Stochastic inventory routing with time-based shipment consolidation

Inspired by the retail industry, we consider a stochastic inventory routing problem where retailers are replenished from central warehouse using time-based shipment consolidation policy. Such dispatching policy, facing demand repetitively at fixed times, is essential in practice. It allows for easy incorporation with dependent up- and downstream planning problems such as personal staffing operations, has become standard part of transportation contracts. We provide new chance-constrained model that determines an optimal clustering groups, their associated interval, each retailers’ level. A newly developed branch-price-and-cut algorithm solves our to optimality. Its efficiency comes tailored labeling solving pricing relies, among others, on optimality pruning criterion based approximate solution 0,1-knapsack problem. Computational experiments show exact method can solve instances up 60 Besides, accommodate practitioners providing fast heuristic provides excellent solutions gap less than 1%. Finally, incorporating uncertainty already process consolidation, it results overall cost-savings 7.7% compared current state-of-the-art.