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Abstract
This paper presents a supply chain (SC) optimization model that balances economic, environmental, and social objectives by aiming
to maximize profits while minimizing greenhouse gas emissions and service level inequalities. It simulates real-world SC issues using a fourechelon facility model with variable demands from three markets.We utilize multi-objective Markov decision processes (MOMDP) through multiobjective reinforcement learning with decomposition (MORL/D), paired with weighted sum proximal policy optimization (PPO), and compare them using a non-dominated sorting genetic algorithm II (NSGA-II). The decision variables are production and delivery quantities, leading to Pareto front sets that illustrate optimal trade-offs. Key contributions include defining a three-objective SC under a MOMDP framework, introducing a Python-based SC simulation tool called Messiah, pioneering MORL/D in multi-objective SC optimization, and comparing it with PPO and NSGA-II. Our findings reveal that MORL/D achieves more balanced outcomes in optimality, diversity, and density, with enhanced hypervolume and expected utility metrics through knowledge sharing.
to maximize profits while minimizing greenhouse gas emissions and service level inequalities. It simulates real-world SC issues using a fourechelon facility model with variable demands from three markets.We utilize multi-objective Markov decision processes (MOMDP) through multiobjective reinforcement learning with decomposition (MORL/D), paired with weighted sum proximal policy optimization (PPO), and compare them using a non-dominated sorting genetic algorithm II (NSGA-II). The decision variables are production and delivery quantities, leading to Pareto front sets that illustrate optimal trade-offs. Key contributions include defining a three-objective SC under a MOMDP framework, introducing a Python-based SC simulation tool called Messiah, pioneering MORL/D in multi-objective SC optimization, and comparing it with PPO and NSGA-II. Our findings reveal that MORL/D achieves more balanced outcomes in optimality, diversity, and density, with enhanced hypervolume and expected utility metrics through knowledge sharing.
| Original language | English |
|---|---|
| Title of host publication | 2025 International Conference on Evolutionary Multi-Criterion Optimization (EMO'25) |
| Publisher | Springer Singapore |
| Pages | 259–274 |
| Volume | 15512 |
| ISBN (Electronic) | 978-981-96-3506-1 |
| ISBN (Print) | 978-981-96-3505-4 |
| DOIs | |
| Publication status | E-pub ahead of print - 28 Feb 2025 |
Keywords
- Multi-objective optimization
- Markov decision process
- Supply chain
- Reinforcement learning
- Evolutionary algorithm
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Dive into the research topics of 'Multi-Objective Sequential Decision Making for Holistic Supply Chain Optimization'. Together they form a unique fingerprint.Projects
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MCAIF: Centre for AI Fundamentals
Kaski, S. (PI), Alvarez, M. (Researcher), Pan, W. (Researcher), Mu, T. (Researcher), Rivasplata, O. (PI), Sun, M. (PI), Mukherjee, A. (PI), Caprio, M. (PI), Sonee, A. (Researcher), Leroy, A. (Researcher), Wang, J. (Researcher), Lee, J. (Researcher), Parakkal Unni, M. (Researcher), Sloman, S. (Researcher), Menary, S. (Researcher), Quilter, T. (Researcher), Hosseinzadeh, A. (PGR student), Mousa, A. (PGR student), Glover, E. (PGR student), Das, A. (PGR student), DURSUN, F. (PGR student), Zhu, H. (PGR student), Abdi, H. (PGR student), Dandago, K. (PGR student), Piriyajitakonkij, M. (PGR student), Rachman, R. (PGR student), Shi, X. (PGR student), Keany, T. (PGR student), Liu, X. (PGR student), Jiang, Y. (PGR student), Wan, Z. (PGR student), Harrison, M. (Support team), Machado, M. (Support team), Hartford, J. (PI), Kangin, D. (Researcher), Harikumar, H. (PI), Dubey, M. (PI), Parakkal Unni, M. (PI), Dash, S. P. (PGR student), Mi, X. (PGR student) & Barlas, Y. (PGR student)
1/10/21 → 30/09/26
Project: Research