Delivery Trucks Research Articles

Hardware-in-the-Loop Implementation of an Optimized Energy Management Strategy for Range-Extended Electric Trucks

The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores a Range-Extended Electric Vehicle (REEV) for medium-duty Class 6 pick-up and delivery trucks. This hybrid architecture combines an electric powertrain with an internal combustion engine range-extender. Maximizing the efficiency of REEVs requires an Energy Management Strategy (EMS) to optimally split the power between the two power sources. In this work, a hierarchical EMS is developed through model-based design and validated via Hardware-In-The-Loop (HIL) simulations. The proposed EMS demonstrated a 7% reduction in fuel consumption compared to a baseline control strategy, while maintaining emissions and engine start frequency comparable to a benchmark globally optimal EMS obtained with dynamic programming. Furthermore, HIL results confirmed the strategy’s real-time implementation feasibility, highlighting the practical viability of the controller. This research underscores the potential of REEVs in significantly reducing emissions and fuel consumption, as well as providing a sustainable alternative for medium-duty truck applications.

Ergonomic Improvement at a Distribution Center Using a Voice-Directed Feedback System to Manage Loading Trucks

This field study reports on a wireless, wearable, automated voice-directed system where truck loaders at a distribution center wear a headset to listen to instructions and speak into a microphone to load pallets of product into delivery trucks accurately. The system was designed to provide immediate and corrective feedback to load pallets in reverse-stop sequence for all multi-stop routes. The voice-directed system intervention was instrumental in reducing the number of pallets loaded out of sequence to 0.015%, based on approximately 175 weekly routes, amounting to a decrease of an out-of-sequence percentage error by 99%.

Dynamic collaborative truck-drone delivery with en-route synchronization and random requests

Coordinated truck and drone delivery is gaining popularity in logistics as it can greatly reduce operation costs. However, existing studies on related operations management problems typically ignore the following important features: (i) the random appearance of requests, which require operators to dynamically respond to the requests; and (ii) the decisions of optimal launch and retrieval locations for trucks and drones instead of fixed to customer locations, which can significantly impact the overall time costs. To tackle these challenges, this study investigates the dynamic collaborative truck-drone routing problem with randomly arriving requests and synchronization on routes. We model the problem as a Markov Decision Process (MDP) and solve the MDP via a reinforcement learning (RL) approach. The proposed RL approach determines: (i) whether each request should be serviced upon arrival, (ii) which truck or drone should be assigned for the request, and (iii) the optimal en-route take-off and landing positions for paired trucks and drones. We further employ a framework of decentralized learning and centralized dispatching in RL to increase performance. Numerical experiments are conducted to assess the proposed solution approach on instances generated based on both the Solomon dataset and real-world operational data of a logistics operator in Singapore over several benchmark algorithms under various battery endurance levels of drones and distinct transportation scenarios including node-based dynamic collaborative truck-drone routing problem, dynamic non-collaborative truck and drone routing problem, and dynamic vehicle routing problem. The results show that our RL solution outperforms the benchmark algorithm in total profit by an average of 28.03 %, and our en-route takeoff and landing scenario outperforms the benchmark scenarios in total profit by an average of 8.43 % in multi-day instances. Additionally, compared to the traditional node-based landing scenario, employing our en-route takeoff and landing strategy can save 0.9 h/(drone*day) of waiting time on average.

Prediction of delivery truck arrivals at container terminals: an ensemble deep learning model

Prediction of delivery truck arrivals at container terminals: an ensemble deep learning model

Medium-Duty Delivery Truck Integrated Bidirectional Wireless Power Transfer System With Grid and Stationary Energy Storage System Connectivity

Medium-Duty Delivery Truck Integrated Bidirectional Wireless Power Transfer System With Grid and Stationary Energy Storage System Connectivity

A Tapped Winding Interior Permanent Magnet Synchronous Machine for Medium-Duty Delivery Trucks

A Tapped Winding Interior Permanent Magnet Synchronous Machine for Medium-Duty Delivery Trucks

Sustainable truck platooning operations in maritime shipping: A data-driven approach

In liner shipping, stakeholders are increasingly committed to adopting autonomous and environmentally friendly transportation solutions, especially for truck operations managing container transfers. Beyond reducing labor costs, truck platooning technology—which enables autonomous trucks to operate in close formations, thereby significantly decreasing fuel consumption—promises to revolutionize fleets involved in maritime container transport. However, the potential of these benefits hinges on the process of developing and implementing optimization plans that address the specific challenges of container logistics, particularly in integrating truck platooning plans. In response to this need, this study extends the traditional instant-dispatch strategy by proposing a novel, data-driven dispatch strategy. We develop algorithms for both models and conduct extensive experiments focusing on truck operations for sea freight containers. Our findings reveal significant advantages of the data-driven dispatch strategy: it substantially reduces the total costs and fuel consumption associated with truck deliveries compared to the instant-dispatch strategy.

Combining vehicle routing and bin packing problem for vehicle routing planning: A case study of a chemical factory

In the logistics distribution process, effective transportation management is required to ensure quality and timely delivery. In addition, transportation must be more economical and faster. The case study company is a chemical plant, an industrial company that produces products for hygiene and cleaning. The delivery manager plans the transportation routes based only on her experience because no decision-making aids are available. In addition, the manager considers the shipping order from first to last when arranging the products on the transportation vehicles. The goods for the final delivery location are placed at the back and bottom of the delivery truck. This cannot guarantee that the transportation routes set by the manager are suitable. Therefore, this study aims to plan delivery routes, arrange items in trucks, and reduce transportation costs for a case study of a chemical factory by using vehicle route problems and container filling problems. Then, the program is developed in Python using the Saving Algorithm, the 3D First Fit Decreasing Algorithm, and the 3D Best Fit Algorithm. The results show that the program method can reduce the use of transportation vehicles from 19 to 14, which is 26.32%; reduce the total distance from 5,499.8 kilometers to 3,406.92 kilometers, which is 38.05%; reduce the wages of transportation vehicles from 36,666 to 24,993 baht, which is 31.84%; can increase the average total weight per vehicle from 961.22 to 1,319.71 kilograms, which is 37.30%; and increase the average space utilization per vehicle from 37.93% to 51.47%, which is 13.54%. This method can reduce costs and increase efficiency for the company. Finally, the developed program also supports inexperienced operations managers in making decisions when planning transportation routes and loading goods, Thereby saving working time.

Electrified fleet and infrastructure aware energy efficient routing

This paper presents an optimization framework to improve the energy efficiency and cost-effectiveness of fleets of commercial trucks operating pickups and deliveries in urban areas. As the electrification of transportation is moving from passenger cars to medium- and heavy-duty vehicles, the proposed analysis considers a fleet of pickup and delivery trucks that includes conventional internal combustion engine vehicles (ICEV), as well as battery electric vehicles (BEV), and plug-in hybrid electric vehicles (PHEV). Given a set of pickups and deliveries, and a fleet including different types of vehicles, the goal is twofold: assign the best vehicle to each task, and solve the vehicle routing problem, i.e., find the optimal route to navigate the vehicle from the origin to the destination(s). To estimate the energy consumption of the different vehicles, vehicle dynamics are considered, together with actual charging infrastructure and road data, including speed limits, road grade, and stop signs. Moreover, the total cost of ownership (TCO) is evaluated to estimate the cost-effectiveness of different fleet compositions and operations. To solve this problem, a hybrid simulated annealing (HSA) heuristic algorithm is proposed. The algorithm is validated against a benchmark exact solver based on mixed integer linear programming (MILP). The proposed methodology achieves optimal results with a 1.2% optimality gap compared to the benchmark, surpassing MILP in computational efficiency. The research findings highlight how fleet composition and operational strategies can vary significantly based on whether the focus is on energy efficiency, total cost of ownership, or a combination of the two, also depending on the number of years of operation. Simulation case studies in the Columbus, OH area demonstrate that integrating fleet and recharging infrastructure information alongside energy savings in vehicle routing problem solutions can achieve 20% to 50% savings in fleet operation costs compared to solely optimizing for minimum energy consumption.

Optimal Operation of an Industrial Microgrid within a Renewable Energy Community: A Case Study of a Greentech Company

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized.

Confined within a sugarcane monoculture: A participatory assessment of water pollution and potential health risks in the community of El Tiple, Colombia

Communities neighboring monoculture plantations are vulnerable to different forms of pollution associated with agro-industrial operations. Herein, we examine the case of El Tiple, a rural Afro descendant community embedded within one of the largest sugarcane plantations in the Americas. We implemented a participatory approach to assess water pollution, exposure via water ingestion, and non-carcinogenic health risks associated with the use of local water sources available to the community. We conducted household surveys to unveil demographic characteristics and family dynamics linked to water consumption. Additionally, we measured water quality parameters and assessed the concentration glyphosate, its major metabolite (aminomethylphosphonic acid) and metals and metalloids. Drinking water El Tiple households is sourced from three primary sources: the local aqueduct system, water delivery trucks, and private deep wells. Tests on water samples from both the local aqueduct and delivery trucks showed no traces of pesticides, metals, or metalloids surpassing regulatory limits set by Colombian or EPA standards. However, we found concentration of contaminants of primary concern, including mercury (up to 0.0052 ppm) and lead (up to 0.0375 ppm) that exceed the permissible regulatory thresholds in water from groundwater wells. Residents of the peripheric subdivisions of El Tiple are four times more reliant on well water extraction than residents of the central area of the town due to lack of access to public drinking water and sanitation infrastructure. Finally, adult women and school-age children have a higher health risk associated with exposure to local pollutants than adult men due to their constant presence in the town. We conclude that expanding the coverage of clean water and sanitation infrastructure to include all households of the community would be the most recommended measure to minimize exposure and risk via ingestion of water pollutants.

A hybrid genetic algorithm with type-aware chromosomes for Traveling Salesman Problems with Drone

There are emerging transportation problems known as the Traveling Salesman Problem with Drone (TSPD) and the Flying Sidekick Traveling Salesman Problem (FSTSP) that involve using a drone in conjunction with a truck for package delivery. This study presents a hybrid genetic algorithm for solving TSPD and FSTSP by incorporating local search and dynamic programming. Similar algorithms exist in the literature. Our algorithm, however, considers more sophisticated chromosomes and less computationally complex dynamic programming to enable broader exploration by the genetic algorithm and efficient exploitation through dynamic programming and local search. The key contribution of this paper is the discovery of how decision-making processes for solving TSPD and FSTSP should be divided among the layers of genetic algorithm, dynamic programming, and local search. In particular, our genetic algorithm generates the truck and the drone sequences separately and encodes them in a type-aware chromosome, wherein each customer is assigned to either the truck or the drone. We apply local search to each chromosome, which is decoded by dynamic programming for fitness evaluation. Our new algorithm is shown to outperform existing algorithms on most benchmark instances in both quality and time. Our algorithms found the new best solutions for 538 TSPD instances out of 920 and 74 FSTSP instances out of 132.

Measurement and Analysis of Last-Mile Parcel Delivery Truck Vibration Levels in Korea

South Korea has one of the largest e-commerce markets in the world. The last-mile delivery segment of e-commerce often causes critical damage to products in protective packages. Despite the rapid growth of the e-commerce market in Korea, the last-mile distribution environment has not yet been thoroughly investigated. The main aim of this study was to provide an understanding of the vibration levels that were measured from various parcel delivery routes within Seoul, Korea, using common types of parcel delivery trucks. Vibration levels of ten delivery trucks were measured and analyzed in terms of power spectral densities (PSDs) and presented as PSD spectra. The last-mile delivery vehicle vibration levels in Korea were found to be consistently lower (in the 1 to 200 Hz frequency range) than those recommended by international standards and lower than the vibration levels of parcel delivery vehicles in the U.S. and Hungary. The results also revealed that the highest intensity peak of the PSD spectrum for Korea was located in the lower frequency range (1.5 to 2 Hz) compared to the ISTA 3A pickup and delivery test profile (3 to 4 Hz) and the test profile recommended for Hungary (13 to 16 Hz). A smoothed composite spectrum was also provided to support Korean packaging engineers in optimizing their packages by simulating proper last-mile truck delivery vibration levels in lab conditions.

Profit-effective component sizing for electric delivery trucks with dual motor coupling powertrain

This study proposes a novel component sizing method for electric delivery trucks (EDTs) employing dual motor coupling powertrain (DMCP) to enhance both the energy efficiency and operating profitability. A control-oriented model for the EDT is first established, encompassing the three-mode DMCP dynamics. Variations in component size and mass have been modeled, with consideration of their effects on the load capacity. To maximize the average profit per kilometer over the truck’s lifespan, four objective functions are defined to accommodate to the diverse types of cargo being transported. We formulate the optimization problem in a bi-level form, and propose a solution method that combines particle swarm optimization (PSO) handling parameter filtering with iterative dynamic programming (IDP) to minimize energy consumption. Three real-world delivery tests show that component sizing leads to an increase in the average profit per kilometer by 2.62%–8.10%. Upon evaluating the impact of powertrain and battery mass/volume on cargo capacity, the battery pricing ceases to impact the sizing of components. However, the electricity price and freight significantly influence the optimal size of components. Moreover, a sensitivity analysis focusing on market price factors underscores the importance of component sizing for maximizing profit, particularly in scenarios where freight costs fluctuate in commercial settings.

Using intermediate points in parcel delivery operations with truck‐based autonomous drones

AbstractAutonomous drones are no longer science fiction but are becoming reality. Prior studies have investigated how an autonomous drone can be used in conjunction with a parcel delivery truck, but they all restricted the drones’ launch/recovery sites to customer nodes visited by a truck. In practice, parcel carriers are considering the use of intermediate points (IPs), the sites found along the arcs connecting customer nodes, as drones' launch/recovery points. This means that the academic literature currently lags the industry practice. This article extends the previous works on truck‐and‐drone last‐mile delivery by investigating the conditions under which the use of IPs is beneficial by using both theoretical and empirical approaches. Our results show that, although using IPs is an effective concept that can save the cost (time) of package deliveries by 2.189% on average, the cost saving realized by using IPs can vary notably across carriers depending on their network characteristics. Specifically, our results suggest that the benefit of using IPs becomes high when a network has the following characteristics: (1) low customer density (a small number of customers served per square mile), and (2) large number of time‐sensitive packages (subject to time window constraints). Based on these findings, we provide normative implications on if, when, where, and to what extent IPs can be beneficial for the truck‐and‐drone joint operations in last‐mile logistics.

A continuous approximation approach to integrated truck and drone delivery systems

Home package delivery by drones as an alternative or complement to traditional delivery by trucks is attracting considerable attention from the commercial sector as well as academia. While drone delivery may offer considerable benefits, the fundamental issues of how best to deploy drones for small package delivery are still not well understood. Unlike most studies in the literature that consider either truck-drone tandems or drone standalone delivery operations, our research provides a strategic analysis for the optimal design of an integrated drone delivery system in which customers in a service region can be served by a combination of truck-drone (TD) delivery, drone-only (DO) delivery, and truck-only (TO) delivery. We formulate and optimize continuous approximation models to identify the optimal delivery service combination that minimizes the total delivery costs including truck and drone operating and stop costs. We evaluate the impact of a wide range of operating characteristics and delivery environments on the optimal delivery system and the benefits of using drones. Our results suggest that an optimal delivery system design often uses a mix of DO and TD, but TD is the dominant delivery service in most scenarios. The magnitude of the cost savings afforded by the use of drones, relative to truck-only delivery, can be large but strongly depends on the values of key parameters.

IDENTIFYING THE CARBON FOOTPRINT OF FORMWORK MATERIAL DELIVERY IN BUILDING CONSTRUCTION PROJECTS

Sustainable construction is a way of implementing sustainable development of the construction industry and infrastructure through building a better quality of life, and minimizing the use of resources and energy in order to actualize The 2030 Agenda for Sustainable Development. The use of construction materials is one source of carbon dioxide emissions and material delivery is one of the stages in the construction material supply chain that has the potential to cause CO2 emissions, one of which is the use of formwork. Global warming is closely related to carbon footprint. The research was done by surveying the building construction project followed by data processing which aims to find out how much carbon emissions are generated by construction projects in the contracting process, delivery, formwork fabrication process, and the use of tower cranes and find out what factors contribute to the carbon footprint and what processes are most significant in producing carbon footprints. The results show that carbon emissions for 28 floors amounted to 84,815.1037 kg.CO2 and by doing 2 scenarios in which scenario 1 maximizes the carrying capacity of each delivery truck during the delivery process and delivery for all the needs of 1 tower is carried out during the GF floor and first floor work, the resulting carbon emission efficiency is 2289.0626 kg. CO2 and in scenario 2 where the delivery of multiplexes for the needs of only 1 floor, which makes delivery carried out for each floor and makes the delivery distance half the actual distance, the resulting carbon emission efficiency is 4628.7909 kg.CO2.

New features for customer classification in the Flying Sidekick Traveling Salesman Problem

The increasing interest in the deployment of truck and drone delivery systems leads to the definition of new and complex vehicle routing problems. In this context, the flying sidekick traveling salesman problem (FS-TSP) is the first truck-and-drone routing problem defined in the literature. Several variants appeared in the last years differing in the operating conditions and the structure of the hybrid truck-and-drone delivery system. Exact and heuristic solution methods have been proposed in the literature to solve these problems effectively. However, the exact solution methods can generally solve only small-size instances due to the complexity of these problems. On the other hand, heuristic solution methods are able to find feasible solutions with an acceptable computational burden but without any guarantee of the quality of the solution. This work aims to investigate the possibility of using data science and machine learning techniques to reduce the complexity of solving an FS-TSP instance. The idea is to determine a good/optimal customer-to-vehicle assignment apriori to reduce the number of decisions involved in the FS-TSP solution. The assignment is determined through the classification of customers based on a subset of features specifically defined for the FS-TSP. This information can be exploited by existing solution approaches for the FS-TSP to improve their performance. An extensive computational campaign on benchmark instances is carried out with a twofold objective. On the one hand, we aim to evaluate the impact of the features on customer classification. On the other hand, we intend to show the relationship between classification and combinatorial optimization results by observing the effect of the customer classification on the FS-TSP solution.

A scenario-based feasibility analysis of autonomous robot deliveries

Autonomous robot deliveries have been considered as one of the ways to augment delivery resources in urban areas. Their high initial cost and strict operation regulations are often seen as the main barriers in this regard. However, a rise in demand of home deliveries and contactless deliveries in the wake of current COVID-19 pandemic has increased interest in autonomous delivery robots. This study presents a scenario-based feasibility analysis of autonomous deliveries using real data of two freight companies in Osaka, Japan. The minimum number of autonomous robots has been estimated in various delivery and cost scenarios and compared with the actual delivery operations. It was found that if current initial costs can be reduced due to mass production, autonomous robots with mothership vehicles can result in similar costs compared with truck deliveries along with the added advantage of low environmental impact. Furthermore, a simple index, counting the number of human-to-human interactions in delivery operations has been used to show the advantages of autonomous robot deliveries in case of pandemics.

Implementation of Genetic Algorithm on Vehicle Routing System

Abstract: This study explores the use of Genetic Algorithms (GAs) to solve the Vehicle Routing Problem (VRP) in logistics. GAs, inspired by evolutionary principles, prove effective in finding efficient routes for a fleet of vehicles serving customers where the goal is to find the most efficient routes for a fleet of vehicles serving customers. Inspired by evolution, GAs prove effective in this task by selecting promising routes, creating new options through crossover and mutation, and evaluating fitness based on objectives and constraints. The study incorporates techniques like reparation, penalties, and adaptive tuning to boost GA performance. Real-world applications in transportation and logistics demonstrate substantial cost savings. The key takeaway is the crucial role of parameter selection, representation design, and fitness function formulation in the success of GAs in solving VRP. The combination of GAs and VRP resolution shows promise in optimizing logistics, improving efficiency, and enhancing service quality. In simpler terms, the study explores a smart way, inspired by nature, to figure out the best routes for delivery trucks, leading to significant cost savings and better service.