1. Multi-scale Modeling & Optimization of Energy & Manufacturing Value Chains
2. Resilience-aware Supply Chain Design & Operations through Multi-objective & Stochastic Optimization
3. Application of Machine Learning Methodologies in Surrogate Modeling and Forecasting
The growing complexity and fragmentation of globalized supply chains have rendered them increasingly exposed to a diverse array of disruptions including, but not limited to, pandemics, tariffs & trade wars, geopolitical upheavals, infectious diseases, and cyberattacks[1]. Black swan events, or low-probability high-impact events, have revealed significant structural weaknesses in global supply chain systems, underscoring their limited capacity to effectively manage unprecedented and unanticipated crises[2]. To this end, it is imperative to comprehensively leverage proactive and reactive strategies bolstering supply chain resilience while maintaining cost-competitiveness in the market[3].
To address this, we propose a holistic methodological framework incorporating stochastic analysis[4] for resilient-aware network design, and model predictive control strategies to generate optimal recourse actions for real-time applications[5]. Principles of expected stochastic flexibility analysis are leveraged to generate design optionalities while hedging against continuous (demand or material availability) and discrete (industry accidents or labor strikes) uncertainties[4]. The uncertainties reflect disruptions occurring across geo-temporal scales that may occur independently, simultaneously, or in a cascading sequence. Further, a rolling horizon model-predictive control methodology is adopted to effectively and rapidly respond during disturbances[5]. The optimization formulation captures trade-offs between economic aspects (total design costs) and resilience objectives (service level) through epsilon-constrained method. Its application is illustrated by a multi-echelon distribution network case study[6]. The study is setup using python programming environment using energiapy[7], pyomo[8], ppopt[9], and mpi-sppy[10] packages. The results showcase design optionalities for varying levels of risk-averseness and optimal reactive actions against disruptions.
[1]. El-Halwagi, M. M., Sengupta, D., Pistikopoulos, E. N., Sammons, J., Eljack, F., & Kazi, M.-K. (2020). Disaster-Resilient Design of Manufacturing Facilities Through Process Integration: Principal Strategies, Perspectives, and Research Challenges. Frontiers in Sustainability, 1.
[2]. Iakovou, E., & White, C. (2020). How to build more secure, resilient, next-gen US supply chains. Brookings Institute TechStream; https://www.brookings.edu/techstream/how-to-build-more-secure-resilient… .
[3]. Gopal, C., Tyndall, G., Partsch, W., & Iakovou, E. (2023). Breakthrough Supply Chains: How Companies and Nations Can Thrive and Prosper in an Uncertain World. McGraw Hill Professional.
[4]. Straub, D. A., & Grossmann, I. E. (1990). Integrated stochastic metric of flexibility for systems with discrete state and continuous parameter uncertainties. Computers & Chemical Engineering, 14(9), 967-985.
[5]. Subramanian, K., Rawlings, J. B., Maravelias, C. T., Flores-Cerrillo, J., & Megan, L. (2013). Integration of control theory and scheduling methods for supply chain management. Computers & Chemical Engineering, 51, 4-20.
[6]. Ivanov, D., Pavlov, A., & Sokolov, B. (2014). Optimal distribution (re)planning in a centralized multi-stage supply network under conditions of the ripple effect and structure dynamics. European Journal of Operational Research, 237(2), 758–770.
[7]. Kakodkar, R., & Pistikopoulos, E. (2023). Energiapy-an Open Source Python Package for Multiscale Modeling & Optimization of Energy Systems. In 2023 AIChE Annual Meeting. AIChE.
[8]. Hart, William E., Jean-Paul Watson, and David L. Woodruff. "Pyomo: modeling and solving mathematical programs in Python." Mathematical Programming Computation 3(3) (2011): 219-260.
[9]. Kenefake, D., & Pistikopoulos, E. N. (2022). Ppopt-multiparametric solver for explicit mpc. In Computer Aided Chemical Engineering (Vol. 51, pp. 1273-1278). Elsevier.
[10]. Knueven, B., Mildebrath, D., Muir, C., Siirola, J. D., Watson, J. P., & Woodruff, D. L. (2023). A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty. Mathematical Programming Computation, 15(4), 591-619.