A Mathematical Model for Lot-streaming Hybrid Flow Shop Scheduling Problem by Considering Learning Effect and Buffer Capacity

Lot streaming, is an effective technique to increase production efficiency in a productive system by splitting job into several smaller parts overlap operations between successive machines multi-stage system. In this paper, hybrid flow shop scheduling problem with lot learning effect and buffer's limitation considered. A bi-objective mathematical model presented for minimizing the total tardiness makespan. To validate proposed model, converted single objective weighting method example solved using GAMS software. Since NP-hard, NSGA-II NRGA algorithms are used solve large scale of problems first available machine (FAM) rule assign jobs from stage onwards solution representation. Also, Taguchi tune parameters. For evaluating performance algorithms, results obtained software compared outputs GA algorithms. 30 instance randomly generated six indicators compare together. After performing experiments comparing each other, show algorithm performs better than NSGAII.