(392t) Towards an Integrated Design and Scheduling Framework for Supply Chain Network Resilience

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 methodological framework incorporating stochastic optimization for design, while being cognizant of disruptions, and model predictive control to generate optimal recourse actions for real-time applications^[4]. Further, to overcome computational costs of solving large-scale stochastic optimization problems, we propose an iterative feasibility-test based approach. The approximated approach involves solving lower-dimensional stochastic problems while verifying feasibility of the obtained design under disruption scenarios. The scenarios reflect disruptions occurring across geo-temporal scales that may occur independently, simultaneously, or in a cascading sequence. The optimization formulation captures trade-offs between economic aspects (total design and operational costs) and resilience objectives (service level) through epsilon-constrained method. Its application is illustrated by a multi-echelon distribution network case study^[5]. The study is setup using python programming environment using energiapy^[6], pyomo^[7], and mpi-sppy^[8] packages. The results showcase design optionalities for varying levels of risk-averseness and optimal reactive actions against disruptions. (233 words)

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[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]. 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.

[5]. 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.

[6]. Kakodkar, R., & Pistikopoulos, E. (2023). Energiapy-an Open Source Python Package for Multiscale Modeling & Optimization of Energy Systems. In 2023 AIChE Annual Meeting. AIChE.

[7]. 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.

[8]. 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.