Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints

摘要: Chance constrained programming admits random data variations and permits constraint violations up to specified probability limits. Different kinds of decision rules optimizing objectives may be used so that, under certain conditions, a problem not necessarily linear can achieved that is deterministic---in all elements have been eliminated. Existence such “deterministic equivalents” in the form convex problems here established for general class following 3 classes 1 maximum expected value “E model”, 2 minimum variance “V “P model”. Various explanations interpretations these results are supplied along with other aspects chance programming. For example, model” interpreted H. A. Simon's suggestions “satisficing” studied relative more traditional associated “E” variants.