multiproduct batch process. The results show that PSO is a powerful method for solving the batch process scheduling problems and is superior to the widely used simulated annealing.