种求解车间作业调度问题的混合遗传模拟退火算法

• Format: Patent
• Language: Chinese
• Published: 06.02.2018
• Subjects: CALCULATING; CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION ORPROCESSING OF GOODS; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS; TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINSTCLIMATE CHANGE

本发明通过算法求解车间作业调度问题。针对遗传算法的局部搜索能力较差，但是把握搜索过程总体的能力较强，而模拟退火算法具有较强局部搜索能力，但模拟退火算法却对整个搜索空间的状况了解不多，不便于使搜索过程进入最有希望的搜索区域等问题，本发明将遗传算法与模拟退火算法相互结合，取长补短，提出了种遗传模拟退火（GASA）混合算法，该算法先对种群执行选择、交叉、变异等遗传操作来产生新的种群，然后对新种群中各个体分别进行模拟退火过程，并以其结果作为下步遗传操作的输入，这个运行过程经过反复迭代，直到满足某个终止条件为止。 The invention relates to a hybrid genetic simulated annealing algorithm for solving a job shop scheduling problem. The job shop scheduling problem is solved through the algorithm. The hybrid genetic simulated annealing algorithm aims to solve the problems that the genetic algorithm is poor in local searching capability but high in capability of overall search process grasping, the simulated annealing algorithm has high local searching capability but knows less of conditions of the entire searching space and is inconvenient for enabling the searching process to enter the most promising searching region and the like. The genetic algorithm and the simulated annealing algorithm are combined for adopting the long points while overcoming the weak points, and the hy