Delivery Trucks Research Articles

Cooperative Truck–Drone Delivery Path Optimization under Urban Traffic Restriction

In the traditional express delivery sector, trucks are the most available and efficient transportation mode in urban areas. However, due to the pressures of traffic congestion and air pollution problems, many cities have implemented strict measures to restrict trucks’ access to many zones during specified time periods, which has caused significant effects on the business of the industry. Due to their advantages, which include high speed, flexibility, and environmental friendliness, drones have great potential for being combined with trucks for efficient delivery in restricted traffic zones. In this paper, we propose a cooperative truck and drone delivery path optimization problem, in which a truck carrying cargo travels along the outer boundary of the restricted traffic zone to send and receive a drone, and the drone is responsible for delivering the cargo to customers. The objective of the problem is to minimize the completion time of all delivery tasks. To efficiently solve this problem, we propose a hybrid metaheuristic optimization algorithm to cooperatively optimize the outer path of the truck and the inner path of the drone. We conduct experiments on a set of test instances; the results demonstrate that the proposed algorithm exhibits a competitive performance compared to other selected popular optimization algorithms.

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. In this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

Hybrid transportation system using trucks and drones

In recent years, the use of drones has gained significant attention as a potential solution for delivering goods and services in a variety of industries. Drones offer advantages over traditional delivery methods, including faster delivery times, increased efficiency, and reduced costs. However, their adoption has been limited by challenges related to their operation and regulation, as well as by the complexity of integrating them into existing transportation networks. To address some of these challenges, we present a novel approach to dispatching a hybrid truck and drone delivery system for serving a set of clients. Our proposal uses OR-Tools, an open-source optimization toolkit, for providing an initial truck tour, and a greedy heuristic method for introducing drone service into this route. We consider that at the starting point, the drone is on the truck. During the tour, the hybrid tandem of vehicles ensures that all the nodes of the initial tour are visited, either by the truck, or by the drone. When both vehicles arrive at final node, the time needed is computed. To evaluate the effectiveness of our proposal, we tested it on an instance of the Traveling Salesman Problem in Romania and compared our results with those based on the Concorde solver, a widely used exact algorithm for solving this problem. Our results demonstrate that our solution is stable, and performs well, indicating that our approach can be a useful tool for efficiently programming the service of hybrid delivery systems.

Retrofitting hollow fibre carbon capture systems to decarbonise surface transport

On-board carbon capture and storage (CCS) is being recognised as an essential transitional solution for transport decarbonisation. However, due to inherent space constraints, retrofitting current CCS technology to vehicles is challenging. Herein, the feasibility of a novel hollow fibre carbon capture system impregnated with a carbon xerogel for on-board CCS was assessed. The working capacity of four carbon xerogels with varying surface area/surface groups, over ten temperature swing adsorption (TSA) cycles, was studied in a packed bed adsorption unit (PBAU). The highest-performing carbon xerogel (i.e. CX) was deposited inside a hollow fibre support to form the hollow fibre adsorption unit (HFAU). Under typical vehicle exhaust gas compositions (i.e. 14 vol% CO2 + 3 vol% H2O balanced in air) and at mild operating conditions (i.e. adsorption at 25 °C, 1 atm; desorption at 125 °C, 1 atm), the HFAU exhibited a 1.3 times greater working capacity than the equivalent PBAU (i.e. 4.7 mmol g−1 and 3.5 mmol g−1, respectively), due to minimized mass transfer limitations. In addition, the results obtained were used in a study that compared hollow fibre carbon capture systems and packed bed carbon capture systems designed to be retrofitted to three transport scenarios: (1) a light-duty vehicle (car/van); (2) a large regional delivery truck; and, (3) a ferry. For scenarios 2 and 3, the findings of the study showcased the potential of retrofitting a hollow fibre carbon capture system to these large surface transport modes. However, for scenario 1, despite obtaining a hollow fibre carbon capture system that was 3.5 times smaller, 5.7 times lighter and 6.6 times cheaper than the packed bed carbon capture system, it cannot be feasibly retrofitted without significant improvements in weight. Furthermore, a short-hand simplified heat exchange approach was presented to obtain the TSA operation temperatures of the carbon capture systems.

Impact of Goods Distribution in the Last Mile: An Investigation in the City of São Paulo

The number of goods deliveries made in the city of São Paulo has been rising every year. This growth is caused by new consumption habits, as people increasingly opt for online shopping, having goods delivered to their homes or neighborhood markets. However, these deliveries have become increasingly complex because of several issues found in the last-mile distribution of goods. This research sheds light on the interactions among issues related to truck circulation restrictions, receiver characteristics, accessibility and delivery planning, urban road infrastructure, driver behavior, and safety and risk problems that hamper last-mile delivery distribution. Data were collected (via a questionnaire) from drivers who work in the urban distribution of non-durable consumer goods in the city of São Paulo. From the data, structural equation modeling is proposed to evaluate these interactions. Empirical results demonstrate that “lack of accessibility and delivery planning” and “poor urban road infrastructure” are the elements that have the most impact on the “safety and risk problems in urban freight distribution” from the driver’s point of view. These results also contribute to mapping the main problems encountered in the last-mile delivery distribution in the city of São Paulo.

Design and Assessment of an Urban Circular Combined Truck–Drone Delivery System Using Continuum Approximation Models and Integer Programming

The analysis of tandem truck–drone delivery systems has recently attracted the attention of the research community, mainly focused on extending classical operational research problems such as the multiple traveling salesperson or the vehicle-routing problem. In this paper, we explore the design of an urban massive combined delivery system using a continuum approximation (CA) method for a circular city characterized by a certain density of customers. Starting from a set of parameters defining the main characteristics of trucks and drones, a sectorization of the delivery area is first determined. Then, for a given truck capacity, the optimal number of trucks is obtained considering different scenarios using three integer programming models. We propose several performance indicators to compare the tandem approach with the alternative solely truck delivery system.

Drone location and vehicle fleet planning with trucks and aerial drones

In the context of parcel delivery, aerial drones have great potential that particularly applies in rural areas. In these areas drones mostly operate faster than trucks. As drones are limited in their payload, a combination of trucks and drones can be beneficial in reducing last-mile delivery costs. There are two different delivery methods that combine truck and drone delivery: trucks and drones collaborating with each other with drones launched from trucks, and trucks and drones serving customers independently of each other with drones launched from microdepots or the central distribution center.We develop a tactical planning model that decides on the cost-optimal vehicle fleet and the location of dedicated drone stations of a logistics service provider that minimizes total costs. The problem setting is modeled as a mixed-integer linear program that allows the assessment of benefits of different transport concepts as well as the impact of mixing different delivery modes. To solve larger instances we develop a specialized adaptive large neighborhood search.We present a numerical study for parcel delivery in a rural area where customers live in scattered settlements, e.g., villages or hamlets. The case study shows that it is best to launch drones both from trucks and dedicated drone stations in 58% of all scenarios considered. This fleet mix leads to average cost savings of 33.3% compared to an only truck scenario and 14.1% if trucks and drones launched from trucks are considered for delivery. Moreover, we find 17 new best-known solutions for benchmark instances from the literature.

Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications, such as e-commerce parcel delivery truck routing, however, mid-route recharging may not be necessary because of constraints on vehicle capacities and the maximum allowed time for delivery. In this study, we develop a mixed-integer optimization model that exactly solves such a time-constrained capacitated vehicle routing problem, especially of interest for e-commerce parcel delivery vehicles. We compare our solution method with an existing metaheuristic and carry out exhaustive case studies considering four U.S. cities—Austin, TX; Bloomington, IL; Chicago, IL; and Detroit, MI—and two vehicle types: conventional vehicles and battery electric vehicles (BEVs). In these studies we examine the impact of vehicle capacity, maximum allowed travel time, service time (dwelling time to physically deliver the parcel), and BEV range on system-level performance metrics, including vehicle miles traveled (VMT). We find that the service time followed by the vehicle capacity plays a key role in the performance of our approach. We assume an 80-mi BEV range as a baseline without mid-route recharging. Our results show that the BEV range has a minimal impact on performance metrics because the VMT per vehicle averages around 72 mi. In a case study for shared-economy parcel deliveries, we observe that VMT could be reduced by 38.8% in Austin if service providers were to operate their distribution centers jointly.

A novel multi-objective optimization model for the vehicle routing problem with drone delivery and dynamic flight endurance

With growing environmental concerns and tough carbon–neutral objectives, logistics providers have to consider not only economic benefits but also environmental impact in the delivery process. This study proposes a novel multi-objective optimization model for the vehicle routing problem with drone delivery. The proposed model involves improving delivery efficiency and reducing environmental impact by extending the conventional ground vehicle (i.e. truck) delivery model with the implementation of drone delivery as well as the optimization of the total energy consumption of trucks. Drones need to collaborate with trucks to serve customers because of their limited flight endurance. Moreover, the fact that flight endurance is dynamic and influenced by the loading rate of drones is also considered to satisfy practical application scenarios. An extended non-dominated sorting genetic algorithm is presented to solve the proposed model. A new encoding and decoding method is incorporated to represent multiple feasible routes of drones and trucks, several crossover and mutation operators are integrated to accelerate the algorithmic convergence, and a multi-dimensional local search strategy is employed to enhance the diversity of population. Finally, the experimental results demonstrate that the presented algorithm is effective in obtaining high-quality non-dominated solutions by comparing it with three other baseline multi-objective algorithms.

Machine learning approach for truck-drones based last-mile delivery in the era of industry 4.0

Under the vision of industry 4.0, the integration of drones in last-mile delivery can transform traditional delivery practices and provide competitive advantages. However, the combinatorial nature of the routing problem and the technical limitations of the drones present a real challenge for adopting a stand-alone drone delivery as an alternative to truck delivery. This study introduces the Parking Location and Traveling Salesman Problem with Homogeneous Drones (PLTSPHD). This problem considers a scenario in which a single truck carries identical drones along with parcels from the depot to preassigned launching/parking sites, from where the drones complete the last-mile deliveries. In contrast to previous studies that tackle truck-drone delivery using conventional optimization approaches, this paper proposes a two-phase machine learning (ML) approach for the PLTSPHD, which minimizes the total operational cost of the last-mile problem. The proposed ML approach for PLTSPHD consists of clustering and routing phases. In the first phase, a constrained k-means clustering algorithm is proposed to cluster delivery locations based on the maximum flight range and number of available drones per truck. A deep reinforcement learning (DRL) model is then developed in the second stage to find an optimal route among all constrained clusters. Experimental results show that solving the presented truck-drone problem using the ML framework can significantly reduce the operational cost compared to standard truck delivery. The constrained clustering reduces the complexity of the routing problem while adhering to the constraints. In addition, the trained DRL model outperforms the state-of-art Google’s OR-tools solver and other types of well-known heuristics in terms of both solution quality and computation time. Moreover, a sensitivity analysis of different key parameters is conducted to highlight some key trade-offs in using multiple drones and their dependence on operating costs and problem sizes.

Battery Selection and Optimal Energy Management for a Range-Extended Electric Delivery Truck

<div class="section abstract"><div class="htmlview paragraph">Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle. An optimal control problem can be formulated and solved off-line, to evaluate the most energy efficient utilization of fuel and battery energy for the specific vehicle design. These off-line solutions can be used as benchmarks to compare different architectures and design options, and rules can be derived from them to design on-line implementable heuristic control strategies. In this work a plug-in series architecture with a range-extender engine is considered for a Class 6 beverage delivery truck and the optimal control problem is solved using dynamic programming. To study the strong coupling that exists between components sizing and energy management a simulation design space exploration is performed with four different cell chemistries and three different battery pack sizes. A drive cycle with variable payload, which is representative of delivery operations, is developed and used as a test case. The results show how the selection of the cell chemistry and the constraints on the battery weight can affect the battery performance and the impact on the optimal energy management strategy.</div></div>

Idle-Free Campaign Survey Results and Idling Reductions in an Elementary School

Air pollution near schools is particularly problematic. Pollution emissions from vehicle idling at or around schools may have significant effects on children’s health including increased rates of asthma and childhood leukemia. Outdoor pollution emissions from idling vehicles can also infiltrate into the schools resulting in health hazards both in school drop-off zones as well as inside nearby buildings. An Idle-Free Campaign was enacted at an elementary school to reduce idling among parents dropping off and picking up students. The campaign involved a focus group, surveys, informational events and materials, and vehicle counting efforts before and after the campaign. The surveys found that regardless of gender or level of education, parents were very concerned about air pollution concerns associated with idling and were willing to take steps to reduce their children’s exposure. Furthermore, the vehicle counting efforts showed a 17% reduction in idling vehicles and a 37% reduction in idling time following the anti-idling campaign. These findings show that a multi-pronged approach involving parents, teachers, staff, bus drivers, and delivery truck drivers, may be an effective tool to reduce idling at schools thus reducing children’s exposure.

Visual Servoing Approach to Autonomous UAV Landing on a Moving Vehicle.

Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided.

Technical Feasibility of Heavy-Duty Battery-Electric Trucks for Urban and Regional Delivery in Germany—A Real-World Case Study

Cutting greenhouse gas emissions to comply with the Paris Agreement is challenging for road freight. While heavy-duty battery-electric trucks (BET) promise tremendous and immediate reduction potential, literature increasingly confirms technical feasibility in general, and several manufacturers launched BET models. However, their real-world application is still being questioned by fleet owners due to the limited range or payload penalties. Thus, our case study aims to assess the technical feasibility of urban and regional delivery in Germany based on real-world and per-vehicle operational data that feed into an energy simulation with Monte-Carlo modeling. Our results demonstrate the importance of vehicle-specific examination for the right battery capacity that ideally matches the vehicle’s operating profile. We find that full electrification may be most accessible for 18-t and 26-t rigid solo trucks, soon followed by tractor-trailers, while truck-trailers turn out as most challenging. With up to 600 kWh battery capacity available in all truck classes, we find nearly 40% of all transport performance and 60% of all diesel trucks may be replaced with BET—while already 400 kWh is sufficient for half of all trucks. Additional measures such as intermediate charging and adjusted and more flexible truck-tour allocation may significantly accelerate electrification.

Case Study of Collaborative Modeling in an Indigenous Community

To support Indigenous communities in preparing for uncertainties such as climate change impacts and unexpected threats to health, there are calls by researchers and community members for decision support tools that meaningfully and sensitively bring together Indigenous contextualized factors such as social dynamics, local- and culture-specific knowledge, and data with academic tools and practices including predictive modeling. This project used a community engaged approach to co-create an agent-based model geographically bounded to a reserve community to examine three community-requested simulations. Community members and researchers co-designed, built, and verified the model simulations: a contaminated water delivery truck; a Pow Wow where a waterborne infectious disease spreads; and a flood which restricts typical movement around the reserve for daily tasks and health care. The simulations’ findings, displayed as both conventional and narrative outputs, revealed management areas where community adaptation and mitigation are needed, including enhancing health service provision in times of disease outbreaks or large community events, and creating back-up plans for overcoming flood impacts to ensure services are accessible for vulnerable members of the community. Recommendations for communities, researchers, and modelers are discussed.

Driver injuries in heavy vs. light and medium truck local crashes, 2010–2019

Objective: Multiple heavy truck driver injury studies exist, but there is a paucity of research on light and medium truck driver injuries. The objective of this study was to use first report of injury (FROI) data to: (a) compare demographic and injury characteristics; (b) assess workers’ compensation (WC) claim disposition and lost work time status; and (c) describe injury scenarios by vehicle type for heavy truck and light/medium truck driver local crashes. Method: Kentucky Department of Workers’ Claims FROI quantitative and free text data were analyzed for years 2010–2019. Of 800 total FROIs, 451 involved heavy trucks and 349 involved light or medium trucks. Results: There was a higher light/medium truck driver crash FROI rate compared to the heavy truck driver crash FROI rate. There was a higher proportion of younger light/medium truck driver crash FROIs compared to younger heavy truck driver crash FROIs. The retail trade industry made up the largest percentage of light/medium truck local crash FROIs (47%); the transportation and warehousing industry was most frequently cited in heavy truck FROIs (46%). The heavy truck types most frequently identified in FROIs were semi-trucks (13%) and dump trucks (11%). The most common light/medium truck type identified was delivery trucks (30%). Most commonly, heavy truck crash FROIs involved rollovers, driving off/overcorrecting on narrow roadways, and driving downhill/unable to downshift. Light/medium truck crash FROIs most frequently involved being rear-ended, running red lights, and turning in front of other vehicles. Conclusions: The utilization of WC FROI data highlighted top injury scenarios and specific vehicle types for targeting driver safety training among truck drivers, particularly light/medium truck drivers. Road safety policies regarding driver training, crash reviews, and in-vehicle monitoring systems are needed for truck drivers with previous crash injuries, especially for light and medium truck drivers. Practical applications: Enhanced safety training on speeding on narrow roadways, on nearing intersections, and on downshifting on hills is needed for semi-truck, dump truck, and coal truck drivers with previous crash injuries. Rear-end crash prevention training (e.g., gradual stopping and checking mirrors) is needed for drivers of furniture, automotive parts and accessories, and groceries and soft drink delivery trucks with previous crash injuries.

Approximation schemes for the joint inventory selection and online resource allocation problem

In this paper, we introduce and study the joint inventory selection and online resource allocation problem, which is characterized by two sequential sets of decisions that are irrevocably linked. First, a decision maker (DM) must select starting inventory levels for a set of available resources. Subsequently, the DM must match arriving customers to available resources in an online fashion so as to maximize expected reward. We first study the problem in its most general form, before focusing on a specific version that arises at Anheuser Busch InBev (ABI). This particular application of our general setting is referred to as the ABI Trailer Problem, and it considers how ABI ships its beer to vendors via third‐party delivery trucks. In this problem, ABI must select the weights of preloaded trailers of beer, which are then matched in an online fashion to the arriving third‐party delivery trucks. For the general setting, we develop simple and easy‐to‐implement approaches that come with robust worst‐case performance guarantees. For the ABI setting, we reveal a simplifying structural property related to the optimal matching policy, which gives rise to a natural adaptation of our original approach. We test the efficacy of these policies through extensive numerical experiments, where we find that our approaches are either near‐optimal or improve upon state‐of‐the‐art benchmarks. In particular, using a data set from ABI, we are able to generate instances of the ABI Trailer Problem, on which our algorithm has the potential to yield revenue improvements in the range of millions of dollars per year.

Hybrid Multi-Objective Optimization Approach With Pareto Local Search for Collaborative Truck-Drone Routing Problems Considering Flexible Time Windows

The collaboration of drones and trucks for last-mile delivery has attracted much attention. In this paper, we address a collaborative routing problem of the truck-drone system, in which a truck collaborates with multiple drones to perform parcel deliveries and each customer can be served earlier and later than the required time with a given tolerance. To meet the practical demands of logistics companies, we build a multi-objective optimization model that minimizes total distribution cost and maximizes overall customer satisfaction simultaneously. We propose a hybrid multi-objective genetic optimization approach incorporated with a Pareto local search algorithm to solve the problem. Particularly, we develop a greedy-based heuristic method to create initial solutions and introduce a problem-specific solution representation, genetic operations, as well as six heuristic neighborhood strategies for the hybrid algorithm. Besides, an adaptive strategy is adopted to further balance the convergence and the diversity of the hybrid algorithm. The performance of the proposed algorithm is evaluated by using a set of benchmark instances. The experimental results show that the proposed algorithm outperforms three competitors. Furthermore, we investigate the sensitivity of the proposed model and hybrid algorithm based on a real-world case in Changsha city, China.

An investigation of contributing factors for warehouse location and the relationship between local attributes and explanatory variables of Warehouse Freight Trip Generation Model

The location of warehouses is strategic for urban goods distribution, and the decision factors are subjective. This paper aims to analyse the contributing factors to warehouse location and describe the association between local attributes and the explanatory variables of a Warehouse Freight Trip Generation Model (WFTGM). We developed this study using data from 42 warehouses located in Belo Horizonte (Brazil). The results indicated that the accessibility to/from warehouses and the land cost are the most critical factors contributing to warehouse location decision. Additionally, the accessibility of trucks for delivery has a strong positive correlation with the accessibility of freight vehicles for loading. The land cost negatively correlates with all factors except the employee's accessibility, indicating an antagonistic relationship with other variables. Considering the Warehouse Freight Trip Generation Model, the warehouse-size related variables provided the best-fit models. Additionally, the warehouse size is not independent of the neighbourhoods' population and distance to the main ring roads traversing this city. The results support the development of public policies related to new warehouses' licensing process and offer guidance on warehouses' location in urban areas.

The Two Echelon Open Vehicle Routing Problem: Optimization of Crowdshipping Based Parcel Delivery

Crowdshipping is a revolutionary concept of the sharing economy. In this study, two carriers are used to perform the following expedition: the truck starts from the depot to complete part of the deliveries, shares part of the load with the crowdshipper at the relay point, and the private driver selected as the crowdshipper continues from that point onward. This study proposes the two-echelon open vehicle routing problem with crowdshipping (2EOVRP-CS) and formulates a mathematical model to determine the crowdshipper, parcel relay location, truck route, and crowdsource route. A tangible nested genetic algorithm (NGA) is proposed, and its efficiency is demonstrated by comparison with CPLEX and genetic algorithm (GA). A real case study is investigated in Xi'an city to test the applicability of the proposed model. The results show that using crowdshipping instead of truck delivery alone can save approximately 14% of the total cost and 26% of truck vehicle miles traveled (VMT). Moreover, several sensitivity analyses are performed. The results show that crowdshipping is sensitive to the detour limit and the time value of carriers. For the detour limit, after the acceptable detour distance increases by 8%, the total cost can be reduced by up to 5.94%.