Query: last-mile delivery

title: Integrating first mile pickup and last mile delivery on shared vehicle routes for efficient urban e commerce distribution
abstract: In this paper, we analyze the route efficiency trade offs that emerge from combining first mile pickup and last mile delivery operations in an urban distribution system. We build on the extant literature on continuum approximation of optimal route distances and propose adjustment factors that account for the effects of integrated pickup and delivery operations. By means of comprehensive numerical experiments and regression analysis, we further propose a set of closed form adjustment factors that improve existing continuum approximation based route length estimations. These adjustment factors incorporate some non trivial route efficiency trade offs emerging from first mile and last mile integration that cannot easily be captured through continuum approximation. The proposed extensions are particularly relevant for the optimal strategic design and operational planning of large scale, high density last mile distribution systems that are gaining in importance in light of e commerce and omni channel retailing. Our analyses suggest that the efficiency gains emerging from integrating first mile pickup and last mile delivery operations can be as high as 30% However, the effective efficiency gains are sensitive to vehicle capacity constraints and other factors complicating the optimal stop sequence in integrated routes. We apply our proposed method to a real world case study informed by operational data from one of India's largest e commerce platforms for the city of Bengaluru. We find that by properly integrating its first mile and last mile operations, the company could reduce its urban traffic and emissions impact by up to 16% while increasing the asset utilization and reducing the cost of operations of its vehicle fleet.

title: Acceptance of autonomous delivery vehicles for last mile delivery in Germany Extending UTAUT2 with risk perceptions
abstract: The inevitable need to develop new delivery practices in last mile logistics arises from the enormously growing business to consumer (B2C) e commerce and the associated challenges for logistics service providers. Autonomous delivery vehicles (ADVs) are believed to have the potential to revolutionise last mile delivery in a way that is more sustainable and customer focused. However, if not widely accepted, the introduction of ADVs as a delivery option can be a substantial waste of resources. At present, the research on consumers' receptivity of innovations in last mile delivery, such as ADVs, is limited. This study is the first that investigates the users' acceptance of ADVs in Germany by utilising an extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and adapted it to the context of ADVs in last mile delivery. Quantitative data was collected through an online survey approach (n 501) and structural equation modelling was undertaken. The results indicate that price sensitivity is the strongest predictor of behavioural intention (i.e. user acceptance) followed by performance expectancy, hedonic motivation, perceived risk, social influence and facilitating conditions, whereas no effect could be found for effort expectancy. These findings have important theoretical and practical contributions in the areas of technology acceptance and last mile delivery.

title: Understanding consumers' behavior to adopt self service parcel services for last mile delivery
abstract: Self service parcel delivery service has been favored by E commerce retailers as an effective solution to the "last mile" delivery, and consumers' adoption behavior is the key point to apply this emerging technology. The aim of the present study is to empirically test the influence of psychological factors on online consumers' behavioral intention to adopt it.

title: A truck and drones model for last mile delivery: A mathematical model and heuristic approach
abstract: We present a mathematical formulation and a heuristic solution approach for the optimal planning of delivery routes in a multi modal system combining truck and Unmanned Aerial Vehicle (UAV) operations. In this system, truck and UAV operations are synchronized, i.e. one or more UAVs travel on a truck, which serves as a mobile depot. Deliveries can be made by both UAVs and the truck. While the truck follows a multi stop route, each UAV delivers a single shipment per dispatch. The presented optimization model minimizes the waiting time of customers in the system. The model determines the optimal allocation of customers to truck and UAVs, the optimal route sequence of the truck, and the optimal launch and reconvene locations of the UAVs along the truck route. We formulate the problem as a Mixed Integer Linear Programming (MILP) model and conduct a bound analysis to gauge the maximum potential of the proposed system to reduce customer waiting time compared to a traditional truck only delivery system. To be able to solve real world problem size instances, we propose an efficient Truck and Drone Routing Algorithm (TDRA) The solution quality and computational performance of the mathematical model and the TDRA are compared together and with the truck only model based on a variety of problem instances. Further, we apply the TDRA to a real world case study for e commerce delivery in Sao Paulo, Brazil. Our numerical results suggest significant reductions in customer waiting time to be gained from the proposed multi modal delivery model.

title: Two echelon vehicle routing problem with drones in last mile delivery
abstract: In recent years, drone routing and scheduling has become a highly active area of research. This research introduces a new routing model that considers a synchronized truck drone operation by allowing multiple drones to fly from a truck, serve one or multiple customers, and return to the same truck for a battery swap and package retrieval. The model addresses two levels (echelons) of delivery: primary truck routing from the main depot to serve assigned customers and secondary drone routing from the truck, which behaves like a moveable intermediate depot to serve other sets of customers. The model takes into account both trucks' and drones' capacities with the objective of finding optimal routes of both trucks and drones that minimizes the total arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by formulated mixed integer programming (MIP) for the small size problem, and two efficient heuristic algorithms are designed to solve the large size problems: Drone Truck Route Construction (DTRC) and Large Neighborhood Search (LNS) Numeric results from the experiment compare the performance of both heuristics against the MIP method in small/medium size instances from the literature. A sensitivity analysis is conducted to show the delivery time improvement of the proposed model over the previous truck drone routing models.

title: On Time Last Mile Delivery: Order Assignment with Travel Time Predictors
abstract: We study how delivery data can be applied to improve the on time performance of last mile delivery services. Motivated by the delivery operations and data of a food delivery service provider, we di.

title: Last mile delivery: Optimal locker location under multinomial logit choice model
abstract: One innovative solution to the last mile delivery problem is the self service locker system. Motivated by a real case in Singapore, we consider a POP Locker Alliance who operates a set of POP stations and wishes to improve the last mile delivery by opening new locker facilities. We propose a quantitative approach to determine the optimal locker location with the objective to maximize the overall service provided by the alliance. Customer's choices regarding the use of facilities are explicitly considered. They are predicted by a multinomial logit model. We then formulate the location problem as a multi ratio linear fractional 0 1 program and provide two solution approaches. The first one is to reformulate the original problem as a mixed integer linear program, which is further strengthened using conditional McCormick inequalities. This approach is an exact method, developed for small scale problems. For large scale problems, we propose a Suggest and Improve framework with two embedded algorithms. Numerical studies indicated that our framework is an efficient approach that yields high quality solutions. Finally, we conducted a case study. The results highlighted the importance of considering the customers' choices. Under different parameter values of the multinomial logit model, the decisions could be completely different. Therefore, the parameter value should be carefully estimated in advance.

title: Effectiveness of carbon pricing policies for promoting urban freight electrification: analysis of last mile delivery in Madrid
abstract: AbstractThis research analyzes the effect of carbon pricing policies in transport electrification. It combines a heuristic algorithm to solve the Green Vehicle Routing Problem with Multiple Technologies and Partial Recharges with an economic Total Cost of Ownership model. The paper compares the performance of battery electric (BEV) and internal combustion vehicles (ICEV) for last mile delivery, using real data of Madrid (Spain) The results show that carbon pricing is scarcely effective when daily mileage is low (precisely when BEVs require incentives) and its effectivity increases as mileage increases (precisely when it is not so necessary to incentivize BEVs) Hence, carbon pricing is not an effective tool for promoting electric vehicles in the short term, and as a result, any political decision to fix CO2 prices must be adopted with a long term view in mind. Specifically for the case of Spain, this research shows that current aids to BEVs are insufficient, with the exception of some regions like Madrid, which complement national subsidies with regional ones.

title: Optimization and analysis of a robot assisted last mile delivery system
abstract: The last mile of freight distribution is a critical part of the supply chain because of its significant costs and customers' increasing expectations from e commerce and same day delivery services. Automated technologies in freight transportation represent an opportunity to develop more efficient systems characterized by the integration of different and complementary modes. In this study, we focus on the possibility of implementing an integrated truck robot system for the last mile delivery. This typology of problem shares similarities with truck drone problems, although robots are characterized by much slower speeds and can perform several consecutive deliveries. Based on these particular features, a heuristic that efficiently identifies solutions based on initial truck tours and corresponding joint robot operations is presented. This solution approach leverages a special version of the "Weighted Interval Scheduling Problem," which allows for a very efficient Dynamic Programming solution. The developed solution approach is adopted to analyze the influence on efficiency of different features concerning the robot's design and operation, and the surrounding environment. The results show that robot assisted last mile delivery systems are quite efficient if robots are employed in heavily congested areas and appropriately retrofitted to accommodate several compartments in the robot's storage.

title: Collaboration Model for Service Clustering in Last Mile Delivery
abstract: As e commerce is rapidly expanding, efficient and competitive product delivery system to the final customer is highly required. Recently, the emergence of a smart platform is leading the transformation of distribution, performance, and quality in express delivery services, especially in the last mile delivery. The business to consumer (B2C) through smart platforms such as Amazon in America and Coupang in Korea utilizes the differentiated delivery rates to increase the market share. In contrast, the small and medium sized express delivery companies with low market share are trying hard to expand their market share. In order to fulfill all customer needs, collaboration is needed. This study aims to construct a collaboration model to maximize the net profit by considering the market density of each company. A Baduk board game is used to derive the last mile delivery time function of market density. All companies in collaboration have to specialize the delivery items into certain service clustering types, which consist of regular, big sized/weighted, and cold items. The multi objective programming model is developed based on max sum and max min criteria. The Shapley value and nucleolus approaches are applied to find the profit allocation. Finally, the applicability of the proposed collaboration model is shown through a numerical example.