We consider supply chain scheduling with learning effects. Multiple customers located at different sites have a lot of jobs to be processed on a single machine. There are learning effects when the jobs are processed on the machine, which means the actural processing time of the job is decreased when there are some jobs processed before this job. Processed jobs are delivered in batches to their respective customers, each shipment incurs a delivery cost and takes a fixed amount of time. We study four main objective functions in supply chain scheduling theory, analyse the problem complexity and provide optimal algorithms for multiple cases.
WANG Lei , ZHANG Yuzhong , WANG Chengfei.
SUPPLY CHAIN SCHEDULING WITH LEARNING EFFECTS. Journal of Systems Science and Mathematical Sciences, 2013, 33(7): 799-806 https://doi.org/10.12341/jssms12132