Optimization and Persistence

Gerald G. Brown, Robert F. Dell and R. Kevin Wood
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Gerald G. Brown: Operations Research Department, Naval Postgraduate School, Monterey, California 93943-5219
Robert F. Dell: Operations Research Department, Naval Postgraduate School, Monterey, California 93943-5219
R. Kevin Wood: Operations Research Department, Naval Postgraduate School, Monterey, California 93943-5219

Interfaces, 1997, vol. 27, issue 5, 15-37

Abstract: Most optimization-based decision support systems are used repeatedly with only modest changes to input data from scenario to scenario. Unfortunately, optimization (mathematical programming) has a well-deserved reputation for amplifying small input changes into drastically different solutions. A previously optimal solution, or a slight variation of one, may still be nearly optimal in a new scenario and managerially preferable to a dramatically different solution that is mathematically optimal. Mathematical programming models can be stated and solved so that they exhibit varying degrees of persistence with respect to previous values of variables, constraints, or even exogenous considerations. We use case studies to highlight how modeling with persistence has improved managerial acceptance and describe how to incorporate persistence as an intrinsic feature of any optimization model.

Keywords: programming; linear; applications; programming; integer; applications (search for similar items in EconPapers)
Date: 1997
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