A distributionally robust chance constraint model to demand-responsive skip planning problem

The increasing complexity of construction and demolition waste management presents significant logistical challenges, particularly in urban environments where skip logistics play a pivotal role. This paper develops a distributionally robust optimization framework to address the intricate task of multi-period scheduling and operational constraints in skip logistics, a domain where existing methods fall short. The proposed model incorporates chance-constrained formulations to explicitly handle travel-time uncertainties while acknowledging broader system variabilities including demand fluctuations and policy restrictions. Beyond theoretical contributions, we address practical implementation challenges including computational scalability and data integration requirements. Using a comprehensive dataset from Sydney, Australia, the framework’s practical applicability is demonstrated through rigorous performance evaluations, showcasing its ability to reduce violation frequencies while maintaining operational efficiency across diverse configurations. The findings not only advance the research in skip logistics optimization but also provide actionable insights for policymakers and practitioners, while suggesting promising extensions toward multi-objective optimization and dynamic replanning capabilities for sustainable and resilient construction and demolition waste management practices.