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Date of Award

Fall 2012

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Chemical Engineering

Supervisor

Christopher L.E. Swartz

Language

English

Abstract

Process operability represents the ability of a process plant to operate satisfactorily away from the nominal operating or design condition, where flexibility and dynamic operability are two important attributes of operability considered in this thesis. Today's companies are facing numerous challenges, many as a result of volatile market conditions. Key to sustainable profitable operation is a robust process supply chain. Within a wider business context, flexibility and responsiveness, i.e. dynamic operability, are regarded as key qualifications of a robust process supply chain.

The first part of this thesis develops methodologies to rigorously evaluate the dynamic operability and flexibility of a process supply chain. A model is developed which describes the response dynamics of a multi-product, multi-echelon supply chain system. Its incorporation within a dynamic operability analysis framework is shown, where a bi-criterion, two-stage stochastic programming approach is applied for the treatment of demand uncertainty, and for estimating the Pareto frontier between an economic and responsiveness criterion. Two case studies are presented to demonstrate the effect of supply chain design features on responsiveness. This thesis has also extended current paradigms for process flexibility analysis to supply chains. The flexibility analysis framework, where a steady-state supply chain model is considered, evaluates the ability to sustain feasible steady-state operation for a range of demand uncertainty.

The second part of this thesis develops a decision-support tool for supply chain management (SCM), by means of a robust model predictive control (MPC) strategy. An effective decision-support tool can fully leverage the qualifications from the operability analysis. The MPC formulation proposed in this thesis: (i) captures uncertainty in model parameters and demand by stochastic programming, (ii) accommodates hybrid process systems with decisions governed by logical conditions/rulesets, (iii) addresses multiple supply chain performance metrics including customer service and economics, and (iv) considers both open-loop and closed-loop prediction of uncertainty propagation. The developed robust framework is applied for the control of a multi-echelon, multi-product supply chain, and provides a substantial reduction in the occurrence of back orders when compared with a nominal MPC framework.

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