A stochastic location-inventory problem in multi-period closed-loop leased pallet pooling systems with palletized transport

Leased pallet pooling is widely adopted by companies using palletized transport to avoid supply chain complexity. This study investigates the integrated location-inventory problem with stochastic demand and returns in multi-period closed-loop leased pallet pooling systems with palletized transport (LPPS-PT), considering hierarchical operation centres (OCs), cyclic inventory in both forward and reverse flows, cross-regional and cross-enterprise characteristics of palletized transport, and initializing inventory configuration. The incorporated factors particularly complicate the distribution and return of leased pallets, leading to highly interdependent and complex location and inventory decisions. The problem is formulated as a two-stage stochastic programming model and solved by a tailored sample average approximation framework embedded with a progressive hedging algorithm with scenario bundles. Numerical experiments are performed on instances generated from real-world data to prove the efficiency of the proposed method. We observe that the LPPS-PT with hierarchical OCs enhances cost-effectiveness and enjoys economies of scale, the indicators of the palletized transport network and the quality of returned pallets significantly affect LPPS-PT design, and the type of stochastic demand has little effect on strategic decisions but considerably affects tactical decisions. Some managerial insights are drawn from the results.