Biased multi-objective particle swarm optimization with simulation for stochastic multi-echelon supply chain

Abstract

This paper studies production and distribution planning in a multi-level supply chain for industrial gas. The proposed solution uses a simulation-driven, agent-guided biased multi-objective particle swarm optimization (MOPSO) framework combined with discrete-event simulation (DES). This approach balances competing goals like cost, work time, and service performance under uncertain demand and production variability. The model considers mode-dependent production with different operating modes for each facility and jointly examines routing and scheduling choices. Biasing through balancing, routing, distribution, and priority agents influences initialization and search. An adaptive evaluation scheme focuses simulation efforts on promising areas of the Pareto front. The framework was tested on a real-world case in the process industry with realistic uncertain parameters. Compared to an unbiased particle swarm baseline, the proposed method achieves faster results and better Pareto-front quality within a similar simulation budget. This leads to effective production and distribution schedule that enhance operational performance and lowering cost while still meeting service targets. The findings indicate that the biased approach outperforms others in finding the best trade-offs between time, cost, and simulation efficiency, showing its potential to improve decision-making in complex and unpredictable supply chain environments.