A Chance-Constrained Two-Echelon Vehicle Routing Problem with Stochastic Demands

Two-echelon distribution systems are often considered in city logistics to maintain economies of scale and satisfy the emission zone requirements cities. In this work, we formulate two-echelon vehicle routing problem with stochastic demands as a chance-constrained optimization problem, where total demand customers each second-echelon route should fit within capacity high probability. We propose two efficient solution procedures based on column generation. Key efficiency these is underlying labeling algorithm generate new columns. novel simultaneous construction routes that builds sequentially. To further enhance performance procedure, use statistical inference tests ensure chance constraints met. reduce number customer combinations for which constraint needs be verified by imposing feasibility bounds demands. With bounds, runtimes algorithms reduced significantly. The algorithm, inference, can also applied dependent, correlated, data-driven (scenario-based) distributions. Finally, show value formulation terms improved cost guaranteed routes. Funding: This work was funded Dutch Research Council (NWO) DAREFUL project [Grant 629.002.211] carried out national e-infrastructure support SURF Cooperative. Supplemental Material: online appendices available at https://doi.org/10.1287/trsc.2022.1162 .