Truck Fleet Research Articles

Energy-Efficient Dispatching of Battery Electric Truck Fleets with Backhauls and Time Windows

<div>The adoption of battery electric trucks (BETs) as a replacement for diesel trucks has potential to significantly reduce greenhouse gas emissions from the freight transportation sector. However, BETs have shorter driving range and lower payload capacity, which need to be taken into account when dispatching them. This article addresses the energy-efficient dispatching of BET fleets, considering backhauls and time windows. To optimize vehicle utilization, customers are categorized into two groups: linehaul customers requiring deliveries, where the deliveries need to be made following the last-in-first-out principle, and backhaul customers requiring pickups. The objective is to determine a set of energy-efficient routes that integrate both linehaul and backhaul customers while considering factors such as limited driving range, payload capacity of BETs, and the possibility of en route recharging. We formulate the problem as a mixed-integer linear programming model and propose an algorithm that combines adaptive large neighborhood search and simulated annealing metaheuristics to solve it. The effectiveness of the proposed strategy is demonstrated through extensive experiments using a real-world case study from a logistics company in Southern California. The results indicate that the proposed strategy leads to a significant reduction in total energy consumption compared to the baseline strategy, ranging from 11% to 40%, while maintaining reasonable computational time. In addition, the proposed strategy provides solutions that are better than or comparable with those obtained by other metaheuristics. This research contributes to the development of sustainable transportation solutions in the freight sector by providing a novel approach for dispatching BET fleets. The findings emphasize the potential of deploying BETs to achieve energy savings and advance the goal of green logistics.</div>

Strategy planning for sustainable biodiesel supply chain produced from waste animal fat

The rapid depletion of fossil fuels and the adverse environmental effects of combustion has growing concerns about the sustainability of fossil fuel and the world turns its focus on cleaner energy sources. Animal fat-based biodiesel is a sensible alternative to non-renewable energy sources as it's produced from inedible, affordable, and easily available biomass. The effective design of a biodiesel supply chain is pivotal for making it more advantageous than fossil fuels from a social, economic, and environmental standpoint. This model reduces the biodiesel supply chain cost while minimizing its environmental impact and maximizing its social impact. It is planned to have multi-supply sites for providing beef, chicken, and mutton fat, and numerous biorefineries, rendering factories, and distribution depots. The augmented ϵ-constraint approach combined with the lexicographic optimization technique is used to solve the multi-objective optimization model. A numerical example is analyzed, and the results show that the environmental impact of establishing biodiesel production facilities is 99.5% of the environmental impact of the entire biodiesel supply chain and that the cost of installing biodiesel production plants is 99% of the cost of the entire biodiesel supply chain. A heterogeneous fleet of trucks is incorporated to distribute the material throughout the supply chain whereby the impact of transportation on the environment is 0.0007% of the total environmental impact of the biodiesel supply chain and transportation cost amount to 0.0014% of the total costs of the biodiesel supply chain. The model demonstrates the biodiesel supply chain network under a sustainability framework and addresses the environmental and social impact based on the life cycle assessment which accelerates the transition to a renewable energy future.

Constraint-Based snowplow optimization model for winter maintenance operations

In the annual winter route maintenance, transportation agencies often deploy multiple fleets of trucks for snow control and removal activities over a vast maintenance area which creates an operational problem in determining the optimal maintenance routes and fleet size. The objective of this paper is to develop a snowplow routing optimization model to enhance the efficiency of snow removal route planning. The routing optimization model was developed using vehicle routing problems, constraint-based programming, and geographic information system. The developed model was applied to optimize the snow removal route planning practice of a District in Kansas, United States, as a case study. The result of this study shows that the optimization model can help minimize the fleet size and increase the level of service for treating snow routes within the selected District. The results of this study are expected to assist transportation agencies in optimizing their snow route removal in winter maintenance operations.

Sustainable Supply Chain Management of COVID-19 Vaccines for Vaccination Delivery based on Routing Algorithms

Covid-19 pandemic has changed daily life in the city of Athens where vaccines are exploited with supply chain technology potentiality. Vaccines are tracked at the city’s airport till their delivery to vaccination centers. Due to the sensitivity of vaccines to the warm climate inherent in the city, delivery is assigned to a fleet of trucks. Specifically, two use cases, i.e., UC-I and UC-II, are proposed, which are based on global and local routing algorithms to exploit trucks’ load COVID-19 vaccine delivery from the airport and transport it to vaccination centers. In this paper, we focus on the supply chain routing algorithm technology of collecting COVID-19 vaccines from the airport and delivering them to vaccination centers in the smart city of Athens, Greece. Concretely, the purpose and the objectives of the research effort are in the areas of: (1) describing in deep detail the proposed supply chain system, (2) exploiting the adopted architecture based on certain separate use cases for system experimentation, (3) adopting specific vaccination routing algorithms to support vaccination distribution, and (4) evaluating experimentally the proposed supply chain system architecture with regards to the adopted use cases’ routing algorithms.

A Machine Learning–Based Tire Life Prediction Framework for Increasing Life of Commercial Vehicle Tires

Abstract In the commercial freight industry, tire retreading decisions are often conservative due to limited knowledge of a tire’s remaining service life. This practice leads to increased costs and material waste. This paper proposes a machine learning–based approach for estimating tire casing life and retreadability, focusing on usage data rather than wear information. This approach could extend the tire’s lifespan and reduce landfill waste. Data integration from diverse tire casing measurement sources presents challenges, including imbalanced removal data. Our methodology addresses these challenges by using historical inspection, telematics, and finite element modeling (FEM) datasets. We introduce “Tire Casing Energy” as a comprehensive usage input and apply a Variance-Reduction Synthetic Minority Oversampling Technique (VR-SMOTE) for data imbalance rectification. A random forest model is used to estimate the state of the tire casing and the casing removal probability, with Bayesian optimization applied for hyperparameter tuning, enhancing model accuracy. The proposed prediction framework is able to differentiate different truck fleets and tire locations based on their usage parameters. With the aid of this machine learning model, the importance and sensitivity of different tire usage parameters can be obtained, which is beneficial to maximize tire life.

Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty

Effective humanitarian relief operations are challenging in the aftermath of disasters, as trucks are often faced with considerable travel time uncertainties due to damaged transportation networks. Efficient deployment of Unmanned Aerial Vehicles (UAVs) potentially mitigates this problem, supplementing truck fleets in an impactful manner. To plan last-mile relief distribution in this setting, we introduce a multi-trip, split-delivery vehicle routing problem with trucks and UAVs, soft time windows, and stochastic travel times for last-mile relief distribution, formulated as a stochastic dynamic program. Within a finite time horizon, we aim to maximize a weighted objective function comprising the number of goods delivered, the number of different locations visited, and late arrival penalties. Our study offers insights into dealing with travel time uncertainty in humanitarian logistics by (i) deploying Unmanned Aerial Vehicles (UAVs) as partial substitutes for trucks, (ii) evaluating dynamic solutions generated by two deep reinforcement learning (RL) approaches – specifically value function approximation (VFA) and policy function approximation (PFA) – and (iii) comparing the RL solutions with solutions stemming from mathematical programming and dynamic heuristics. Experiments are performed on both Solomon-based instances and two real-world cases. The real-world cases – the 2015 Nepal earthquake and the 2018 Indonesia tsunami – are based on locally collected field data and real-world UAV specifications, and aim to provide practical insights. The experimental results show that dynamic decision-making improves both performance and robustness of humanitarian operations, achieving reductions in lateness penalties of around 85% compared to static solutions based on expected travel times. Furthermore, the results show that replacing half of the trucks with UAVs improves the weighted objective value by 11% to 56%, benefitting both reliability and location coverage. The results indicate that both the deployment of UAVs and the use of dynamic methods successfully mitigate travel time uncertainties in humanitarian operations.

Productivity estimation of battery trolley mining truck fleets

ABSTRACT The mining sector is facing an immediate challenge to reduce its carbon emissions. The movement of material with mining truck fleets is a critical factor in reducing emissions. As the industry gradually replaces diesel trucks with more environmentally friendly transportation systems, it is important to analyse new solutions. This paper presents a new type of mining truck fleet called the Battery Trolley system, which is a combination of advanced technologies, including a battery-electric drivetrain, autonomy, trolley assist, and energy recovery systems. Depending on the site-specific differences and technology adoptions, decision-makers have three Battery Trolley system configurations to choose from: dynamic charging, stationary charging, and dual trolley Battery Trolley systems, each of which has its own advantages and disadvantages. One of the challenges of using Battery Trolley systems is their capacity and productivity. This study uses open-accessible project parameters and databases, with engineering assumptions, to create a mining scenario for both the dynamic charging and stationary charging perspectives. The study provides an equation to evaluate the Battery Trolley system’s mining productivity. Results show an incremental analysis of the Battery Trolley productivity as the trolley power increases from 8 MW to 32 MW in various Battery Trolley configurations. The study compares the Battery Trolley productivity for several open-pit mining applications, including copper, iron, and overburden waste. The results indicate that: (i) trolley power limitations significantly affect the capacity of Battery Trolley systems; (ii) a stationary charging option can achieve higher capacity and productivity than dynamic charging; (iii) Battery Trolley productivity varies in different applications under the given simulation assumptions.

The multi-visit drone-assisted pickup and delivery problem with time windows

We consider a new combined truck–drone routing problem with time windows in the context of last-mile logistics. A fleet of trucks, each equipped with an identical drone, is scheduled to provide both pickup and delivery services to a set of customers with minimum cost. Some customers are paired, in that the goods picked up from one must be delivered to the other on the same route. Drones are launched from and retrieved by trucks at a pool of designated stations, which can be used multiple times. Each drone can serve multiple customers in one flight. We formulate this problem as a large-scale mixed-integer bilinear program, with the bilinear terms used to calculate the load-time-dependent energy consumption of drones. To accelerate the solution process, multiple valid inequalities are proposed. For large-size problems, we develop a customised adaptive large neighbourhood search (ALNS) algorithm, which includes several preprocessing procedures to quickly identify infeasible solutions and accelerate the search process. Moreover, two feasibility test methods are developed for trucks and drones, along with an efficient algorithm to determine vehicles’ optimal waiting time at launch stations, which is important to consider due to the time windows. Extensive numerical experiments demonstrate the effectiveness of the valid inequalities and the strong performance of the proposed ALNS algorithm over two benchmarks in the literature, and highlight the cost-savings of the combined mode over the truck-only mode and the benefits of allowing multiple drone visits.

Studi Optimalisasi Rute Pengangkutan Sampah Stationary Container System Berbasis Sistem Informasi Geospasial (SIG) di Kecamatan Balikpapan Timur

Waste transportation in East Balikpapan District currently does not have an effective waste transportation route, this has resulted in not optimal waste transportation, which is indicated by the large amount of waste accumulated at the TPS. Based on these problems, East Balikpapan requires optimizing waste management, especially in waste transportation, to reduce the accumulation of waste in TPS, which can cause environmental pollution. The research procedure used is to optimize the Waste transportation route in the East Balikpapan District using a qualitative descriptive method. Through this method, this research can improve the waste transportation system at the research location by providing improvements that are modelled using Geographic Information Systems (GIS). Determination of the most effective transportation route will be adjusted to the condition of the road being traversed, the distance travelled and the speed of the vehicle in the waste transportation process as well as the time it takes for the SCS (stationary container system) system to transport waste. Based on the existing conditions, transporting two dump trucks takes 7 hours, and one dump truck takes 6 hours and 30 minutes, with a distance of 69 km, 68 km and 104 km. The distance and time for transporting waste under existing conditions can be optimized with alternative routes, optimization results for the three dump truck fleets, namely the distance of 68 km with a time of 6 hours, a distance of 46 km in 5 hours and a distance of 68 km with a time of 5 hours in 2 trips per day.

Joint operation planning of drivers and trucks for semi-autonomous truck platooning

Semi-autonomous truck platooning, a futuristic and promising traveling mode of trucks on highways, has received extensive attention as autonomous driving gains more maturity. Under the semi-autonomous platooning mode, rather than requiring a human driver for each truck in the platoon, only one human driver is needed for the leading truck, and all following trucks can be controlled by autonomous driving and wireless communication technology. Therefore, in addition to saving energy, such a technology can help reduce labor costs by using fewer drivers to fulfill the same demand. Compared to traditional truck platooning, the schedules of drivers under semi-autonomous truck platooning may not be exactly the same as trucks, which are usually neglected in recent studies. Therefore, in this paper, we make the first attempt to develop a mathematical modeling framework to optimise the operation plan of drivers and trucks interdependently. To be mentioned, the truck fleet is equipped with the semi-autonomous platooning function and time windows. Specifically, the operation plan will dictate the number of drivers to be dispatched, and traveling routes and time schedules of drivers and trucks interdependently with the objective to minimise the total operation cost, including drivers' fixed dispatch cost, on-road labor cost, and energy consumption cost while fulfilling all delivery demands. To tackle the large-scale problem in a timely manner, a tailored Lagrangian Relaxation approach is proposed to solve the model. Numerical experiments are conducted to demonstrate the performance of the proposed modeling framework and validate the feasibility and efficiency of the proposed solution algorithm.

Renewable Energy and Decarbonization in the Canadian Mining Industry: Opportunities and Challenges

Mining in Canada stands as one of the most energy-intensive sectors, playing a pivotal role as a significant provider of copper, nickel, and cobalt to the international market. Anticipated growth in the global population, coupled with the transition of several low-income economies to middle-income status, is poised to escalate the demand for essential raw materials. This surge in demand is expected to drive an increase in energy consumption across various stages of the Canadian mining industry, encompassing exploration, extraction, processing, and refining. Due to their geographical constraints, most Canadian mining operations rely heavily on fossil fuels such as diesel and heavy fuel. Considering the global shift towards decarbonization and the pursuit of net-zero emission targets, exploring avenues for adopting electrification solutions and integrating renewable energy technologies, particularly in sizable surface mines, is imperative. Within this context, our study delves into the challenges and prospects associated with infusing renewable energy technologies and embracing electrification alternatives within Canadian mining practices. This exploration encompasses a comprehensive review of pertinent literature comprising academic research, technical analyses, and data disseminated by international entities and experts. The findings underscore a prevalent trend wherein Canadian mining enterprises are prominently investing in robust electric truck fleets, particularly for heavy-duty operations. Additionally, incorporating renewable energy solutions is notably prevalent in remote sites with extended operational lifespans. However, an in-depth examination reveals that the most formidable hurdles encompass successfully integrating renewable energy sources and battery electric vehicles. Financial constraints, logistical intricacies, and the imperative to enhance research and development competencies emerge as pivotal challenges that demand strategic addressing.

ANALYSIS OF PARAMETERS AFFECTING FUEL CONSUMPTION BASED ON STATISTICAL MODELLING

Fluctuating fuel prices and the importance of road transport in the contextof the environmental impact of transport make the research relatedto fuel consumption analyses still up‑to‑date and socially important. Thearticle presents the methodology for determining the statistical modelof fuel consumption based on the analysis of the significance of drivingparameters. The fleet of trucks (sets of tractor truck and semi‑trailer) wasselected as the research object due to their dominant share in the roadcommercial transport sector in the transport of goods. In order to calculatedeterminants of fuel consumption, the classic method of least squares wasused, as a result of which an optimal statistical model of fuel consumptionwas developed using the elimination method. The model developed wasalso verified based on the real data.

COMPARATIVE ANALYSIS OF THE UNIT COSTS OF THE POLISH FREIGHT INTERNATIONAL TRANSPORT OPERATING ON THE EASTERN AND EU MARKETS, 2009-2021

The article represents next part of the cyclical publications of the Departmentof Economic Research and Transport accessibility in the field of cost analysis ofthe Polish international freight transport companies. It made a comparative assessmentof the average unit costs of international carriers incurred in 2021 inrelation to the results from previous years. The study covered enterprises servingEastern markets and the European Union (EU). The statistical characteristics ofthe surveyed entities are presented, taking into account their size, determinedbased on the size of the fleet of trucks operated. It also shows the average costsof one vehicle-kilometre (vehicle-km) of mileage for a truck over 12 Mg GVM,depending on the size of the enterprise and taking into account the direction oftransport. The generic cost structure of the surveyed carriers was also analysed.The development of unit costs in 2021 in relation to the period 2009-2020 is presentedin tabular and graphic form. A summary of the evaluation of the impact ofexternal factors on the functioning of the sector, from the carriers’ perspective, wasalso presented. The study was conducted based on the questionnaires completedby carriers associated with (ZMPD) Association of International Road Carriers.

Load-Out and Hauling Cost Increase with Increasing Feedstock Production Area

The impact of average delivered feedstock cost on the overall financial viability of biorefineries is the focus of this study, and it is explored by modeling the efficient delivery of round bales of herbaceous biomass to a hypothetical biorefinery in the Piedmont, a physiographic region across five states in the Southeastern USA. The complete database (nominal 150,000 Mg/y biorefinery capacity) had 199 satellite storage locations (SSLs) within a 50-km radius of Gretna, a town in South Central Virginia USA, chosen as the biorefinery location. Two additional databases, nominal 50,000 Mg/y (29.1-km radius, 71 SSLs) and nominal 100,000 Mg/y (40-km radius, 133 SSLs) were created, and delivery was simulated for a 24/7 operation, 48 wk/y. The biorefinery capacities were 15.5, 31.1, and 47.3 bales/h for the 50,000, 100,000, and 150,000 Mg/y databases, respectively. Three load-outs operated simultaneously to supply the 15.5 bale/h biorefinery, six for the 31.1 bale/h biorefinery, and nine for the 47.3 bale/h biorefinery. The required truck fleet was three, six, and nine trucks, respectively. The cost for load-out and delivery was 11.63 USD/Mg for the 50,000 Mg/y biorefinery. It increased to 12.46 and 12.99 USD/Mg as the biorefinery capacity doubled to 100,000 Mg/y and tripled to 150,000 Mg/y. Most of the cost increase was due to an increase in truck cost as haul distance increased with the radius of the feedstock supply area. There was a small increase in load-out cost due to an increased cost for travel to support the load-out operations. The less-than-expected increase in average hauling cost for the increase in feedstock production area highlights the influence of efficient scheduling achieved with central control of the truck fleet.

Bridging the Gap: A Comparative Analysis of Regressive Remaining Useful Life Prediction and Survival Analysis Methods for Predictive Maintenance

Regressive Remaining Useful Life Prediction and Survival Analysis are two lines of research with similar goals but different origins; one from engineering and the other from survival study in clinical research. Although the two research paths share a common objective of predicting the time to an event, researchers from each path typically do not compare their methods with methods from the other direction. Given the mentioned gap, we propose a framework to compare methods from the two lines of research using run-to-failure datasets. Then by utilizing the proposed framework, we compare six models incorporating three widely recognized degradation models along with two learning algorithms. The first dataset used in this study is C-MAPSS which includes simulation data from aircraft turbofan engines. The second dataset is real-world data from streamed condition monitoring of turbocharger devices installed on a fleet of Volvo trucks.
 
 
 

Application of fuel cell and alternative fuel for the decarbonization of China's road freight sector towards carbon neutral

The deployment of new energy trucks is a crucial national strategy in China to achieve deep decarbonization in road transport. Although fuel cell trucks and electric tucks are regarded as alternatives to conventional fuel trucks due to the advantage of zero usage emissions, their applicability for each truck type has not been determined.By developing a total cost of ownership analysis, this study dynamically determines the most economical alternative fuel type for each truck type and identifies when the large-scale deployment of new energy trucks is economically achievable. Then, by conducting a life cycle analysis with a localized database, this study quantitively evaluates the green gashouse emission intensities and their possible changes of different types of trucks. Finally, the emission reduction potential in the truck fleet by applying fuel cell and alternative fuel during 2020–2050 is comprehensively evaluated.The main results suggest that electric trucks are economically suitable for mini and light-duty trucks by 2031 and 2026, while fuel cell trucks suit medium-duty and heavy-duty trucks by 2027 and 2032. The application of alternative fuels in the truck fleet could achieve 28% carbon emissions in 2030 and up to 80% in 2050.

Multivisit Drone-Vehicle Routing Problem with Simultaneous Pickup and Delivery considering No-Fly Zones

The employment of drones for the distribution of goods represents a significant avenue for addressing logistical challenges at the end of the supply chain. The truck-drone cooperative delivery model overcomes drone limitations such as limited capacity and endurance and has emerged as a crucial mode of drone participation in logistics delivery. This delivery model effectively reduces delivery costs and shortens delivery times. Herein, we examine a variant of the truck-drone routing problem, which encompasses the strategic deployment and routing of multiple fleets of trucks, each equipped with an auxiliary drone. The objective is to fulfill all the pickup and delivery demands of a designated customer base while minimizing the overall route cost. Within this problem domain, drones are authorized to serve multiple customers within their capacity and endurance limits, providing both pickup and delivery services during each trip. However, the utilization of drones for servicing all customers is impeded by the existence of no-fly zones that have been implemented in numerous cities worldwide. These prescribed no-fly zones cause significant challenges when attempting to optimize the routing of truck-drone operations. Thus, this study constructs a mixed integer linear programming (MILP) model for the path optimization problem of joint service of trucks and drones considering no-fly zones and simultaneous pickup and delivery. Given the intricacy of the MILP model, we propose a two-stage heuristic algorithm based on a simulated annealing approach, combined with strategies for rectifying infeasible solutions and expediting algorithmic processes. During the phase of computational experimentation, we explore the advantages derived from enabling drones to serve multiple customers and assess the effectiveness of the proposed model and two-stage heuristic algorithm. Finally, sensitivity analysis is conducted on two key parameters.

Analysis of the optimal service lives of trucks under EUAC and benefit-based methods: A case-study of a road tanker fleet

The trucking industry utilizes an extensive fleet of trucks. Of specific relevance is the determination of optimal economic service lives for this stock. Using a case study of a large-scale road tanker fleet belonging to a transportation arm of a major Eurasian oil company, we outline the application of some traditional life-cycle cost-based (EUAC) and benefit-based techniques for estimating the optimal service lives of the truck fleet; then we propose a simple and user-friendly version of modified cost-benefit analysis that integrates both cost-side and benefit-side considerations into the picture and accounts for the alternative investment opportunities available to the fleet owner. All the modelling is presented in a discrete-time format for convenience and ease of use. The key finding from the case study is that the answer to the optimal service lives of trucks hinges on the alternative investment opportunities available to their owner. The anticipated returns from such alternative investment opportunities enter as discount rates into analysis and the techniques we discuss are sensitive to the value of this parameter.

Solving a multi-objective solid transportation problem: a comparative study of alternative methods for decision-making

The transportation problem in operations research aims to minimize costs by optimizing the allocation of goods from multiple sources to destinations, considering supply, demand, and transportation constraints. This paper applies the multi-dimensional solid transportation problem approach to a private sector company in Egypt, aiming to determine the ideal allocation of their truck fleet.In order to provide decision-makers with a comprehensive set of options to reduce fuel consumption costs during transportation or minimize total transportation time, a multi-objective approach is employed. The study explores the best compromise solution by leveraging three multi-objective approaches: the Zimmermann Programming Technique, Global Criteria Method, and Minimum Distance Method. Optimal solutions are derived for time and fuel consumption objectives, offering decision-makers a broad range to make informed decisions for the company and the flexibility to adapt them as needed.Lingo codes are developed to facilitate the identification of the best compromise solution using different methods. Furthermore, non-dominated extreme points are established based on the weights assigned to the different objectives. This approach expands the potential ranges for enhancing the transfer problem, yielding more comprehensive solutions.This research contributes to the field by addressing the transportation problem practically and applying a multi-objective approach to support decision-making. The findings provide valuable insights for optimizing the distribution of the truck fleet, reducing fuel consumption costs, and improving overall transportation efficiency.

Optimisation of transport and technological system parameters of an agricultural enterprise in conditions of partial uncertainty

DOWNLOAD ARTICLE Optimisation of transport and technological system parameters of an agricultural enterprise in conditions of partial uncertainty Viacheslav Matsiuk, Viktoriia Opalko, Liliya Savchenko, Oleg Zagurskiy, Nadiia Matsiuk Received 07.04.2023, Revised 17.07.2023, Accepted 11.08.2023 Abstract At the stage of production of a wide range of agricultural products, to ensure the smooth operation of agricultural enterprises, it is necessary to solve the problems of fast and efficient delivery of relevant equipment, spare parts, and consumables with the rational use of available production resources. The research aims to improve the transport and technological system for the supply of orders in the form of consignments to meet the needs of the production activities of an agricultural enterprise. For this purpose, an agent-based simulation model was developed in the AnyLogic 8.7 environment using the Java compiler, since this toolkit allows simultaneously combining discrete-event and agent-based approaches. The model was implemented on the example of an enterprise of a separate subdivision of the National University of Life and Environmental Sciences of Ukraine “Agronomic Research Station”. As a result, a comprehensive, optimisation mathematical model of the supply of goods on an extensive network of road routes using the agricultural enterprise’s fleet of vehicles under conditions of partial uncertainty was obtained. In the course of experiments and calculations based on the real process of an agricultural enterprise, a range of values of the size of the truck fleet that meets the optimisation conditions has been found. It has been determined that the range of values close to the optimal size of the unloading truck fleet varies from 9 to 14 units. It has been established that the values of the optimisation criterion describing the average delivery time from the beginning of the need for an order to the moment of its delivery vary from 9.96 to 12.78 hours. The limit level of load of the transport and technological system is determined, at which the limit level of technological fault tolerance is ensured. It is 135 or more orders per year for each supplier with an estimated fleet of 12 trucks. The results of the study, such as the use of analytical tools and algorithms to optimise routes and allocate resources, can be used to improve transport efficiency, and help companies choose the most profitable and environmentally friendly routes for transportation