To remain competitive in today’s economic environment, companies must operate their supply chains (SC) efficiently by simultaneously optimizing decisions across multiple spatial and temporal levels (Figure). However, this is a challenging task because supply chains are highly dynamic and interconnected networks, often comprised of different decision makers. Thus, centralized approaches cannot be implemented in practice, and existing decentralized methods yield suboptimal solutions and are insufficient to meet industrial needs. The goal of our research is to develop optimization models and methods, as well as decision-making frameworks, for a range of SC problems, including (1) biofuel SC design (Ng and Maravelias, 2017; Ng et al., 2018), (2) inventory routing (Dong et al., 2017; Dong et al., 2018), (3) distributed operational planning (Subramanian et al., 2013; Subramanian et al., 2014), and (4) simultaneous production and distribution planning.
Dong Y, Jerome N, Maravelias CT. Reoptimization Framework and Policy Analysis for Maritime Inventory Routing under Uncertainty. Optimization and Engineering, 937-976, 19, 2018.
Dong Y, Sundaramoorthy A, Pinto JM, Maravelias CT. Solution Methods for Vehicle-based Inventory Routing in the Chemicals Sector. Computers and Chemical Engineering, 101, 259-278, 2017.
Ng RTL, Kurniawan D, Wang H, Mariska B, Wu W, Maravelias CT. Integrated Framework for Designing Spatially Explicit Biofuel Supply Chains. Applied Energy, 116-131, 216, 2018.
Ng RTL, Maravelias CT. Economic and Energetic Analysis of Biofuel Supply Chains. Applied Energy, 205, 1571-1582, 2017.
Stadtler, H. Supply Chain Management and Advanced Planning - Basics, Overview and Challenges. European Journal of Operational Research, 163(3), 575-588, 2005.
Subramanian K, Rawlings JB, Maravelias CT. Economic Model Predictive Control for Inventory Management in Supply Chains. Computers and Chemical Engineering, 64, 71-80, 2014.
Subramanian K, Rawlings JB, Maravelias CT, Flores-Cerrillo J, Megan L. Integration of Control Theory and Scheduling Methods for Supply Chain Management. Computers and Chemical Engineering, 51, 4-20, 2013.