An interval-parameter chance-constrained dynamic programming approach for capacity planning under uncertainty

In this study, an interval-parameter chance-constrained dynamic programming (ICDP) method is developed for the capacity planning of an integrated municipal solid waste (MSW) management system under uncertainty. The ICDP method integrates interval-parameter dynamic programming (IDP) and chance-constrained programming (CCP) within a general framework with advantages in uncertainty reflection, dynamic facilitation, and risk analysis. It can not only dynamically deal with uncertainties presented as interval numbers and probability distributions, but also provide all potential solutions for facility-capacity expansion under a range of violation levels. The ICDP method is applied to the long-term MSW management and planning in the City of Regina, Canada, where data envelopment analysis (DEA) technique is advanced to identify the optimal capacity-expansion scheme under different system costs and constraint-violation levels. Solutions are valuable for generating alternatives and thus help decision makers to identify desired waste management policies under various economic, environmental and system-reliability conditions.

► An interval-parameter chance-constrained dynamic programming method is developed.
► The method is applied to municipal solid waste management in the City of Regina.
► It can reflect the dynamics of system and examine risk of violating constraints.
► Data envelopment analysis is advanced to get optimal capacity-expansion schemes.
► Results can identify desired policies under various system-reliability conditions.