Adaptive model predictive inventory controller for multiproduct batch plant

Abstract

An inventory control system was developed for multiproduct batch plants with an arbitrary number of batch processes and storage units. Customer orders are received by the plant at order intervals and in order quantities that are subject to random fluctuations. The objective of the plant operation is to minimize the total cost while maintaining inventory levels within the storage or warehouse capacity by adjusting the startup times, the quantities of raw material orders, and production batch sizes. An adaptive model predictive control algorithm was developed that uses a periodic square wave model to represent the flows of the material between the processes and the storage units. The boundedness of the control output and the convergence of the estimated parameters in implementations of the proposed algorithm were mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of this approach was demonstrated by performing simulations. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1867–1880, 2015

The Influence of Raw Material Inventory Control on the Production Process at PT. Dayup Indo in North Jakarta

“…Meanwhile, the quantitative approach is an approach that builds facts, provides descriptive statistics, tests theories, shows relationships and influences as well as comparisons between variables, estimates and enlivens the results. Casual associativity in this research is used to determine the extent of the causal relationship of the influence of raw material inventory control as variable X and the production process as variable Y (Kyriakidis, 2016;Yi & Reklaitis, 2015).…”