Study On Order-based Intelligent Algorithms For Simulation Optimization Problems

Simulation optimization studies the optimization problems with simulation based evaluation, which has strong engineering background. Simulation optimization problems have hardnesses, such as large scale, multi-modal, randomness, hardness to model, time-consuming evaluation and so on, so it is always a focus both in academic and engineering fields. Production scheduling problems are a class of typical simulation optimization problems, and effective scheduling and optimization technologies are critical to realize advanced manufacturing and improve production efficiency. In recent years, computional intelligence has obtained intensive attention and wide applications in production scheduling field. Aiming at the difficulties of simulation optimization problems, this dissertation stresses the study on order-based intelligent algorithms and the applications for typical production scheduling problems.The main contents of this dissertation are as follows.Surveyes on simulation optimization, intelligent optimization and ordinal optimization are provided.For deterministic optimization problems, a class ordr-based genetic algorithm is presented by incorporating the idea of ordinal optimization into genetic algorithm (GA), which is applied to effectively solve the flow shop scheduling problems.For stochastic optimization problems, genetic ordinal optimization approach is proposed, which combines the optimal computing budget allocation (OCBA) technique with the proposed order-based GA. In addition, hypothesis-test based simulated annealing (SA) and genetic algorithms are proposed respectively by incorporating the statistical idea of hypothesis test into intelligent algorithms. All the proposed algorithms can effectively solve the flow shop scheduling with random processing time.Aimed at the problem of selecting suitable parameters and operators for SA and GA, an OCBA based approach is proposed by formulating the considered problem as a stochastic optimization problem. Furthermore, an effective adaptive GA with multiple operators is proposed with the idea of award-penalty for flow shop scheduling problems.By combining the directed graph based local search with GA, an effective hybrid algorithm is proposed for flow shop scheduling with limited buffer size.Finally, summarization and prospect are provided for the work of this dissertation.