Scheduling and sequencing are important problems in production planning and have many applications in manufacturing and service companies. Flexible Job shop Scheduling Problem (FJSP) with parallel batch processing machines caused increasing production rate, decrease bottleneck, improve system performance and also reduce the volume of capital investment. Such as applications of this problem are integrated circuits and steel pieces factories. Many studies have been achieved on FJSP with different objectives function but, until now; don’t have been observed about Reentrant Flexible Job shop Scheduling Problem (RFJSP) with parallel batch processing machines considering non identical job size with Total Weighted Tardiness (TWT). In this study roblem has been researched. In this thesis, has been developed a Mixed Integer Linear Programming (MILP) for single batch processing machine i.e. . Then aMILP model has developed for RFJSP i.e. .By using these models, two MILP have been developed for roblem in case of all of work station machines are batch processing and also case of some station machines are batch processing machines and other are discrete machines.This problem is strongly NP-hard, so two Meta heuristic Genetic Algorithm (GA) and Simulated Annealing (SA) developed. Finally to analyse of Meta heuristic methods efficiency, 144 instance problems with different difficulty generated randomly. Instance problems left; MARGIN: 0in 0in 0pt; unicode-bidi: embed; DIRECTION: ltr; mso-layout-grid-align: none" align=left
