DIPIP: Dual Interval Probabilistic Integer Programming for Solid Waste Management

Abstract

In this study, a dual interval probabilistic integer programming (DIPIP) model is developed for long-term planning of solid waste management systems under uncertainty. Methods of joint probabilistic programming and dual interval analysis are introduced into an interval-parameter mixed-integer linear programming framework. DIPIP improves upon the existing interval, chance-constrained and joint probabilistic programming approaches by allowing system uncertainties expressed as probability distributions as well as single and dual intervals. Highly uncertain information for the lower and upper bounds of interval parameters can be reflected. The developed method is applied to a case study of solid waste management. The results indicate that reasonable solutions of facility expansion schemes and waste-flow allocation patterns have been generated. A tradeoff exists between economic consideration and system stability.