ORDER ADMISSION CONTROL FOR REVENUE MANAGEMENT IN MAKE-TO-ORDER BATCH PLANTS

FACUNDO ARREDONDO; ERNESTO CARLOS MARTINEZ

Mar del Plata

Congreso; VI Congreso Argentino de Ing. Qca; 2010

Asociación Arg. de Ing. Químicos

Challenging issues for optimal capacity allocation in make-to-order (MTO) batch plants are fixed manufacturing capacity and a highly diversified product portfolio compounded with pronounced fluctuations in demand and profitability. In this work, revenue management is carried out by maximizing profits under uncertainty in high-mix, low-volume MTO production systems using an intelligent decision rule to dynamically control the inflow of one-of-a-kind orders. A novel algorithm (ARLOA) for learning and updating an order acceptance policy using data is proposed. Locally weighted regression is used to generalize the gain value of accepting or rejecting similar orders regarding attributes such as product mix, price, size and due date. The order acceptance policy is learned by classifying an arriving order as belonging either to the acceptance set or to the rejection set. For exploitation, only orders in the acceptance set must be chosen for shop-floor scheduling. For exploration some orders from the rejection set are also considered as candidates for acceptance.  Comparisons made with threshold heuristics for capacity control in a multiproduct batch plant highlight superior performance of the dynamic order admission policy resulting from selective order acceptance using revenue management.