Truck Operations Research Articles

Assessment and Association of the World Health Organization’s Five Keys to Food Safety Among Selected Food Truck Operators Demographic in Jeddah City

The World Health Organization (WHO) reports that food handlers play a significant role in the maintenance of food safety from food production to storage. This study aims to assess the implementation of WHO five keys to safer food in the food trucks in Jeddah city. This study was an observational study. A total of 103 registered and licensed food trucks in Jeddah were selected using the opportunity sampling method. The WHO’s five keys to safer food questionnaire was used by the researchers to inspect and evaluate the hygiene status of food trucks. A chi-square test was used to assess the association between the demographic characteristics of food truck handlers and the implementation of the variables used to evaluate WHO’s five keys to safer food in their food trucks. P value < 0.05 was considered significant. The participation rate was 64.4% (103/160). Most food truck handlers were older than 30 years (55.3%), were men (81.6%), had secondary-level education (40.8%), and were Saudi nationals (69.9%). More than 50% of food truck handlers had less than three years of experience in business, and 67% of food truck handlers had undergone training in food handling. The researchers found good implementation of Key 5c. – “Water from cans/bottles used for cooking” (93.2%), followed by Key 1a. “Cleaning and disinfecting done periodically” (78.6%), and poor implementation for Key 3a. – “Calibrated thermometers are used to check temperatures” (30.1%) in the food trucks. The overall implementation score of WHO’s five keys to safer food was found to be good (score ≥ 8) in 57.3% of food trucks. Food handlers’ age, gender, and training in food handling indicated a significant association with the implementation of WHO’s five keys to safer food. The exposure of food handlers to food handling training indicated a significant association with good implementation of all five keys to safer food.

Sustainable open pit mining through GHG-conscious short-term production scheduling

ABSTRACT The mining industry is a notable contributor to global greenhouse gas (GHG) emissions, posing challenges to achieving the Paris Agreement’s goal of capping emissions at 30 Gt CO2-equivalent annually by 2030. This paper introduces a novel Mixed Integer Linear Programming (MILP) model tailored for short-term open-pit mine planning that integrates environmental considerations, particularly GHG emissions, alongside economic objectives. The model handles complex operational challenges including block sequencing, multiple transport destinations, and stockpile management. Additionally, it provides the opportunity to examine the adoption of In-Pit Crushing and Conveying (IPCC) systems as the main transport method, an innovative approach aimed at reducing emissions from haulage – which accounts for over 35% of GHG emissions in open-pit mining. Applied to a case study in an iron ore mine, the model not only considers the environmental benefits of IPCC systems compared to traditional truck and shovel (TS) operations but also highlights significant reductions in haulage costs and carbon tax liabilities. The findings demonstrate that fixed IPCC (FIPCC) systems, in particular, offer substantial decreases in GHG emissions, presenting a compelling case for their broader adoption in the industry.

Lessons Learned From Haul Truck Operator Near-Miss Events: Use of the Critical Decision Method to Identify Strategies to Improve Operator Safety in Mining

Lessons Learned From Haul Truck Operator Near-Miss Events: Use of the Critical Decision Method to Identify Strategies to Improve Operator Safety in Mining

Evaluation of road roughness and its influence on operating parameters in open-pit mines

Hauling ore and waste rock is a major cost factor in open-pit mines, encompassing both road maintenance and off-road truck operation. Road maintenance costs are directly influenced by traffic volume and the size of trucks using the roads. Off-road truck operating costs are impacted by various factors, including those affecting their movement, such as the direct interaction between tires and the road surface. Tire-road interaction significantly affects rolling resistance. Rougher surfaces with deeper grooves caused by track sinks lead to higher rolling resistance. This study employed laser profiling to characterize the surface roughness of five mine haul roads in two large Brazilian iron ore mines. This study evaluated the impact of these road irregularities on various operating parameters for 240-ton off-highway trucks, including rolling resistance, average travel speed, travel time, productivity, and unit transport cost. The results indicate that a 10 cm increase in road surface roughness can lead to a significant increase in rolling resistance (up to 5%), a substantial decrease in average speed (25%), a notable increase in travel time (26%), a decrease in productivity (19%), and a corresponding increase in unit transport cost (21%).

Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments

Efficient truck dispatching strategies are paramount in container terminal operations. The quality of these strategies heavily relies on accurate and expedient simulations, which provide a crucial platform for training and evaluating dispatching algorithms. In this study, we introduce data-driven machine learning methods to enhance container port truck dispatching simulation accuracy. These methods effectively surrogate the intersections within the simulation, thereby increasing the accuracy of simulated outcomes without imposing significant computational overhead in sparse data environments. We incorporate three data-driven learning methods: genetic programming (GP), reinforcement learning (RL), and a GP and RL hybrid heuristic (GPRL-H) approach. The GPRL-H method proved the most efficacious through a detailed comparative study, striking an effective balance between simulation accuracy and computational efficiency. It reduced the error rate of simulation from approximately 35% to about 7%, while also halving the simulation time compared to the RL-based method. Our proposed method also does not rely on precise Global Positioning System (GPS) data to simulate truck operations within a port accurately. Demonstrating robustness and adaptability, this approach holds promise for extending beyond port operations to improve the simulation accuracy of vehicle operations in various scenarios characterized by sparse data.

Plug-in charging or electric roads? Powering U.S. long-haul heavy-duty trucks in 2050

Abstract Pervasive plug-in fast chargers and/or electrified roadways (eRoads) might address the limited range, long recharging times, and reliance on greenhouse gas (GHG)-intensive, costly, and heavy batteries associated with electrifying long-haul heavy-duty trucks (HDTs). While these large-scale interventions shift environmental and cost burdens onto infrastructure, there is a lack of studies investigating how eRoads affect system-level GHG emissions, costs, material use, and peak electric grid power demands. We compare these aspects for the case of electrifying U.S long-haul HDTs (Class 8) in 2050 powered by combinations of plug-in fast chargers and eRoads. Our model accounts for battery downsizing, energy consumption, and truck operation patterns in quantifying life cycle impacts of batteries, plug-in chargers, eRoads, and hourly truck electricity demand. We find that plug-in fast chargers and eRoads reduce annualized 2050 HDT life cycle GHG emissions by 8% to 14% compared to using long-range batteries, which in turn have at least 50% lower emissions than diesel trucks. Conductive rails, overhead lines, and wireless eRoads (amortized across light- and heavy-duty vehicles) have lower system-wide costs than long-range batteries, plug-in fast chargers, or diesel trucks. Cost savings from smaller batteries, lower energy use and avoided recharging time offset high eRoads capital costs. While eRoads can reduce both system-level GHG and costs compared to diesel trucks, these reductions are sensitive to eRoads capital costs and losses from wireless power transfer and air resistance. eRoads require less lithium (87%) and copper (67%) than long-range batteries but increase regional peak power demands by up to 32%. Efficient wireless power transfer and aerodynamic pantographs enhance eRoads’ GHG and cost advantages, which may diminish if future batteries are more energy-dense, cheaper, or less GHG intensive. If successfully deployed, eRoads present opportunities for tighter integration between the transportation and electricity infrastructure systems, enabling optimized charging strategies to lower GHG emissions and costs.

Neck and shoulder pain and inflammatory biomarkers in plasma among forklift truck operators - A case-control study.

The aim of this study was to investigate a panel of inflammatory biomarkers in plasma from forklift truck operators (FLTOs) and healthy controls, and their relation to neck pain characteristics. From employees in a warehouse, 26 FLTOs were recruited and 24 healthy age- and sex-matched controls (CONs) were recruited via advertisement. The inclusion criterion for FLTOs was that they should operate reach decker and/or counterbalanced tilting mast forklift trucks. All participants were asked to answer a questionnaire covering demographic data, pain intensity numeric rating scale (NRS), anatomical spread, psychological distress, and health aspects. Pain sensitivity was measured using a pressure algometer. Blood samples were collected and analyzed for inflammatory proteins in plasma using a panel of 71 cytokines and chemokines. Multivariate data analysis including orthogonal partial least square-discriminant analysis (OPLS-DA) was performed to identify significant biomarkers. Thirty percent of FLTOs reported NRS > 3 in the neck. Shoulder pain was common in 26% of the FLTOs. Pain and discomfort that most often prevented completion of activities were in the neck (20%), lower back (32%), and hips (27%). The FLTOs reported significantly (p = 0.04) higher levels of anxiety than the CON group and they had significantly lower pressure pain thresholds in the trapezius muscle on both right (p < 0.001) and left sides (p = 0.003). A significant OPLS-DA model could discriminate FLTOs from CON based on nine inflammatory proteins where the expression levels of four proteins were upregulated and five proteins were downregulated in FLTOs compared to CONs. Twenty-nine proteins correlated multivariately with pain intensity. The profile of self-reported health, pain intensity, sensitivity, and plasma biomarkers can discriminate FLTOs with pain from healthy subjects. A combination of both self-reported and objective biomarker measurements can be useful for better understanding the pathophysiological mechanisms underlying work-related neck and shoulder pain.

Research on Energy Saving Effect of Parallel and Perpendicular Yard Layouts under Different Proportions of Transshipment at the Automated Container Terminal

The proportions of container transshipment is the key factor in determining the proportion of automated guided vehicle (AGV) and external container truck operations. In terms of parallel and perpendicular layouts of automated container terminals (ACTs), varying proportions of container transshipment result in different proportions of AGVs and external container truck operations, subsequently leading to distinct impacts on energy consumption (EC) for each ACT layout. This paper deemed EC as the primary evaluation criterion, established an EC model encompassing yard cranes (YCs) and container trucks, and investigated the EC of parallel and perpendicular layouts at different proportions of container transshipment. The results indicate that when the proportions of container transshipment were less than 17%, the parallel layout had lower EC; when it was between 17% and 21%, there was not much difference between the two layouts; when it was greater than 21%, the perpendicular layout had lower EC. This conclusion was based on an ideal environment established in this paper. When making decisions, decision makers should use this model as a starting point and adapt it flexibly to the actual situation of the port, in order to arrive at a reasonable and feasible plan.

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.

Modeling cost-optimal fuel choices for truck, ship, and airplane fleets: The impact of sustainability commitments

For the next 25 years, numerous transport operators have introduced sustainability commitments to mitigate carbon emissions. However, static goals to reduce fossil fuel usage and adopt renewable fuel technologies pose economic risk. This study analyzes the costs of strategic fleet replacement through a mixed integer linear program. Comparing three Norwegian transport operators, we determine cost-optimal investments in fossil and renewable fuel technologies for two approaches: One integrating and the other neglecting sustainability commitments. Our findings reveal that the truck operator’s sustainability commitments incur minimal additional costs, with battery-electric trucks proving cost-effectiveness early. In contrast, ship and airplane operators exhibit significant differences in fleet replacement decisions, resulting in additional costs ranging from +6% to +31% for ships and +4% to +11% for airplanes, across fuel cost and carbon price scenarios. Norwegian carbon price regulations fall short in incentivizing a cost-competitive technology transition for ships and airplanes. Additional policies are needed to encourage gradual fleet replacement.

Understanding the Workload of Remote Truck Operators with Discrete Event Simulation

This study employs a discrete event simulation (DES) model to understand the dynamic workload of remote truck operators managing partially-automated trucks. The DES model uses operator queues and event generators simulating automated truck events and leverages data from the California DMV’s disengagement database and driving simulation experiments. Disengagement data were partitioned into three groups by disengagement frequency: low, moderate, and high and separate arrival time distributions were developed for each group. Simulations from the model suggest that for companies with low disengagement rates, operator utilization will likely remain below minimal thresholds to prevent boredom. In contrast, companies with moderate or high disengagement rates both exceed operator utilization capacity and generate prolonged wait times as more trucks are controlled. These findings suggest that calibrating remote truck control to human capabilities will be challenging. A sensitivity analysis suggests that accurately estimating disengagement rates will be crucial for model accuracy and predictive performance.

Human reliability analysis in maintenance and repair operations of mining trucks: A Bayesian network approach

Failures in mining machinery can abruptly halt mineral production and operations, emphasizing the indispensable role of humans in maintenance and repair operations. Addressing human errors is crucial for ensuring a safe and reliable system, particularly during maintenance activities where accidents frequently occur. This paper focuses on evaluating Human Reliability (HR) to enhance activity implementation effectiveness. Given the challenge of limited and uncertain data on human errors, this study aims to estimate the probability of human errors using Bayesian networks (BN) under uncertain parameters. Applying this approach to assess HR in the maintenance and repair operations of mining trucks at Golgohar Iron Ore Mine in Iran, the study identifies critical factors influencing error occurrence in a fuzzy environment. The results highlight key factors impacting human error and offer insights into estimating HR with minimal human intervention.

Integrating Task Component Design in Excavator–Truck Operation Planning: Enhancing Automation Systems through a Web-Based System

Integrating Task Component Design in Excavator–Truck Operation Planning: Enhancing Automation Systems through a Web-Based System

Developing Fuel Savings Performance Function Considering Various Truck Platooning Configurations

&lt;div&gt;Truck platooning facilitates the operation of trucks in close proximity to one another, resulting in decreased air resistance and improved fuel efficiency. While previous research has mostly focused on the effects of intra-distance on fuel savings, this study aims to develop fuel savings performance functions considering various truck platooning configurations. This article comprehensively investigates the influence of different truck platoon configurations on fuel savings. This analysis focuses on examining the impacts of several variables including inter-vehicle distance, platoon speed, truck weight, number of trucks in the platoon, and the truck’s distinctive design characteristics. Data used in the analysis were collected from 10 different field experiments. Three machine learning techniques—artificial neural networks (ANN), extreme gradient boosting (XGBoost), and K-nearest neighbors (KNN)—alongside the negative binomial regression model were employed. Upon evaluation, the negative binomial regression model emerged as the most accurate, boasting a prediction accuracy of 74%. This high-performing model was subsequently leveraged to derive an equation for estimating fuel savings. The results indicated that the truck platoon’s size is the most significant factor affecting fuel efficiency. Specifically, the inclusion of additional trucks in the platoon leads to substantial fuel savings. Moreover, as the platoon’s speed increases, there is a noticeable increase in fuel savings. The design of the truck plays a role: conventional trucks are more fuel efficient than cab-over trucks. Lastly, the weight of the truck has a minor impact on the platoon’s fuel efficiency. Overall, it is essential to consider multiple variables when evaluating truck platoon arrangements for optimal fuel efficiency.&lt;/div&gt;

Evaluation of Coal Transport Route in Jambi City (Talang Duku Port-East Ring Road II)

In its implementation, the role of coal transportation infrastructure is important in supporting the mining industry in Jambi City. However, it also causes various complex problems; one of them is traffic congestion on arterial roads in Jambi City. In May 2023, the Jambi Regional Police of the Republic of Indonesia issued a notification letter to stop the mobilization of coal transportation in Jambi Province. The policy caused several coal companies to switch their transportation method to shipping in order to continue operating, while several other companies stopped operating until an indeterminate time. Therefore, this study aims to compare traffic performance before and after the implementation of the policy on Jambi City arterial roads, especially on Talang Duku Port Road and East Ring Road II. The method used was a comparative analysis of the degree of saturation before and after the enactment of regulations on the prohibition of coal truck operations. The results of this study show the importance of traffic management as one of the solutions to reduce traffic congestion. Nevertheless, other alternatives are still needed to support the smooth operation of coal transportation activities.

Integration of HIRARC and B/C Ratio to Reduce the Number of Accidents in PT. XYZ

PT XYZ is one of the companies engaged in the mining industry. The company has total coal reserves of 5.8 million tons and produces a coal calorific value of 4900 Kcal/kg GAR. PT XYZ uses a 30-ton class dump truck for overburdened transportation activities. Overburden transportation is transporting material from one location or facility to another. The operation of dump trucks has risks that need to be controlled to minimize work accidents. Truck operators have considerable risks, including bumping, hitting, slipping, falling, and pinched. This study aims to identify potential accidents that can occur and propose preventive measures based on the risk level of each possible accident in the operation of dump trucks using the HIRARC method at PT XYZ. Based on the results of the study shows that the potential hazards experienced by dump truck operators include collisions, pinching, slipping, overturning units, backing units when on an incline, buckets crashing into vessels, grazing other units, low back pain, neck pain, herniated nucleus pulposus, fatigue, loss of control, unprotected operators, collapsed and dragged units, reduced control, loss of control, trapped units, buried units, falling into ravines, damaged units. In addition, the risks experienced by dump truck operators are 6 high risks, 7 medium risks, and 9 low risks. From the overall transportation of overburden material, dump truck operators experienced a high risk of 27%, a medium risk of 32%, and a low risk of 41%.

Modeling and Optimization of Container Drayage Problem with Empty Container Constraints across Multiple Inland Depots

Container drayage involves the transportation of containers by trucks. Although the distance is relatively short compared to maritime and rail transport, container drayage accounts for 25% to 40% of the total container transportation costs and significantly contributes to increased fuel consumption and carbon emissions. Thus, the modeling of the container drayage problem (CDP) has received a lot of attention in the last two decades. However, the three fundamental modeling factors, including the combination of trucking operation modes and empty container relocation strategies, as well as empty container constraints and multiple inland depots, have not been simultaneously investigated. Hence, this study addressed a comprehensive CDP that simultaneously incorporates the three modeling factors. The problem was formulated as a novel mixed integer linear programming (MILP) model based on the DAOV graph. Given the complexity of this problem, it was not realistic to find an exact solution for large instances. Therefore, an improved genetic algorithm (GA) was designed by integrating the “sequential insertion” method and “solution re-optimization” operation. The performance of Gurobi and GA was validated and evaluated through randomly generated instances. The results indicate that (1) the proposed algorithm can provide near-optimal solutions for large-scale instances within a reasonable running time, (2) the greatest cost savings from combining trucking operation modes and empty container relocation strategies range from 10.45% to 31.86%, and (3) the three modeling factors significantly influence the fuel consumption and carbon emissions, which can provide managerial insights for sustainable container drayage practices.

Simulation analysis and prevention strategies for fatigue fracture of the swivel shaft in concrete pump trucks

During the pumping operation of concrete pump trucks, the swivel shaft primarily withstands a series of complex loads such as bidirectional bending moments and torque (stemming from the self-weight of the boom in the amplitude plane, the self-weight of the concrete; wind load and rotational inertia force in the rotation plane; torque from the delivery pipe and the concrete inside), as well as lateral wind load on the concrete and the impact vibration caused by the flow of concrete. These factors can easily lead to fractures due to the loads exceeding the material load limit of the swivel shaft. This study targets the swivel shaft fracture accidents during the construction process of concrete pump trucks, hypothesizing that the accidents occur due to improper installation angles of the swivel shaft (angle between the oil channel hole and the cylinder axis). Firstly, numerical simulation of the onsite conditions is conducted and compared with the results observed from the actual accidents. Secondly, by fixing the onsite conditions (keeping the boom posture unchanged), a 360° rotational simulation calculation of the fractured swivel shaft is performed to identify the relationship between high stress intervals and the swivel shaft rotation angle and the angle between the second variable oil cylinder axis. Finally, a full-condition calculation is conducted for the boom and the swivel shaft. The results indicate that the high stress area of the swivel shaft does not change with the swivel shaft rotation angle (when the boom is fixed) but is related to the position of the second variable oil cylinder axis and is symmetrically distributed around it. Therefore, adjusting the swivel shaft rotation angle to avoid the high stress area near the symmetry axis of the second variable oil cylinder can prevent the fracture accidents of the swivel shaft.

Seasonal energy efficiency: a case study of an urban distribution battery electric truck operating in Brazil

Seasonal energy efficiency: a case study of an urban distribution battery electric truck operating in Brazil

Application of the Machine Learning Method to Determine Spring Load Limits and Winter Weight Premium

Freight transportation plays a crucial role in sustaining the Canadian economy. However, heavy truck transportation also puts enormous pressure on roadway networks. Spring Load Restrictions (SLR) are implemented to minimize road damage caused by heavy traffic during the thaw-weakening season, and Winter Weight Premium (WWP) is used to reduce the impact of SLR on trucking operations by allowing higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of the pavement. Different methods have been proposed to improve the application of SLR and WWP; however, they rely mainly on indirect indices, such as the cumulative thawing index and cumulative freezing index, which pose challenges in their calculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a novel approach, machine learning models directly derive the start and end dates of SLR and WWP from frost and thaw depths in the pavement structure which are determined by pavement temperature and moisture content. In contrast to previous studies that neglected the influence of soil moisture content on determining the start and end dates of SLR and WWP, this study examines the variation in soil moisture content to evaluate the validity of existing theories. The findings reveal a high level of agreement between the machine learning model’s estimations of frost and thaw depths and the measured values, with R2 values exceeding 0.91.