Optimal production planning and scheduling in chemical industries

Abstract

This thesis presents novel mathematical models and efficient solution methods for the production planning and scheduling problems arising in chemical process industries. First, a production planning for chemical process networks with uncertain demand forecast scenarios over a long-range horizon is addressed. Capacity expansion timing and sizing of each chemical processing unit are determined including sales, purchase, and operating amount to maximize the expected net present value while enforcing the deviation of net present values and the excess capacity over a given time horizon to be small. A multiperiod mixed integer nonlinear programming optimization model that is both solution robust and model robust for any realization of demand scenarios is developed using the two-stage stochastic programming programming modeling framework. Two example problems are considered to illustrate the effectiveness of the model. Next, a multiperiod optimization model is proposed for addressing the supply chain optimization in continuous flexible process networks. The main feature of this study is that detailed operational decisions are considered over a short-time horizon ranging from one week to one month. For given flexible process networks where dedicated and flexible processes coexist, the supply chain for sales, intermittent deliveries, production shortfalls, delivery delays, inventory profiles, and job change overs will be taken into account. Such a rigorous optimization model that can cover decisions over one month, requires efficient solution strategies to reduce the computational expense. A bilevel decomposition algorithm is described that involves a relaxed problem(RP) and a subproblem (SP) for the original supply chain problem. Decisions for purchasing raw materials is made in the RP problem in which the changeover constraints are relaxed, yielding an upper bound to the profit. In the SP subproblem, fixing the delivery provided in RP, the supply chain optimization ...