A bio-inspired reorder optimisation framework for lumber supply chain management using a problem-adapted European beaver optimiser

The paper proposes a problem-adapted bio-inspired optimisation framework, termed the European Beaver Optimiser (EBO), to improve reorder process optimisation in lumber supply chain management. A lumber supply chain decision problem is formulated with ordering, holding, shortage and transportation costs, incorporating lead times and inventory balance constraints. The supply chain is modelled via an agent-based approach, where an inventory-management agent uses EBO to optimise reorder point and order quantity under stochastic demand and operational uncertainty. Across all scenarios, EBO produces lower median total supply chain cost than the non-optimised baseline policy. Cost distributions under EBO exhibit reduced variance (narrower interquartile ranges), indicating more predictable performance, particularly under high demand variability and supply disruptions. The work contributes (i) a problem-adapted bio-inspired optimisation scheme tailored to reorder-point, order-quantity and safety-stock search in lumber supply chains, (ii) an integration framework linking the search process with operational supply chain decisions under uncertainty and sustainability-related cost components and (iii) comparative evidence against a non-optimised baseline and established benchmark optimisers, supported by sensitivity and runtime analyses.