Rate My Professor Lily Wong

Dr. Lily Wong is a Lecturer in the School of Engineering at Monash University Malaysia. She earned her PhD in Applied Mathematics from Universiti Malaya in 2018, with a dissertation entitled "Modified Ant Colony Optimization Algorithms For Deterministic and Stochastic Inventory Routing Problems." Prior to this, she obtained a Master of Science in Applied Mathematics from Universiti Putra Malaysia in 2009, focusing on "Heuristic Placement Routines For Two-Dimensional Rectangular Bin Packing Problem," and a Bachelor of Science (Honours) in Mathematics from the same institution in 2007.

Lily Wong's research interests center on Applied Mathematics and Operations Research. She develops and modifies metaheuristic algorithms, particularly Ant Colony Optimization (ACO), to solve supply chain management challenges including the Inventory Routing Problem (IRP) in split delivery, multi-product, and stochastic contexts. Her investigations also include other evolutionary methods such as Artificial Bee Colony (ABC) and Genetic Algorithms (GA) applied to location routing, network routing, and job-shop scheduling problems. Notable publications comprise "Ant colony optimization for split delivery inventory routing problem" (Malaysian Journal of Computer Science, 2017), "Enhanced ant colony optimization for inventory routing problem" (22nd National Symposium on Mathematical Sciences, 2015), "A modified ant colony optimization to solve multi products inventory routing problem" (21st National Symposium on Mathematical Sciences, 2014), "Ant colony optimization for one-to-many network inventory routing problem" (IEEE International Conference on Industrial Engineering and Engineering Management, 2014), and "Population based ant colony optimization for inventory routing problem" (2014). Dr. Wong teaches ENG1090 - Foundation Mathematics and ENG2005 - Advanced Engineering Mathematics, supervises postgraduate theses on optimization topics, and recently co-authored "Explainable multi-modal approach for uncovering key predictors of stroke risk from ECG, EMG, blood pressure and respiratory signals" (Scientific Reports, 2026) with the Department of Electrical and Robotics Engineering.