## 5 New Opportunities for Design and Development of Innovative Last Mile Logistics
### Effective Management of Uncertainties in LML  
Supply chain systems inevitably involve structural and operational complexity as there are a wide variety of stakeholders, information, and materials that should be managed with their associated uncertainties [106; 107]. Supply chain complexity becomes more critical in LML in which highly distributed supply channels and end-user locations exist with multiple delivery methods; more uncertain elements should be carefully controlled to achieve the effectiveness of LML in practice. For this, the following issues should be discussed more in depth to properly handle an increase in uncertainties while operating LML:  
* Conceptualization and measurement of LML complexity: Complexity in supply chains has been variously defined depending on aspects and domains in the existing literature relevant to traditional supply chains [108]. However, the concepts and measures of complexity in LML have not been widely addressed in LML studies to date. Uncertainties that are caused by the variety, size, and dynamic operations of LML may be distinct from those in conventional logistics. Therefore, complexity in LML needs changes in definitions and measures that were originally used for conventional logistics, although basic concepts for supply chain complexity may be partially applicable to LML cases. Along with efforts to define and develop complexity measures for LML, the impact of complexity on LML performance should be investigated to understand underlying complexity dynamics in an LML system.  
* Modeling LML with dynamic factors: The dynamic nature of a city's logistics system should be reflected on LML models with new indicators and aspects that properly describe uncertainties in LML. Multiple indicators to comprehensively represent traffic congestion conditions and parking space availability, which can affect delivery productivity, should be considered as sources of complexity in LML [109]. In addition, various types of uncertainty such as demand volatility, infrastructure accessibility, conflicting objectives among stakeholders [10] are required to be incorporated into LML models. Indeed, real-time data, fleet management and dynamic route planning, and tracking devices will be important resources to develop algorithms and optimization techniques in LML to reflect real-world problems.
* Mitigation of uncertainties in LML: The complexity of LML would exponentially increase as crowd-sourcing and new delivery technologies such as drones and UAVs are actively employed in logistics operations. In this regard, studies that discuss how the impact of new uncertainties in advanced LML approaches is effectively controlled are needed. For example, operational strategies that enable a highly distributed delivery network to be simplified are required to avoid complex routes [18]. Autonomous robots that assist truck-based LML can be effective in making deliveries in relatively small areas with multiple delivery stops [92].
* Developing solution approaches: Addressing VRP or location routing problems for LML generally makes associated mathematical models more complicated. This leads to high computational complexity due to its combinatorial structure in searching for optimality. As integrating new LML concepts and approaches into existing LML operations usually increases computational complexity, well-designed algorithms and/or heuristics should be developed to solve a large-scaled problem in a polynomial time. Refs. [110, 111] classified multiple VRP variants for urban freight transportation and the associated algorithms solving the various LML problem.