Abtin Baghdadi, Fatemeh Salehi Amiri, Harald Kloft
• Modular, simulation-driven framework links inventory buffering with fleet-based distribution in precast supply chains. • Inventory shortfalls are quantified as cumulative backlog (piece-days) from time-accumulated unmet demand. • PSO-based exploration with response surfaces and feasibility-envelope mapping yields interpretable decision maps. • In the studied scenarios, doubling storage reduces backlog by ∼ 25%; staggering project starts reduces backlog by ∼ 75–80% without extra storage. • Expanding service distance ( ≈ 50 → 200 km) shrinks the feasible fleet-inventory region; delivery shortages become more likely and shortage penalties dominate the objective. Multi-project precast supply chains face coupled constraints from time-phased demand, limited storage, production/availability lags, and travel time. This study presents a modular, simulation-driven decision-support framework that links a production–inventory buffering module with storage limits and availability delay and a factory–truck–project distribution module constrained by fleet size, service distance, and per-truck limits on trips and working hours. Candidate plans are explored using particle swarm optimization, and results are synthesized via response-surface models and feasibility-envelope mapping to reveal sensitivities and regime shifts. Inventory shortfalls are quantified as cumulative backlog (piece-days), i.e., time-accumulated unmet demand. In the studied scenarios, doubling storage reduces backlog by about 25%, while staggering project start times reduces it by roughly 75–80% without additional storage. On the distribution side, increasing service distance from about 50 to 200 km substantially shrinks the feasible region and shifts outcomes from transport-driven trade-offs toward shortage-penalty dominance. Overall, the framework delivers interpretable decision maps to support storage sizing, demand shaping, and fleet/service-radius design.