We develop a framework for obtaining (deterministic) Fully Polynomial Time Approximation Schemes (FPTASs) for stochastic univariate dynamic programs with either convex or monotone single-period cost functions.

Using our framework, we give the first FPTASs for several NP-Hard problems in various fields of research such as supply chain management, logistics, scheduling, economics and mathematical finance.

Joint work with Diego Klabjan (Northwestern), Chung-Lun Li (The Hong Kong Polytechnic University), James Orlin (MIT) and David Simchi-Levi (MIT).