Khan, Kamil A.
MacGregor, John F.
Madabhushi, Pranav Bhaswanth
Marlin, Thomas E.
Okeke, Ikenna J.
Sudaresan Ramesh, Praveen
Swartz, Chris L. E.
Supervised by Dr. Chris Swartz
B. Eng. Chemical Engineering & Bioengineering Co-op, McMaster University (2010)
Imperial Oil Ltd., Process Engineering Intern, Nanticoke, Ontario (May 2008-August 2009)
Optimization-based Formulations for Design and Control of Process Supply Chains
Supply chain optimization (SCO) involves the efficient coordination of business processes, including supply chain design, purchasing, production, transportation and distribution, in order to bring greater net value to the customer at minimum overall cost. There are significant economic incentives to be realized through improved integration between business planning and operational decision-making at manufacturing facilities. Our research is concerned with two key problems in SCO:
- Design of Dynamically Resilient Process Supply Chains for Forest
The main feature of this work is an optimization-based methodology to quantitatively assess the dynamic resiliency of a supply chain design. Agility is regarded as a key attribute of a dynamically resilient supply chain for mitigating against uncertainty. Synonymous with agility is "quick-response" which enables supply chains to rapidly meet customer demands for shorter lead times. An application area for this work is the forest products industry (FPI), where a possible strategy to improve the struggling business model entails the shift from high-volume commodity products, toward low-volume, high-value speciality products. This new paradigm implies a core transformation from physically efficient (lean) to market-responsive (agile) supply chains. The effect of design on dynamic performance is explored through manufacturing and supply chain case studies relevant to the FPI. We anticipate this work to be useful in identifying bottlenecks that limit agility in supply chains.
- Robust Decision Making for Hybrid Process Supply Chain Systems
via Model Predictive Control
Model predictive control (MPC) is a promising solution for the effective control of process supply chains. The main objective of this work is to develop an effective decision-support tool for supply chain management, based on a robust model predictive control (MPC) algorithm. The tool is anticipated to be beneficial for exploiting agility in the supply chain. Project sub-objectives are as follows: (i) capture uncertainty in model parameters and demand by stochastic programming (ii) accommodate hybrid supply chain systems governed by logical conditions, and (iii) address multiple supply chain performance measures, including economics and customer service, within an integrated framework.
- Wang, H., Mastragostino, R., Swartz, C. L. E. Flexibility analysis of process supply chain networks, Computers & Chemical Engineering,, 84 409–421 (2016) [ Publisher Version ]
- Mastragostino, R., Patel, S., Swartz, C. L. E. Robust decision making for hybrid process supply chain systems via model predictive control, Computers & Chemical Engineering,, 62 (5) 37-55 (2014) [ Publisher Version ]
- Mastragostino, R., Swartz, C. L. E. Dynamic operability analysis of process supply chains for forest industry transformation, Ind Eng Chem Res,, 53 9825-9840 (2014) [ Publisher Version ]
- Mastragostino, R., Swartz, C. L. E. Operability considerations in process supply chain design for forest industry transformations, Paper 614e, AIChE Annual Meeting, Minneapolis., (2011)
R. Mastragostino and C.L.E. Swartz. "Operability Considerations in Process Supply Chain Design for Forest Industry Transformations," In American Institute of Chemical Engineering Annual Meeting, Minneapolis, Minnesota. 2011
R. Mastragostino and C.L.E. Swartz. "Model Predictive Control of Process Supply Chain Systems under Uncertainty," In Canadian Society of Chemical Engineering Annual Meeting, London, Ontario. 2011