A Solution Algorithm for Exploring Shop Scheduling Problems

Combinatorial optimization is widely used in assignment problems, such as traveling salesman problem (TSP), scheduling problems, et al. In this study, an evolutionary-based block model (EBBM) was proposed to enhance the speed of convergence so as to avoid premature convergence problem. The main idea of blocks is to find key blocks from chromosomes and use these blocks to improve evolutionary algorithms (EAs) to solve combinatorial optimization problems (COPs). Block is a kind of information that explores the effects of individual genes on the evolution of chromosomes, containing information that is helpful for evolution and information that hinders evolution. In this paper, these two different kinds of information are stored and used. The evolution direction of the information providing algorithm, through the influence of two different information, not only improves the convergence speed of the algorithm, but also improves the diversity of the algorithm solution, so as to achieve goals of high stability and good solution quality. The block mechanism proposed includes building a probability matrix, generating blocks by associated rule and applying blocks to construct artificial chromosomes. Since the block is used as the basic unit for constructing the artificial solution. In this paper, the blocks mined by the association rules not only have diversity, but also control the block in formation strength required for the evolution process according to the set confidence level. Finally, in order to confirm the quality of solutions, the proposed approach is experimentally implemented on permutation flow-shop scheduling problem (PFSP). According to the average error rate, the optimal error rate, and the convergence curve, the solution effect of the algorithm is discussed, and the experimental results show that the block mechanism by positive and negative information is useful to improve the speed of convergence and avoid premature convergence problem. In additional, the results also demonstrate that BBEM is applicable and efficient to the COPs.