## 2 Literature Review
[12] proposed an online dynamic optimization framework with an order collection time window based on order dynamics and delivery status. [PERSON] et al. [14] established a real-time order allocation and path optimization model by taking into account the real-time and timeliness of orders. Based on demand uncertainty, [PERSON] et al. [3] solved the scheduling problem of AGVs by considering the uncertainty of the replenishment time of the workstation. However, in the study of non-VRP takeout delivery, only a small number of scholars have considered the uncertainty factors in the online order delivery problem [17; 27; 28]. Considering the randomness of customer orders and the uncertainty of food preparation time, a time buffer setting using a parametric cost function approximation (CFA) was proposed by [PERSON] et al. [17]. In order to deal with the uncertainty of meal preparation time of merchants, [PERSON] et al. [27] used the fuzzy set theory to obtain the time interval for meal preparation. [PERSON] et al. [28] established a Gaussian mixture model reflecting the distribution of uncertain service times. A hybrid distribution estimation algorithm is developed to solve the problem. In the field of takeout delivery, order combination refers to the combined pick-up and combined delivery of multiple orders. At present, order combination is only discussed in the centralized transportation organization model. On this basis, this paper introduces the concept of action combination. In order to optimize the efficiency of delivery, order combination is considered under the decentralized transportation organization model. In the academic research of AGVs, the principle of one single match is mainly adopted, but the related content of action combination has not been fully considered. However, in the field of non-AGV takeout delivery, scholars have paid attention to the issue of order combination (i.e., bundling and unified distribution of multiple orders) [7; 8; 13; 17]. In addition, [PERSON] et al. [7] and [PERSON] et al. [8] considered the combined take-up of action combination in the VRP mode. [PERSON] et al. [13] proposed to combine the delivery of a group of orders that meet the time window and load requirements. At the same time, a Principal Component Analysis (PCA) and K-means cluster were adopted to decide whether to combine orders. In route planning, after-effect refers to the consideration of the impact of the previous decision on the current decision. For the study of AGVs, no scholars have specifically discussed the issue of after-effect. In the field of non-AGV takeout delivery, some scholars have paid attention to the importance of after-effect. By predicting the distribution of order demand in the future period, they consider the future order demand in the scheduling process [10; 17]. Based on the order quantity data of each restaurant, [PERSON] et al. [17] set a time buffer to consider future order demand and generate the probability of customers placing orders. [PERSON] et al. [10] studied the fairness and decentralized decision making of delivery personnel to accommodate the problem of matching new orders with delivery personnel. Based on the description above of the existing study, we integrate our research into Table 1. In Table 1, \"One-time decision\" refers to not considering whether a new customer appears, but only considering whether the customer's needs fluctuate. \"Rolling decision \"' means to consider not only the dynamics, but also the inertia routings of delivery personnel. Y means to consider the uncertain factors of the meal preparation time of restaurants, N means not to consider the uncertain factors of the meal preparation time of restaurants. c represents order combination, i.e., under the centralized transportation organization model; C represents action combination, that is, under the decentralized transportation organization model. f represents the scheduling after-effect based on prediction. F represents the scheduling after-effect based on no prediction.