# Enhancing E-Grocery-Delivery-Network Resilience with Autonomous Delivery Robots
## 2 Literature Review  
During recent years, increasing attention has been paid to the resilience of freight systems. Many studies on the topic of resilience reported in the literature cover a wide range of mode-specific problems, such as port coalitions [7], train intermodal transportation [8], airline cargo transportation [9] and urban delivery [10; 11]. Since the outbreak of COVID-19, there has been fresh interest in resilient last-mile delivery, especially smart mobility-assisted delivery. For example, drones have played an important role in cargo delivery regarding disaster relief [12] and resilience [13]. Drones provide an effective solution in rural and low-density suburban areas, far-flung regions, and some areas where road accessibility is limited or in some situations where roads limit the fast delivery of goods. However, functional constraints (e.g., landing space, security, payload) may bring challenges associated with the delivery of parcels in areas of very high population density, which reduces the deployment of drones at scale in dense urban areas. Compared to drones, ADRs with built-in parcel lockers may unlock higher service levels for urban and high-density areas [14; 15; 16]. These ADRs would be stationary in certain areas, communicating to consumers, moving, and then making home deliveries. This kind of robot navigates on a road at a low speed and can deliver groceries with a limited weight compared with a traditional vehicle. To determine the route of a truck, schedule the drop-off locations of a robot, and plan the ADR-assisted delivery, [PERSON] et al. modeled the delivery as a vehicle routing problem (VRP) with synchronization, where the late delivery numbers to customers were minimized [14]. However, that current model does not account for the potential role of ADR in enhancing the resiliency of a dynamic E-grocery delivery network. To plan a resilient E-grocery network, in this study, we focused on the ADR-assisted two-echelon vehicle routing problem (2E-VRP). The 2E-VRP is attributed to the VRP with two-echelon delivery connected by micro hubs. [PERSON] and [PERSON] (2015) originally formulated drone-assisted last-mile delivery as a collaborative truck-and-drone routing problem [17]. [PERSON] et al. (2018) then extended [PERSON]'s work by considering multiple drones launched from a single truck [18]. Afterwards, [PERSON]. et al. (2021) described an ADR-assisted delivery network by developing a 2E-VRP with vehicle synchronization constraints [19]. These previous studies assumed that the service time of vehicles and demands are deterministic, and the objective was to minimize the operation cost or travel time. In spite of the fact that the deterministic 2E-VRP is considered a generalization of the VRP [20], various methods have still been developed to study the particular problems in the 2E-VRP. This is because the two sub-problems, the location-allocation problem and VRP, were thought to be NP-hard issues [21; 22; 23; 24; 25], which cannot be solved in a polynomial time. It is also challenging to optimize the realistically sized problems using purely specific methods. Considering the shortcomings of the methodology, metaheuristics approaches were chosen to optimize larger data sets, such as constructive algorithms [26; 27; 28], variable neighborhood-based metaheuristics [29; 30; 31; 32], the adaptive large neighborhood search [33; 34; 35], tabu search [36], multi-start iterated local search [37], and tree-based search algorithm [38]. However, in reality, various parameters of the problem may be uncertain, e.g., travel times, customer demands, and/or customer presence. Although many papers consider stochastic variants of the classical single-echelon vehicle routing problem [39], limited number of papers deal with stochastic variants of the 2E-VRP [40; 41; 42]. [PERSON] et al. (2017) [40], [PERSON] et al. (2017a) [41] and [PERSON] et al. (2023) [42] consider stochastic customer demands and capture the uncertainty with a recourse action, in which the stochastic service time is not considered.