OPTIMIZATION AND PERSISTENCE

Most optimization-based decision support systems are used repeatedly w ith only modest changes to input data from scenario to scenario. Unfor tunately, optimization (mathematical programming) has a well-deserved reputation for amplifying small input changes into drastically differe nt solutions. A previously optimal solution, or a slight variation of one, may still be nearly optimal in a new scenario and managerially pr eferable to a dramatically different solution that is mathematically o ptimal. Mathematical programming models can be stated and solved so th at they exhibit varying degrees of persistence with respect to previou s values of variables, constraints, or even exogenous considerations. We use case studies to highlight how modeling with persistence has imp roved managerial acceptance and describe how to incorporate persistenc e as an intrinsic feature of any optimization model.