Multiperiod planning model for product plants considering capacity and contract constraints

Abstract

TOC(Theory Of Constraints) is a philosophy that can be applied to manage and optimize the activities of the business. TOC exploits the fact that there are only a few active constraints in any system, depending on the manufacturing typologies, the market, the production technology, the cost of raw material and the added value. Constraints of purchase of raw materials, production of products and sales of products in a manufacturing firm constrain its goal to maximize its profit. In this thesis the purpose is to solve this constrained optimization problem by setting up a multiperiod planning model. As the types of contracts in the process industry are shifting from cost-plus fee contracts to lump-sum fixed price contracts, the competitive pressure is becoming higher than before. Under this competitive environment, the purchase cost of raw materials in the process industry amounts to about 60~70% of the total costs. To achieve a competitive advantage in the process industry, it is necessary to properly manage purchase and supply contracts. In chapter 2, the types of contracts that will be considered when purchasing raw materials from suppliers and selling products to customers are modeled as follows: (1) fixed price, (2) discount after certain amount, (3) bulk discount, and (4) fixed duration. In chapter 3, two types of optimization models for a process network are considered: one for the short-term planning problem and the other for the long-term planning problem. In the short-term planning problem we consider the schedule of purchase of raw materials, production from each process with fixed capacity, inventory of each product, and sales of products. In the long-term planning problem we consider the optimal selection and expansion of processes given time varying forecasts of the demands and prices of chemicals over a long time horizon. The advantages of the proposed models are highlighted in two case studies of increasing complexity. To solve the probl...