Algorithms For Joint Scheduling Problem With Batch- Processing Machines And Non-identical Job Sizes

Production with batch-processing machines and arbitrary-size jobs is an important production scheduling problem.In this mode, jobs have arbitrary sizes and are processed in batches on machines as long as the total size of jobs in a batch does not exceed the machine capacity. The processing of a batch cannot be interrupted until all the jobs in the batch are completed. We combine production with purchasing, inventory, distribution and other parts, consider a multi-stage joint scheduling problem for manufacturers with batch-processing machines and arbitrary-size jobs, The objective is to minimize total cost and total time.This paper consider a type of two-stage joint scheduling problem with production and distribution, and a type of three-stage joint scheduling problem with production, inventory and distribution.First,six types of polynomial time algorithm are presented to solve the two-stage joint scheduling problem with different production and distribution environment,and analyze the time complexity and performance guarantee to the proof the effectiveness of the proposed algorithm. Then we design an improved ant colony optimization method to solved the two-stage and three-stage joint scheduling problem, the candidate list is generated by classification of jobs to reduce the running time.In order to proof the effectiveness of IACO, we test the performance of the proposed method using random instances.Finally,we summary the content of this paper, and prospect the future research direction.