Optimization and persistence

Optimization and persistence

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Article ID: iaor19981431
Country: United States
Volume: 27
Issue: 5
Start Page Number: 15
End Page Number: 52
Publication Date: Sep 1997
Journal: Interfaces
Authors: , ,
Keywords: artificial intelligence: decision support, programming: integer
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.

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