Corresponding Author: Miguel Jaller
Introduction
In the last decade, e‐commerce has grown substantially, increasing business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer transactions. As a result, there has been a continuous growth in last mile operations, especially deliveries to residential areas, bringing along externalities such as congestion, air and noise pollution, and energy consumption. This project aims to develop an analytical framework to model last mile operations based on continuous approximation techniques. The model will help estimate the economic and environmental impacts of residential deliveries, from a growth perspective, and through comparative analyses between consumer decisions (e.g., trip complementarity and substitution, trip‐induced demand). The model will estimate impacts for freight operators (shipper, and carriers), and the community. Based on data from the National Household Travel Survey, and the American Time Use Survey, the researchers will conduct empirical analyses with the modeling framework. Moreover, to contend with the transportation issues, the team will evaluate a number of scenarios involving city logistics strategies such as the introduction of cargo consolidation facilities (CF), alternative delivery points, and the use of cargo bikes and zero emission vehicles for the last mile.
Continuous Approximation Analytical Model
Ever since the advent of computers, transportation related problems such as Traveling Salesman Problem (TSP) or Vehicle Routing Problem (VRP) have been extensively worked upon. Researchers have continuously developed and modified algorithms and solutions to these problems. However, computer optimization techniques render inefficient when handling large and complex problems. Continuous approximation is one technique that have provided a sound compromise between accuracy and practicality. Daganzo (1984a; 1984b) estimated tours using continuous approximation techniques, wherein the vehicles would stop at each customer in its tour and deliver the package. Since then, various tour based models such as Tipagornwong and Figliozzi (2014), and Estrada and Roca‐Riu (2016), Jaller (2011) have successfully adapted Daganzo’s work.
Without loss of generality, a delivery tour begins from a depot, where the fleet is loaded with the packages. Each vehicle then travels the first leg of the tour–the long‐haul, to the service region. In this study we will extend Daganzo’s work, wherein the fleet completes the last‐mile by, either (i) stopping at each customer in its tour and delivering the package, as in Daganzo’s original work, or (ii) consolidating packages at a consolidation facility and then delivering the package to the customers, or (iii) consolidating packages at stores/pick‐up points, wherein the customers travel to these stores/pick‐up points and collect their packages. Finally, the fleet returns back to the depot, as shown in Figure 1. The team will also analyze the traditional case, similar to (iii) where customers travel to and purchase products in‐store. Daganzo’s objective was to model the tour length traveled to serve N customers, by a fleet of vehicles – each vehicle serving C customers per tour, departing from a depot located at a distance p from the center of the service region of size A.
Most of the tour based models have largely been developed for the depot located far away from the center of the service region. Estrada and Roca‐Riu, 2016, did model tours departing from consolidation facilities located within the service region; however, the field of study lacks a comprehensive model that can estimate the tour length regardless of where the depot is located. There are three possibilities, within the service region, near the service region, and far away from the service region. This study will develop a comprehensive tour length model covering all the possibilities of the location of the depot. The study will evaluate a number of scenarios involving shipper‐carrier strategies and city logistics such as depot location, service region characteristics, time window, introduction of cargo consolidation facilities and alternative delivery points, and the use of cargo bikes and zero emission vehicles for the last mile. The framework will have a cost based model for a single day of operation considering delivery cost, fuel cost and emission cost. The study will also consider a planning horizon of Hyears, further accounting for fixed cost for depot operation, dependent upon the location of depot, as well as fleet purchase and resale costs.
Figure 1 Tours from the warehouse to the service region
References
Daganzo, C. F. (1984a). The Distance Traveled to Visit N Points with a Maximum of C Stops per Vehicle: An Analytical Model and an Application. Transportation Science, 18(4), 331‐350.
Daganzo, C. F. (1984b). The length of tour in zones of different shapes. Transportation Research Part B, 18B(2), 135‐145.
Estrada, M., & Roca‐Riu, M. (2017). Stakeholder’s profitability of carrier‐led consolidation strategies in urban goods distribution. Transportation Research Part E (104), 165‐188.
Jaller, M. (2011). Resource Allocation Problems During Disasters: The Cases of Points of Distribution Planning and Material Convergence Handling. Ph.D., Rensselaer Polytechnic Institute.
Tipagornwong, C., & Figliozzi, M. (2014). Analysis of Competitiveness of Freight Tricycle Delivery Services in Urban Areas. Transportation Research Record: Journal of the Transportation Research Board(2410), 76–84.