An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem

This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into variable search for solving the permutation flow-shop scheduling problem. The first constructs as seed using recursive application of extended two-machine In this method, jobs are recursively decomposed two separate groups, and, each group, optimal is calculated based on Then overall permutation, which obtained by integrating sub-solutions, improved through technique. same technique, one also paradigm and can find arrangement subset jobs. employed search, concept critical path has been used to help process avoid unfruitful computation arrange only promising contiguous parts permutation. fashion, leaves those already have high-quality arrangements concentrates modifying other parts. results computational experiments benchmark instances indicate procedure works effectively, demonstrating solutions, in very short distance best-known within seconds typical personal computer. terms required running time reach solution, outperforms some well-known metaheuristic algorithms literature.