IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 11, Issue 5 Ver. I (Sep- Oct. 2014), PP 73-81 www.iosrjournals.org www.iosrjournals.org 73 | Page Development of a Quality Control Programme for steel production: A case study O.E. Isaac and A.K. Le-ol Department of Mechanical Engineering, Faculty of Engineering, Rivers University of Science and Technology, Port Harcourt , Rivers State Nigeria Abstract: In the steel making process, effective scheduling is needed for improvement of productivity. This paper study a dynamic process with a release time, where the process times of job may change during production process due to uncertainties, the objective is to ensure continuity of the production process and just in time delivery of final products. A solution methodology is developed which combine a model predictive control (MPC) strategy based approach and lagrangian relaxation algorithm. The MPC approach tackle the parallel process scheduling problem, and the rolling horizon approach allows applying lagrangian relaxation algorithm to solve the model of the scheduling problem in a rolling fashion. Computational experiments are carried out comparing the proposed. Method with the passive adjustment method often adopted by some quality control engineers. The result shows that the proposed method yields significantly better results. Keywords: model predictive control, lagrangian relaxation algorithm. NOMENCLATURE I = Set of all jobs, I = {1, 2 …N}, is the total number of jobs M =Total number of identical parallel machines p i = Processing time of job i bi = Starting time of job i ri = Release time of job i Mk = Number of machines available at time k K = Total number of time units to be considered R = Total number of rolling windows during the whole time horizon U (t) = Set of jobs that have not been processed until the decision point t Wi = Weight of job i Sp = Steel make respond Rf = refining furnace Cf = Converter furnace HFS = Hybrid flow shop C i = completion time of job i in a rolling window, , I i  it is a time point (C i = k means that the job completes at the end of time unit k) O I C = Completion time of job i in the initial schedule, i  I  ik =1 job i is processed at time unit k, i  I, k = 1, 2 .., k 0 I. Introduction The metal-forming industry is an important link in the manufacturing chain, supplying extrusions, tubes, plates and sheets to many major manufacturing enterprises, including the automobile, aircraft, housing and food services, and beverage industries (Balakrishnan and Brown 1996). Iron and steel production includes several process phases (iron-making, steel-making – continuous casting and steel rolling), and is very extensive in investment and energy consumption. It is also characterized by high-temperature high-weight material flow with complicated technological processes. To accommodate customer requirements for different types of finished products with fluctuating demands, different rolling mills in the steel-rolling phase are designed with sufficient production capacity. Since the steel-making process (SP) phase needs expensive and energy-extensive equipment and runs in a continuous mode, its capacity is usually below the total capacity of the rolling stage. Effective scheduling of SP resources is therefore vital, especially in today’s highly competiti ve global steel market. The steel-making process consists of three stages: steel-making, refining and continuous casting. Each stage further includes parallel machines, as shown in figure.1 The following is a brief description of the production process.