Logistic Resource Allocation Based on Multi-Agent Supply Chain Scheduling Using Meta-Heuristic Optimization Algorithms

Abstract

Logistics resource allocation depends on the precise scheduling of Supply Chain (SC) agents. Coordination of management across all sites, products, and production divisions is essential for effective scheduling. For multi-agent systems in heterogeneous SCs, it is crucial to have a prior understanding of production, delivery, and connectivity. Hence, an innovative meta-heuristic optimization inspired by sparrow behavior is introduced as multi-agent-based scheduling and resource allocation (MA-SRA) to resolve delivery delays and errors during delivery in logistic SC management. Allocating resources efficiently and creating workable schedules in an SC with multiple agents are the primary significant problems focused on in this research. The MA-SRA algorithm provides an achievable solution to the problem of optimizing logistics operations by combining precise scheduling with production balance and multi-agent searchers. If the scheduling operations are inadequate, sparse agents are repurposed for production based on fitness. This maintains balance and connectivity by adjusting agent ratios. Delays are minimized, and connectivity is maximized because no adjustments need to be reversed. The research findings show that the proposed approach improves operational efficiency and brings significant advantages to the industry in terms of enhanced allocation of resources, connectivity, delivery efficiency, and fewer delays and scheduling errors.