## 1 Introduction
They propose a myopic solution of multi-period IRP and describe the scenario for each planning stage: first, the depot's initial inventory level of each demand location is revealed at the beginning of the stage. Then the decision-maker determines the inventory and routing decision. After replenishment is made, the demand occurs. The inventory retention and shortage costs are calculated at each demand location based on demand information. Finally, determine the end-of-the-day inventory levels, and repeat these procedures. The rolling horizon framework solves a short-term problem approximated as a periodic solution over a long-term horizon. Such efforts include [PERSON] et al., [PERSON] et al., and [PERSON] et al. [18; 19; 20]. The stochastic demand IRP is a difficult challenge. [PERSON] et al. consider a finite planning horizon IRP with stochastic demand [21]. [PERSON] et al. propose a cyclic distribution strategy [22]. They point out that stochastic IRP can be solved by solving a scenario tree. [PERSON] and [PERSON] study a multi-product IRP with uncertain demand and stockout [23]. They assume that the demand is only revealed when the vehicle arrives at the demand location and included a recourse mechanism consisting of a return trip to the depot when stockouts occur. [PERSON] et al. propose a two-stage decision framework to deal with a scenario-based stochastic IRP [24]. They determine the routing decisions in the first stage while the inventory policies are determined in the second stage. These studies are based on stochastic optimization, whereby it assumes that the probability distribution is known for demand. However, obtaining the probability distribution of demand in practice is extremely complex, even impossible sometimes. Robust optimization is a powerful methodology for uncertain problems with incomplete information on their probability distributions. Robust optimization is to optimize the objective when considering the worst case of uncertain parameters under the defined uncertainty set. [PERSON] and [PERSON] first propose robust optimization for solving linear programming problems with uncertain parameters, considering their worst-case values in the uncertainty set [25]. [PERSON] et al. derive a closed-form robust solution for an uncertain inventory problem with stochastic demand [26]. Their work is based on the dual balancing policies proposed by [PERSON] et al. (2017, 2018). Then, [PERSON] and [PERSON] extend their work to dual-sourcing inventory problems (2018). They provide a closed-form robust solution by normalizing the slow ordering cost and fast lead time to 0. Dual-sourcing inventory policy is using two replenishment sources, where the regular one is cheaper but slower than the other one. By interspersing one or several faster and more expensive replenishment periods in a regular one, the dual-sourcing policy can reduce retention costs and the impact of the demand uncertainty (2018). Traffic congestion will lead to a longer travel time, thus affecting the timeliness of replenishment. [PERSON] et al. study the a vehicle routing problem based on reliable link travel times (2018). The network fundamental diagram is very useful for dynamic traffic assignment, which can reflect the impact of traffic conditions at different times on routing choices. Then, [PERSON] and [PERSON] use the floating car data for choosing transportation routes (2018). [PERSON] et al. use floating and probe vehicle data to estimate energy consumption of electric vehicles (2018). In addition, [PERSON] et al. study regional transport plans, which can help the traffic situation from the perspective of upper level planning (2018). To ensure the timeliness of replenishment, it is an essential strategy to combine air and ground transportation, especially in the perishable goods supply chain and emergency supplies distribution (2018). [PERSON] et al. propose an air-land transportation vehicle routing problem, which uses drones for last-mile delivery (2018).