Freight Load Balancing and Efficiencies in Alternative Fuel Freight Modes

This research conducted a probabilistic life-cycle assessment (pLCA) into the greenhouse gas (GHG) emissions performance of nine combinations of truck size and powertrain technology for a recent past and a future (largely decarbonised) situation in Australia. This study finds that the relative and absolute life-cycle GHG emissions performance strongly depends on the vehicle class, powertrain and year of assessment. Life-cycle emission factor distributions vary substantially in their magnitude, range and shape. Diesel trucks had lower life-cycle GHG emissions in 2019 than electric trucks (battery, hydrogen fuel cell), mainly due to the high carbon-emission intensity of the Australian electricity grid (mainly coal) and hydrogen production (mainly through steam–methane reforming). The picture is, however, very different for a more decarbonised situation, where battery electric trucks, in particular, provide deep reductions (about 75–85%) in life-cycle GHG emissions. Fuel-cell electric (hydrogen) trucks also provide substantial reductions (about 50–70%), but not as deep as those for battery electric trucks. Moreover, hydrogen trucks exhibit the largest uncertainty in emissions performance, which reflects the uncertainty and general lack of information for this technology. They therefore carry an elevated risk of not achieving the expected emission reductions. Battery electric trucks show the smallest (absolute) uncertainty, which suggests that these trucks are expected to deliver the deepest and most robust emission reductions. Operational emissions (on-road driving and vehicle maintenance combined) dominate life-cycle emissions for all vehicle classes. Vehicle manufacturing and upstream emissions make a relatively small contribution to life-cycle emissions from diesel trucks (<5% each), but these are important aspects for electric trucks (5% to 30%).

Today’s customer-driven economic environment shifts from “supply chain” to “demand chain”. To successfully compete, companies are faced with the challenge of minimizing inventory and reducing warehouse costs whenever possible while keeping retailers’ shelves full. Cross Docking is a strategy whose ability to reduce lead times, control inventory carrying costs and deliver just-in-time service with a minimal investment makes it an attractive option for many companies responding to economic pressures. In a cross docking strategy, warehouses and distribution centers serve as transfer points for inventory, but no inventory is held at these transfer points. One of the keys to keeping a Cross Docking facility synchronized is to design a system and schedule that are obtainable, taking into account travel times and expectations for how much freight will actually fit on a truck; therefore, Vehicle Routing should be considered. This research examines a Vehicle Routing Problem with Cross Docking (VRPCD) considering cross docking operational characteristics such as assignment restrictions, deadlines and door environment. A sequential operational VRPCD model is presented. Its objective is to determine the best route that minimizes operational costs of shipment, and the minimum operational cost within the cross docking facility. A two-phase algorithm using Variable Neighborhood Search (VNS) has been proposed. The experimental results show that the VNS algorithm is capable of finding good solutions in a much shorter computation time when compared with Gurobi Optimizer solutions. Good quality of solutions is obtained in all the experiments developed.

India moves most of its goods by road, and diesel trucks, which entirely dominate freight, impose a significant cost burden across the supply chain. These operational costs do not remain inside a company's accounts but travel upward through the supply chain, ultimately appearing in the price of every packet of chips, every medicine, and every bag of rice that a consumer buys. This paper examines whether replacing diesel trucks with electric trucks could reduce freight costs and, by extension, lower the retail prices that ordinary Indian consumers pay. Through structured analysis of running costs and total ownership expenses over eight years, the study finds that electric trucks reduce transportation costs by 28 to 35 percent across different sectors. Sectors such as e-commerce, FMCG, agriculture, and pharmaceuticals gain the most from these savings. Critically, these freight savings demonstrate a meaningful pass-through effect rather than remaining locked inside business balance sheets with consumers benefiting through lower retail prices. Estimated retail price reductions range from approximately 0.9 percent for branded packaged goods to 4.2 to 6.3 percent for ecommerce deliveries with reductions of up to 8 to 12 percent in rural areas where transport costs constitute a larger share of final prices. The paper also examines environmental benefits, charging infrastructure requirements and relevant government schemes. Practical barriers including high upfront costs, limited financing, range constraints, and a shortage of trained technicians mean adoption currently remains slow. The paper concludes with recommendations for industry, government and fleet operators

Purpose Electric trucks and platooning are promising technologies to reduce greenhouse gas emissions in the freight sector. To maximize the benefits of these two technologies, effective coordination of charging and platooning is essential, especially considering insufficient charging stations (CSs), long charging duration and tight freight delivery window for middle-mile electric trucks. Therefore, this paper aims to jointly optimize the scheduling of charging and platooning of electric trucks over the freight transportation network. Design/methodology/approach This paper proposes a mixed integer linear programming model to minimize the total costs from en-route charging, depot charging, and delivery delay. This also presents scenario analyses to understand the impacts of key features on system costs, including battery capacity, number of charging plugs at CSs, charging speed, availability of alternative paths and platoon energy-saving percentage. To solve the model with a large fleet size, a warm-start-based parameter-tuned solver approach, and hybrid metaheuristics of variable neighborhood search and local branching were implemented and compared based on performance. Findings The proposed model was implemented using the freight network in Florida. In a case study with a small fleet size, platoon scheduling reduced 19% of en-route charging cost and 30% of delivery delay cost compared with the case of only charge scheduling. Electric trucks were charged around three times with an average duration of 35 min per session to facilitate platoon scheduling and minimize the total cost. Originality/value Previous models optimized charging and platoon scheduling for single routes that cannot be generalized for network level and multiple origin-destination pairs; this study addresses network-level optimization.

In recent decades, the rapid development of international economy has led to significant inter-regional trade imbalances. In marine transportation, the voluminous empty containers accumulated at ports in import dominated countries have to be re-circulated back to ports in export dominated countries for re-deployment. This repositioning of empty containers is crucial for shipping companies to provide effective transportation services. At present, empty container repositioning has proved to be highly expensive, contributing to 27% of the overall operation cost of shipping companies. Consequently, major stakeholders including the shipping companies have strived to reduce the repositioning cost. Unfortunately, few major improvements have been made over the years and there are still lingering research issues. The objective of this research, therefore, is to develop efficient and effective methodologies to obtain empty container repositioning plans that can significantly reduce transportation cost while fulfilling customer demands of empty and laden containers.
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\nIn this research, the empty container repositioning problem under two related real marine transportation scenarios has been studied. The first scenario considers multi-vessel transportation for both laden and empty containers with split vessel routing (denoted as ECRSVR). A mathematical model of ECRSVR is developed based on the formulation of the Pickup and Delivery Problem with Time Windows (PDPTW). However, a major research issue arises due to such integrated consideration of split vessel routing and transportation of empty and laden containers. The resulting model will encompass many more new decision variables and constraints. Thus, it cannot be readily solved by existing algorithms. To address this issue, a new Branch-and-Price (B&P) algorithm incorporating B&B and column generation is developed. Specifically, a new dynamic programming algorithm with new label structure and dominance rules is established to solve the pricing problem in B&P, and a new multi-layer branching scheme is developed for implementing the algorithm. The effectiveness and robustness of the proposed methodology are then tested on a set of randomly generated problems.
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\nThe second scenario extends the first by including transshipment activities in the ECRSVR (denoted as ECRSVRTS). Apparently, the inclusion of transshipment activities will significantly increase the size of the solution space of feasible routing and distribution schemes. Also, more constraints have to be included in the model to account for the interdependence between the distribution schemes of separate vessels. To tackle this problem, two mathematical models are developed. The first is a port-based model that explicitly characterizes the routing plans and distribution volumes for each vessel at each time interval as decision variables. The resulting integer program is non-linear and difficult to solve. Therefore, an equivalent event-driven node-based model inspired by the formulation of PDPTW is constructed. In this model, the pickup/delivery tasks and transshipment events are represented as nodes in a generated topology. The B&P scheme is then modified to solve the node-based model and a Hybrid Scatter Tabu Search (HSTS) is developed to solve the pricing problems. Results obtained from the numerical experiments using randomly generated test problems show that the proposed HSTS algorithm outperforms conventional scatter search and tabu search algorithms.

The integration of truck platooning and electrification presents a promising avenue for improving operational efficiency and environmental sustainability in freight transportation. Realizing the energy and cost saving as well as emission reduction benefits requires a holistic design of truck routing, scheduling, and platooning strategies that account for practical operational constraints. This study investigates the integrated planning problem of routing, scheduling, and platooning for a mixed fleet of conventional trucks (CTs) and electric trucks (ETs), referred to as mixed fleet truck platooning (MFTP) problem. The MFTP incorporates charging scheduling and key operational factors, such as platooning leader-follower positioning under the battery constraints of ETs and charging station availability and capacity, and the positional configuration of trucks within a platoon. The objective is to minimize the total operation cost of the MFTP system, including charging cost, fuel cost, travel labor cost, charging labor cost, and platoon formation labor cost, while ensuring timely arrivals across multiple origin–destination (OD) pairs. The proposed MFTP is formulated as a novel mixed-integer linear program (MILP). Extensive numerical experiments on the Illinois highway network are conducted to examine the effectiveness and efficiency of the proposed model. The findings shed light on planning mixed fleets of CTs and ETs with platooning, offering valuable managerial insights for decision-makers.

Routing in ad hoc networks is a challenging task. During the route discovery phase of AODV-like routing schemes, flooding of route request (RREQ) packets creates broadcast storm, which increases the possibility of packet collision. On the other hand, location-aided routing schemes may mitigate the problem by limiting the broadcast region. However, such schemes might fail in many cases where conventional routing schemes, such as AODV, can easily find a route. Besides, it is costly for every node to maintain location information of all nodes in the network. To overcome the above mentioned problems, we propose a routing scheme named Farthest Node Only (FARNO), where only the farthest node in each quadrant is designated to forward an RREQ packet. Simulation results reveal that the proposed scheme can prevent the above mentioned problems and perform well in many aspects.

Life cycle GHG emissions and lifetime costs of medium-duty diesel and battery electric trucks in Toronto, Canada

The use of heavy-duty battery electric trucks for long-haul transportation is challenging because of the required high energy amounts and thus the high capacity of traction batteries. Furthermore a high capacity battery implies high initial costs for the electric vehicle. This study investigates the required battery capacity for battery electric trucks considering the requirements of long-haul transportation in Germany and compares the life cycle costs of battery electric trucks and conventional diesel trucks in different transportation scenarios. The average consumption is simulated for different battery electric truck configurations on the main German highways and transportation scenarios incorporating battery charging during driver rest periods. The results show that in average case the required battery would restrict the payload to only 80% of a usual diesel truck payload that might be acceptable considering the statistical payload use. The life cycle costs in the examined scenarios also considering the charging infrastructure show that battery electric trucks can already perform on the same costs level as diesel trucks in certain scenarios.

Constructing a route connecting a source node to a destination node is one of the central problems in sensor networks and mobile computing. In this paper we consider the application of a data structure called ExtDCEL that can be very effective in implementing several location-based routing algorithms that include face routing, greedy most forward routing, and hybrid routing. The ExtDCEL data structure is very convenient for representing unit disk graphs as well as planar components. Many network properties can be computed locally when the graph is represented in ExtDCEL. In particular, we show how the popular hybrid greedy face routing algorithm can be implemented efficiently by using ExtDCEL.

Author(s): Porter, Mason A.; Spencer, David J.; Hung, Cu Hauw | Abstract: The goal of our project is to automatically generate school bus routes for the Los Angeles Unified School Districtn(LAUSD). We examined four algorithms, including two from the existing literature and two new ones that we developed. A major focus of our work was the construction of “mixed-load routes,” which transport students from multiple schools. Based on our measurements (whose imperfections we discuss), three of the four algorithms perform at least as well as the existing route plan, and one of those three performs better than the existing route plan. We also delivered a user-friendly routing program to LAUSD that uses one of these algorithms, and we have made our software publicly available. Our insights and results are also applicable to other school districts that permit mixed-load routing.

Efficient truck–shovel allocation is essential for optimizing open-pit mining operations, but the integration of heterogeneous diesel and electric fleets introduces complex scheduling challenges, including charging requirements, range limitations, and equipment capacity constraints. This study proposes an integrated allocation framework tailored to heterogeneous fleets, formulating a multi-objective optimization model that minimizes transportation cost and waiting time under realistic constraints. An enhanced multi-objective particle swarm optimization algorithm with adaptive penalty mechanisms is developed, providing superior convergence and computational efficiency compared to traditional methods. A case study demonstrates that heterogeneous fleets achieve a better trade-off, with a balanced fleet configuration reducing transportation cost by 26.1% and waiting time by 19.2% compared to pure diesel and electric fleets, respectively. Sensitivity analyses reveal that fluctuations in fuel and electricity prices reshape the trade-off, while faster charging enhances electric truck competitiveness but increases diesel idle time. These findings offer practical insights for configuring heterogeneous fleets and adapting scheduling strategies in dynamic energy and technology environments, supporting sustainable mining operations.

Electromobility plays a key role on the path toward a sustainable society, where electrification of freight transports can mitigate climate change by decreasing the use of fossil fuels, reducing noise, and improving air quality. For heavy trucks there are several challenges and aspects to consider. Among these are estimating total cost, estimating energy consumption, deciding on charging locations and capacity, fleet mix, and how to make route planning. Many companies are making investment decisions to introduce electric trucks without accurate information or any practical experience on these aspects. One reason is the lack of electric heavy trucks in actual operation and information on their use available. We present and analyze the performance from the first two years of operation of the world’s first fully battery electric timber truck at the forest company SCA operating in Sweden. The analysis is based on quantitative data from the Scania battery electric timber truck with more than 65,000 kms of operation, as well as qualitative data from unstructured interviews with persons involved in developing and operating the truck, both inside and outside SCA. The analysis provides important information and experiences of the transport, energy consumption based on multiple measurement systems and estimations, total cost, and a sensitivity analysis comparing diesel and electric heavy trucks using the most important input including electric and diesel price, purchasing price, government subsidies, C02 emission reduction, and charging downtime. From this, it is clear how electrical trucks can be competitive.

Fault-tolerance has been the focus of attention recently in the research of parallel processing and computer networking. A fault-tolerant routing algorithm should guarantee the delivery of messages in the presence of faulty components. In this paper, we present a comparative performance study of seven prominent fault-tolerant routings in 2-D wormhole-switched tori. The routing algorithms here are coupled with the Software-Based routing scheme which supports dynamic fault-model, enabling the network to remain fully operational at all times. The performance measures studied here are the throughput and average message latency. Results obtained through simulation suggest two classes of presented routing schemes as high performance candidates in most faulty networks.

This paper introduces a new IEEE standard, IEEE 802.15.5,which provides mesh capability for wireless personal area network (WPAN) devices. The standard provides an architectural framework enabling WPAN devices to promote interoperable, stable, and scalable wireless mesh topologies. It is composed of two parts: low-rate WPAN mesh and high-rate WPAN mesh. In this paper, we present only low-rate WPAN mesh because it is designed to support wireless sensor networks. IEEE 802.15.5 low-rate part is a light-weight scalable mesh routing protocol that caters well to the requirements of resource-constrained wireless sensor networks. By binding logical addresses to the network topology, IEEE 802.15.5 obviates the need for route discovery. This eliminates the initial route discovery latency, saves storage space and reduces the communication overhead and energy consumption. A distributed link state scheme is further built atop the block addressing scheme to improve the quality of routes, robustness, and load balancing. The routing scheme scales well with regard to various performance metrics. The standard also provides enhanced functions such as multicast, reliable broadcast, power saving, time synchronization, route tracing and portability. We also present the performance evaluation of major functions performed with a 50-nodes tested deployed over a whole floor (100 × 140 ft2) at CUNY Engineering building. The results testify that the IEEE 802.15.5 will serve well for wireless personal area networks and wireless sensor networks.