# A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas
## 4 Literature on LMD Vehicle Routing
### Occasional Drivers (Crowdshipping)  
As modern society continues to adapt to the shared economy, occasional drivers (ODs) are likely to play more important roles in future delivery systems. In crowdshipping, individual ODs act as operators in the sharing economy [54]. They make their resources (vehicles and time) available to assist the LMD. Based on the advancement of social media, people had already begun using the concept of crowdshipping to have their friends deliver parcels [55]. It is believed that numerous companies have already been adopting crowdshipping as their foundational business model since 2011. Crowdshipping was popularized by Amazon from 2015 [56]. Crowdshipping takes advantage of ODs, who are sometimes more cost-efficient than company-owned fleets [57]. It especially helps delivery during peak times. Some research regards crowdshipping as a part of an open VRP, in which the drivers are not required to return to the depot [58; 59]. However, crowdshipping considers more issues that are not considered important in the traditional open VRP. While a traditional VRP considers the time windows of customers, LMD considers the time windows of the ODs' availability as well. Also, the segmentation process of LMD considers that some ODs cannot access some customers' sites due to the vehicle characteristics or driver's preferences. In the literature, the terms crowdshipping, cargo hitching [60], crowd logistics [61], passing-by vehicles [62], crowdsourcing [63], and collaborative logistics [55] refer to delivery by occasional drivers. In Figure 3, crowdshipping groups the customers based on their locations and time requirements, and assigns them to available drivers (matching). Grouping is based on either the driver's destinations [61; 64] or customers' neighborhoods [65; 66]. Scheduling and matching in crowdshipping involve many challenges [67]. Most research shows that occasional drivers have their own destinations, except for [58]. Different matching mechanisms, such as pure self-scheduling, hybrid and centralized scheduling, route matching, and bulletin-board type matching, are being used by different platform companies [68]. Table 1 shows the identified publications on crowdshipping. The nature of problems, important considerations, and solution methods are summarized in the table. Crowdshipping naturally chooses a vehicle from available ODs and company fleets (the third nature of the LMD problems). Although all of the natures of LMD problems can be seen in crowdshipping, because of the complexity of the problems, most of the literature considers at most two of them and uses heuristic algorithms for problem-solving and data analytics. In crowdshipping research, occasional drivers have their own vehicles. Vehicle loading capacity (90% of the papers), travel distance, driver's work time, or number of deliveries are commonly considered. Also, various types of time window are considered in the papers, underlining the significance of meeting delivery deadlines. Some papers include waiting time in the customer's desired timespan and penalties for late arrivals. The dynamicity consideration of the variables and parameters is mostly limited to online orders (20% of the papers), and the stochastic nature of the problem (20% of the papers) is confined to travel times and fluctuating customer demands [69; 70]. Among the papers in Table 1, just two of them use transactional data to evaluate their model, while most of them use simulated data. Various solution methods, ranging from metaheuristics to machine learning techniques, are used across the studies to deal with the complexities of ODs' logistics. Some contradictory observations are seen in the literature. While some papers advocate crowdshipping for emission reduction [66; 71; 72], some others argue that crowdshipping does not reduce environmental pollution [73].