Robust optimization for fleet planning under uncertainty

摘要: Abstract We create a formulation and solution procedure for fleet sizing under uncertainty in future demands operating conditions. The focuses on robust optimization, using partial moment measure of risk. This risk is incorporated into the expected recourse function two-stage stochastic programming formulation, decomposition used as procedure. A numerical example illustrates importance including problem nature fundamental tradeoff between acquiring more vehicles accepting potentially high costs if insufficient resources are available.