Improved culture gene algorithm for solving multi-objective flexible job shop scheduling problem

• Main Authors: TANG QIN, HU CHENGHUA
• Format: Patent
• Language: Chinese; English
• Published: 03.05.2017
• 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

The invention relates to the technical field of job shop scheduling, in particular to an improved culture gene algorithm for solving a multi-objective flexible job shop scheduling problem. The algorithm comprises the following steps of performing process-based encoding; generating an initialized population; performing local search by a hill-climbing method; calculating fitness; judging whether an optimization criterion is met or not (if yes, generating an optimal individual and ending the algorithm, otherwise, executing the next step); performing selection; performing SPX crossover; performing mutation; performing local search by the hill-climbing method; generating a new-generation population; calculating fitness; and circulating the process. The algorithm is improved as follows: the local search is performed by utilizing the hill-climbing method, so that local optimum can be escaped for obtaining a better solution, and the calculation time can be shortened; and in addition, the crossover and mutation modes