Using Four Metaheuristic Algorithms to Reduce Supplier Disruption Risk in a Mathematical Inventory Model for Supplying Spare Parts

Due to the unexpected breakdowns that can happen in various components of a production system, failure reach targets and interruptions process are not surprising. Since this issue remains for manufactured products, halting results loss profitability or demand. In study, address number challenges associated with management crucial spare parts inventory, mathematical model is suggested determination optimal quantity orders, case an unpredicted supplier failure. Hence, system has types equipment assumed, which substituted event breakdown. This study’s inventory was developed based on Markov chain disruption. Moreover, optimum ordering policies, re-ordering points, cost values four metaheuristic algorithms were utilized include Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Moth–Flame Optimization (MFO) Algorithm, Differential Evolution (DE) Algorithm. Based results, reliable suppliers cannot meet all demands; therefore, we should sometimes count unreliable reduce unmet