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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.
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.
| Original language | English |
|---|---|
| Title of host publication | 2025 International Conference on Evolutionary Multi-Criterion Optimization (EMO'25) |
| Publication status | Accepted/In press - 16 Nov 2024 |
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)
1/10/21 → 30/09/26
Project: Research