Priority-based multi-objective algorithms for green supply chain network design with disruption consideration

This research answers three current supply chain issues: demand variability, disruptions, and sustainability. We develop a multi-objective supply chain network model integrating inventory decisions for each echelon under demand uncertainty and limited production capacity. The model describes the optimal productions and allocations in normal and disrupted conditions. The objective function is to minimize the total supply chain cost and emissions. To handle the complexity, we proposed a priority-based Non-dominating Sorting Genetic Algorithm II (pb-NSGA-II) and priority-based Multi-Objective Particle Swarm Optimization (pb-MOPSO) with four novel decoding procedures to accommodate the priority: ordering cost, carbon emission, backtrack priority-based decoding, and adaptive decoding. The experiments indicate that at low disruption duration, the supply chain network design (SCND) is not affected by the disruption due to the existence of safety stock. However, the SCND starts rescheduling and reallocating its demands at medium and high disruption durations.