Lot-sizing and scheduling are two important issues in production planning systems which involvment any industries. Lots of researches have been presented in this subject so far. On other hands considering earliness-tardiness cost has significanted beacause of Just-In-Time production. Industries systems conclude a variety of production environment such as parallel machines that complicated solving of planning problems. In this research lot-sizing and scheduling problem on parallel machine has been studied. Holding Inventory and backlog cost has been considered as an earliness-tardiness penalties. There are a series of orders that have been justify; LINE-HEIGHT: 150%; MARGIN: 0cm 0cm 0pt -0.1pt; unicode-bidi: embed; DIRECTION: ltr; mso-add-space: auto" A mixed integer programming formulation has proposed bsed on TSP. Number of product batch calculate as a parameter before solving model. Computational resault demonstrated that the MIP use large CPU time to get resault due to complexity of problem. So in next step problem has been modeled by constraint programming method that reduce solving time significantly. So that for an instance with 2 hours CPU solving time in MIP, the CP method reduces solving time to 2 minutes. To complete the solving process, a heauristic proposed to assign orders to products. Finally a case-study in steel-mill industry shows efficiency of designed system compared with past systems. Experimental resaults shows that proposed systems has planned the orders less than 10 minutes solving time for diffrents instances while this is 1 to 2 hours for the existing system.
