ISIP: capacity planning for flood management systems under uncertainty

Abstract

An inexact two-stage stochastic integer programming (ISIP) model is developed for capacity planning of flood diversion under uncertainty. It incorporates the concepts of two-stage stochastic programming and chance-constrained programming within an interval-parameter integer programming framework. ISIP can facilitate dynamic analysis of capacity-expansion planning when uncertainties are presented in terms of probabilistic distributions and discrete intervals. Moreover, it can be used for examining various policy scenarios associated with different levels of economic penalties when the promised targets are violated. The developed method is applied to a case study of flood-diversion planning under uncertainty. Reasonable solutions are generated for binary and continuous variables. They provide the desired capacity-expansion schemes and flood-diversion patterns, which are related to a variety of trade-offs between system cost and constraint-violation risk. Decisions with a lower-risk level imply a higher system cost and an increased reliability in satisfying the system constraints; conversely, a desire for reducing the system cost could result in an increased risk of violating the system constraints.

Keywords:

• capacity planning
• flood diversion
• integer programming
• interval optimisation
• policy analysis
• stochastic
• uncertainty

Acknowledgements

This research has been supported by the Natural Science Foundation of China (50849002 and 50675074) the Major State Basic Research Development Program of China (2003CB415201, 2005CB724200, 2006CB403307), and the Natural Science and Engineering Research Council of Canada. The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestions.