## 4 Current Issues of Last Mile Logistics
### Sharing Economy  
The advancement of information and communications technologies has accelerated the introduction of sharing economy in LML. Sharing economy in LML involves a way of parcel deliveries by individual, self-contracted contractors joined at a crowdsourcing platform. New technologies that help find optimal routing and parking spots in real time have been lowering the entry barriers of crowdsourcing so that more logistics service providers can join a crowdsourcing platform for last mile parcel deliveries. Crowdsourcing for last mile deliveries is likely to spread further as the digital economy grows, which may be one of the viable options for tackling rising unemployment [53]. Using a crowdsourcing platform, passenger cars in urban areas can be utilized for home delivery services. Crowd workers in LML can be characterized by open-loop car routes, drivers' wage-response behavior, interplay with the ride-share market, and service zone sizes for fulfilling last mile deliveries from shared logistics. Although sharing economy in LML is not as scalable as a traditional truck-only system, this transition to the sharing economy paradigm for LML has a potential to create economic benefits by reducing original truck fleet sizes and exploiting additional operational flexibilities [54]. Specifically, from the logistics company's perspective, using a personal car to carry delivery parcels by an individual contractor can keep costs low, as it does not require additional fleet capability even if more parcels need to be carried. Instead, the company apply surge pricing to resolve a demand-supply imbalance for the last mile parcel deliveries. This allows the logistics company to be more flexible to the logistics-related market. From the self-contracted worker's perspective, their idle capacity can be utilized for carrying parcels. Many people have idle capacity in the course of their day, and using small slivers of time to carry parcels can be one of the ways to effectively use such an idle capacity. Furthermore, this provides an avenue toward the gig economy and shows options for resolving unemployment issues. From the final customer's perspective, the reduction in high overhead business intermediaries with a low-cost technology platform can reduce the delivery cost to the customer's site. Table 3 summarizes the major issues and findings of LML studies related to sharing economy. Ref. [55] presented a mixed integer programming (MIP) model that solves a \begin{table}
\begin{tabular}{l l} \hline
**Research Area** & **Main Issue** \\ \hline \multirow{3}{*}{Sharing economy} & Impact of sharing economy in LML to employment market \\  & Operations of sharing economy in LML \\  & Environmental impact of sharing economy in LML \\ \hline \multirow{4}{*}{Proximity stations/points and hubs} & Integrating proximity stations/points into the existing LML seamlessly \\  & Searching for potential locations for these stations/points \\ \cline{1-1}  & Location-routing problem for LML \\ \cline{1-1}  & Vehicle routing problem for LML \\ \cline{1-1}  & Assessment of distributed network strategies for LML \\ \hline \multirow{4}{*}{Environmentally sustainable LML} & Multi-criteria decision making for sustainable LML \\ \cline{1-1}  & Environmental impact assessment and sustainable strategies for e-commerce LML \\ \cline{1-1}  & Integration of environmental sustainability into new LML approaches \\ \hline \multirow{3}{*}{Delivery technology innovation} & Limitations of traditional truck-or van-based last mile delivery services \\ \cline{1-1}  & Transition to innovative and environment-friendly last mile delivery services using advanced \\ \cline{1-1}  & vehicle technologies \\ \hline \end{tabular}
\end{table} 
Table 2: Typology of LML-related research. vehicle routing problem (VRP) in which vehicles are operated by crowdsourced occasional drivers. Ref. [56] extended the work of [55] to an MIP model considering multiple parcel deliveries for each crowdsourced driver. Ref.