Utilization Rate Of Vehicles Research Articles

Coordinated last-mile deliveries with trucks and drones: A comparative study of operational modes

Throughout the recent decade, drone delivery technology has been developed to address problems such as last-mile delivery and recurrent traffic jams in metropolitan regions. To resolve the relatively short travel range and limited capacity issues of drones, researchers have proposed to further enhance the utility of drones by deploying them with ground vehicles (e.g., trucks) in tandem. This new concept leads to a new transportation planning problem, i.e., coordinated delivery of trucks and drones (CDTD). In this paper, we conduct a comparative study of three representative operational models: the flying sidekick traveling salesman problem (FSTSP), the traveling salesman problem with drone (TSP-D), and the parallel drone scheduling traveling salesman problem (PDSTSP). Metrics including delivery efficiency and vehicle utilization rate are evaluated for the three models under various scenarios, to extract insights on which operational model is advantageous for which scenario. We also benchmark the algorithms for CDTD by various metrics including result quality, runtime and scalability, and conduct sensitivity analysis to assess the performance of the system under various system parameters, including drone speed, drone range and customer geographic distribution. Finally, a case study is conducted to demonstrate the superior performance of CDTD model as compared to classical TSP model based on real-world data. Experimental results demonstrate that the FSTSP and TSP-D highlight superior performance over PDSTSP and truck-only TSP when the majority of customers are out of the flight range of drone and the customer demand is clustered, as a result of the synchronization and compensation for limitations between the two types of vehicles.

BM-RCWTSG: An integrated matching framework for electric vehicle ride-hailing services under stochastic guidance

Electric vehicles (EVs) are playing an increasingly vital role in sustainable ride-hailing services, primarily due to their potential to reduce carbon emissions and enhance environmental well-being. A core service of ride-hailing platforms is to match riders with nearby drivers within a short time window. In this mandate, it is crucial to ensure a high matching rate and low rider waiting time since these are key indicators of customer satisfaction and the platform’s operational effectiveness. However, existing matching mechanisms in the literature failed to consider important aspects related to EVs, including the range limit, charging detour time, and charging waiting time. These factors can significantly affect the system matching rate, rider waiting time, and vehicle utilization rate. To address the new challenges of the increased adoption of EVs in ride-hailing services, we propose a data-driven stochastic optimization framework for EV ride-hailing matching. The proposed framework integrates two stochastic optimization models, namely the stochastic guidance (SG) model and the EV matching (EVM) model. The SG model optimizes EV relocation and improves the system matching rate by proactively guiding idle EVs to the designated regions based on probabilistic rider demand forecasting. The EVM model utilizes the relocated EVs and current rider demands to provide the optimal matching solution that minimizes the overall cost associated with rider waiting time, EV charging waiting time, and charging detours. We conduct experimental validation of this approach using two real-world data sets from New York City. Comparative results suggest that by utilizing the proposed framework, the matching rate can be increased up to 22.9%, and the rider and EV waiting times can be reduced up to 14.9% and 73.6%, respectively, compared to the benchmark models.

Carsharing in China: Impact of system and urban factors on usage and efficiency

The carsharing service has experienced significant growth over the past few years in China, yet few studies have scrutinized the multi-city variations of this service. Using carsharing data from 61 cities in China, we analyzed the usage and efficiency of each city and investigated the impact of system and urban factors on the service performance. The study reveals vast differences in carsharing supply and demand across Chinese cities. Our results show the parking station density and the parking lot to vehicle ratio of the carsharing system are positively related to the usage. Urban factors such as public transportation availability, educational attainment levels, and vehicle restriction policies, are found to have significantly positive associations with the carsharing usage. However, no urban factors demonstrate significant associations with the efficiency measured by vehicle utilization rate. Moreover, the presence of other competing carsharing services within a city exhibits a positive impact on the performance of carsharing systems. This study also examined nonlinear effects of the factors. It provides valuable insights into the management of carsharing services in China, which can inform policy-making and operational strategies for sustainable development of carsharing.

Sustainable vehicle allocation decisions under a vertical logistics collaboration setting

Enhancing vehicle utilization through a collaborative logistics approach allows businesses to contribute to the three pillars of sustainability by reducing fuel consumption and emissions, enhancing profit margins, and increasing customer satisfaction. This paper aims to introduce an Integer Linear Programming model for solving a vehicle allocation problem within a proposed vertical logistics collaboration setting. In this collaboration setup, production capacity allocation decisions are jointly made by collaborating customers and the logistics service provider, in order to create a more balanced and profitable logistics plan for all involved parties. Through the application of the proposed mathematical model, our numerical analyses illustrate the effects of the vertical logistics collaboration on the performance of transportation operations, including improved vehicle utilization rate and increased satisfied demand amount. Besides, we analyze the outputs of this operation related to the economic (by focusing on profit growth), environmental (by measuring the reduction in emissions), and social (by assessing the discounted transportation costs for customers) aspects of sustainability. To the best of our knowledge, no current study in the literature has established the vehicle allocation problem in the context of vertical logistics collaboration across all three sustainability pillars at the same time. The model can also serve as a tool for evaluating the potential economic, environmental, and social benefits arising from vertical collaboration and joint projects with customers, along with the support offered to logistics decisions.

生鲜农产品冷链集货车辆路径与货物配载联合优化研究

As a bridge connecting agricultural production and consumption, the circulation of agricultural products has the function of connecting supply and demand, guiding production and promoting consumption. However, the development of rural logistics in China is slow, and most logistics centers still rely on experience to plan the pick-up vehicle routings, resulting in long transport time and high cost. In order to improve the efficiency of pick-up and reduce transportation costs, a joint optimization model of cold-chain pick-up vehicle routing and cargo allocation for fresh agricultural products was proposed in this study. Soft time window constraint and three-dimensional loading constraints were considered, and the lowest pick-up cost was used as optimization goals in this model. In addition, adaptive large neighborhood search algorithm (ALNS) and heuristic depth-first search algorithm (HDFS) were combined to solve the model. A case study of Kunming International Flower Auction Center was conducted to compare the schemes of pick-up vehicle routing before and after optimization. Results demonstrate that the pick-up cost after optimization decreases by 9.6 %, the number of vehicles decreases by one, the total volume utilization rate of vehicles increases by 23 %, and the total load utilization rate of vehicles increases by 15 %. This study provides a model reference and solution method for enterprise operators to formulate schemes of pick-up vehicle routing quickly and reasonably.

Fleet sizing and charging infrastructure design for electric autonomous mobility-on-demand systems with endogenous congestion and limited link space

Autonomous vehicles are to revolutionize the way urban mobility demands are served, and they are most likely to be powered by electricity. To accurately quantify the benefits of replacing existing mobility services with autonomous electric vehicles, electric autonomous mobility-on-demand (EAMoD) systems need to be evaluated and designed with the congestion effects they may cause taken into account. In this work, the congestion effects are depicted through a discrete event simulation model with state-dependent link travel speed and limited link space that allow endogenous congestion to emerge, spread, and dissipate across the entire road network. Three mathematical models are integrated into the simulation model to optimally match vehicles with waiting requests, relocate empty vehicles to potential high-demand areas, and assign low charge vehicles to charging stations based on the workloads of the charging stations. Based on the simulation model, we apply Bayesian optimization to jointly design the fleet size and charging facility configuration considering the endogenous congestion incurred by the daily operation of autonomous electric vehicles. Contraction hierarchies are adopted to route vehicles to perform assigned tasks in real-time. The proposed solution method is tested on Manhattan below 60th street, which corresponds to the potential congestion pricing zone in New York City. Experiment results show that the proposed simulation-based optimization approach is computationally tractable, and can find a satisfactory solution to the fleet sizing and charging infrastructure design problem within a tight computation budget. Excluding congestion effects at system design stage would lead to up to 14% passenger loss due to long assignment waiting time. Although the charging facility cost accounts for only 1.8% of the total cost, the number of chargers can indirectly affect the percentage of passengers that can be served through its efficiency in recharging vehicles. A charging facility configuration aligned with the fleet size can help to improve service quality and vehicle utilization rate.

Vehicle Rebalancing in a Shared Micromobility System with Rider Crowdsourcing

Problem definition: Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Because customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results: We construct a time-space network with multiple service regions and formulate a two-stage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the operator decides the initial vehicle allocation for the regions, whereas in the second stage, the operator determines subsequent vehicle relocation across the regions over an operational horizon. We develop an efficient solution approach that incorporates scenario-based and time-based decomposition techniques. Our approach outperforms a commercial solver in solution quality and computational time for solving large-scale problem instances based on real data. Managerial implications: The budgets for acquiring vehicles and for rider crowdsourcing significantly impact the vehicle initial allocation and subsequent relocation. Introducing rider crowdsourcing in addition to the 3PL can significantly increase profit, reduce demand loss, and improve the vehicle utilization rate of the system without affecting any existing commitment with the 3PL. The 3PL is more efficient for mass relocation than rider crowdsourcing, whereas the latter is more efficient in handling sporadic relocation needs. To serve a region, the 3PL often relocates vehicles in batches from faraway, low-demand regions around peak hours of a day, whereas rider crowdsourcing relocates a few vehicles each time from neighboring regions throughout the day. Furthermore, rider crowdsourcing relocates more vehicles under a unimodal customer arrival pattern than a bimodal pattern, whereas the reverse holds for the 3PL. Funding: This work was supported by the Research Grants Council of Hong Kong [Grants 15501319 and 15505318] and the National Natural Science Foundation of China [Grant 71931009]. Z. Jin was supported by the Hong Kong PhD Fellowship Scheme. Y. F. Lim was supported by the Lee Kong Chian School of Business, Singapore Management University [Maritime and Port Authority Research Fellowship]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1199 .

Optimization of Truck–Loader Matching Based on a Simulation Method for Underground Mines

The choice of transportation system has an important impact on the production efficiency and economic behavior of underground mines. Trackless vehicle transportation has gradually become the main method in underground mines because mining companies have realized that mining efficiency can be improved using advanced vehicle mechanization and automation in the mining process. The extracted ore is loaded onto trucks by loaders in situ, then the trucks drive to ore passes for unloading Trucks load and unload ore in a cyclical manner between stopes and ore passes. Numerous trucks drive in tunnels simultaneously to achieve production targets, and there are interactions and influences among trucks, such as blocking and queuing, due to limited underground space. To address this issue, a transportation route model was built, and the ore transportation process was divided into three parts, including ore loading, truck transportation, and ore unloading. The simulation method was applied to optimize the number of loaders and trucks under the constraints of stope production capacity, transportation route and capacity, and vehicle capacity, to achieve the optimal vehicle utilization rate and transportation capability. The Monte Carlo simulation method was utilized to take the uncertainties of the transportation parameters into account to improve the robustness of the simulation results. The model was verified using the case study of an underground gold mine located in Shandong Province, China, with the objective of accomplishing optimal truck–loader matching considering various stopes in a mining area.

Vehicle Dispatch and Route Optimization Algorithm for Demand-Responsive Transit

Giving priority to the development of public transit is an important way to achieve efficient, convenient, safe, comfortable, economic, reliable, green and low-carbon sustainable development. In view of the highly dispersed and regular passenger flow, demand responsive transit is an important complementary means for traditional public transport to improve passenger satisfaction. However, high operating costs and low load factor will have a bad impact on the operation of public transport and reduce passenger satisfaction. In this work, firstly, by analyzing the demand frequency of historical travel stations, the stations with high demand are extracted by time periods as high probability travel points; On this basis, a dynamic vehicle dispatching optimization model is established, and the static vehicle dispatching is carried out with the goal of minimizing the running mileage of the bus system; Finally, based on the initial static route and the later real-time travel demand, the accurate dynamic planning algorithm is used to optimize the dynamic route with the goal of minimizing the change of the system mileage, so as to achieve timely response to the demand. The results show that the two-phase scheduling optimization model based on the station extraction strategy can provide a reasonable real-time vehicle scheduling and route optimization scheme, improve the utilization rate of vehicles and the passenger load factor, and provide a theoretical basis and application guidance for actual vehicle scheduling.

A multiple leaders particle swarm optimization algorithm with variable neighborhood search for multiobjective fixed crowd carpooling problem

Carpooling is a shared travel mode that can increase vehicle utilization and ease urban traffic pressure. Carpooling in a fixed group of people can not only increase the utilization rate of vehicles, but also provide better interpersonal relationships. This paper studies a multiobjective carpooling problem for people working in the same industrial park. The optimization objectives include minimizing the total mileage of vehicles, the total mileage of employees, and the extra time consumed by employees. A mathematical model was established, and a sequence code was designed. A novel type of multiple leaders particle swarm optimization algorithm MPSO-VNS combined with variable neighborhood search is proposed. The leaders are selected from the optimal solution set of particle motion according to the direction distance index. Experiments show that the MPSO-VNS algorithm can effectively solve the multiobjective carpooling problem. Compared with the six algorithms of NSGA-Ⅱ, MOEA/D, PSO, MaPSO, VNS, and Two-Level VNS, MPSO-VNS can obtain a better non-dominated solution set and also has good computational efficiency.

Study on the optimization of urban emergency supplies distribution paths for epidemic outbreaks

The humanitarian logistics vehicle routing problem (VRP) of urban emergency supplies in response to epidemic outbreaks is studied in this work to address the adverse effects of epidemic outbreaks on human society. Under the premise that the greater the urgency of the demand for various emergency supplies by the hospitals in the city’s jurisdiction, the higher the priority of distribution, the fitness function of the highest vehicle utilization rate and the lowest transportation cost is established. The aim of the research is to construct a multi-objective mathematical model of emergency supplies distribution for large cities in case of an epidemic outbreak. Based on the multi-verse optimization algorithm and the differential evolution algorithm, a hybrid multi-verse optimizer algorithm based on differential evolution (DE-IMOV) is proposed. A simulation experiment is then carried out to verify the model. The calculation results show that the developed algorithm can effectively reduce the distribution cost and improve the full load rate of vehicles on the basis of considering the urgency of emergency supplies demand, which provides a solution for the optimization of heterogeneous vehicle distribution paths for urban emergency supplies in response to epidemic outbreaks.

Optimization of Apron Support Vehicle Operation Scheduling Based on Multi-Layer Coding Genetic Algorithm

Operation scheduling of apron support vehicles is an important factor affecting aircraft support capability. However, at present, the traditional support methods have the problems of low utilization rate of support vehicles and low support efficiency in multi-aircraft support. In this paper, a vehicle scheduling model is constructed, and a multi-layer coding genetic algorithm is designed to solve the vehicle scheduling problem. In this paper, the apron support vehicle operation scheduling problem is regarded as a Resource-Constrained Project Scheduling Problem (RCPSP), and the support vehicles and their support procedures are adjusted via the sequential sorting method to achieve the optimization goals of shortening the support time and improving the vehicle utilization rate. Based on a specific example, the job scheduling before and after the optimization of the number of support vehicles is simulated using a multi-layer coding genetic algorithm. The results show that compared with the traditional support scheme, the vehicle scheduling time optimized via the multi-layer coding genetic algorithm is obviously shortened; after the number of vehicles is optimized, the support time is further shortened and the average utilization rate of vehicles is improved. Finally, the optimized apron support vehicle number configuration and the best scheduling scheme are given.

Freight Fleet Management Problem: Evaluation of a Truck Utilization Rate Based on Agent Modeling

The article deals with the problem of estimating the rational number and utilization rate of the vehicles' fleet. According to the analysis results of the state-of-the-art literature it has been revealed that the issue of substantiating the rational fleet size and the rate of its utilization were not fully solved. The purpose of the study was to increase the efficiency of servicing transportation orders by determining the required number of vehicles. The goal of the research was the influence of the transportation process parameters on the truck utilization rate. Originating from the probabilistic nature of the transportation process, it has been proposed to use the AnyLogic software product to develop a simulation model for vehicle orders' servicing. From the processing of the experimental results by the regression analysis methods, it has been found that the dependence of changes in the vehicle utilization rate is of a linear form.

A Simulation-optimization framework of vehicle relocation for one-way electric carsharing systems

In one-way electric carsharing systems (ECSs), customers can pick up an electric vehicle (EV) at one location and return it to any location, causing an imbalance of EVs and revenue loss for ECSs. In order to address this problem, a simulation-optimization framework is introduced to determine optimal vehicle relocation strategies. In this framework, the discrete event simulation model considers stochasticity and EV-dependent road congestion to simulate the operation of ECSs. And the optimization module is triggered by the simulation model to provide relocation strategies to the operator based on the genetic algorithm with the optimal computing budget allocation. The results of numerical experiments demonstrate that the simulation model can reflect the operation of ECSs in the real world, and the joint relocation strategies achieve improvements in both the profit and the vehicle utilization rate and reduce the number of relocations concerning the single relocation strategies used in the ECS.

Data-driven analysis on matching probability, routing distance and detour distance in ride-pooling services

By serving two or more passenger requests in each ride in ride-sourcing markets, ride-pooling service is now becoming an important component of shared smart mobility. It is generally expected to improve vehicle utilization rate, and therefore alleviate traffic congestion and reduce carbon dioxide emissions. A few recent theoretical studies are conducted, mainly focusing on the equilibrium analysis of the ride-sourcing markets with ride-pooling services and the impacts of ride-pooling services on transit ridership and traffic congestion. In these studies, there are three key measures that distinguish ride-pooling service analysis from the non-pooling ride-sourcing market analysis. The first is the proportion of passengers who are pool-matched(referred to as pool-matching probability), the second is passengers’ average detour distance, and the third is average vehicle routing distance to pick up and drop off all passengers with different origins and destinations in one specific ride. These three measures are determined by passenger demand for ride-pooling and matching strategies. However, due to the complex nature of ride-resourcing market, it is difficult to analytically determine the relationships between these measures and passenger demand. To fill this research gap, this paper attempts to empirically ascertain these relationships through extensive experiments based on the actual on-demand mobility data obtained from Chengdu, Haikou, and Manhattan. We are surprised to find that the relationships between the three measures (pool-matching probability, passengers’ average detour distance, average vehicle routing distance) and number of passengers in the matching pool (which reflects passenger demand) can be fitted by some simple curves (with fairly high goodness-of-fit) or there exist elegant empirical laws on these relationships. Our findings are insightful and useful to theoretical modeling and applications in ride-resourcing markets, such as evaluation of the impacts of ride-pooling on transit usage and traffic congestion.

Provisions of the marginal utility theory in the solution of bus servicing issues in the Arctic zone of Russia

Improving the effectiveness and quality of maintenance and repair of road vehicles is one of the primary tasks in the Arctic zone of Russia. The paper analyzes international experience in solving these problems. It provides a description of a system ensuring the operability of buses with a division into subsystems. The marginal utility theory in conjunction with a system of maintenance and repair of motor vehicles is presented. The following relationships are addressed: the fleet utilization coefficient vs. the relative cost of production and technical facilities, the technical readiness coefficient of vehicles vs. the relative cost of production and technical facilities, the specific capital investments in production and technical facilities vs. the scale of the enterprise, the technical readiness coefficient and the consumption of spare parts vs. the mechanization level. In the course of the study, we considered the structure of the operational and repair cycle of vehicles. Besides, we compared the effectiveness of different options for improving the production and technical facilities of bus servicing. It was proved that one of the most important effectiveness indicators (vehicle utilization rate) reaches the maximum level with a rational combination of servicing centralization, specialization, and cooperation.

CNN-GRU-AM for Shared Bicycles Demand Forecasting.

The demand forecast of shared bicycles directly determines the utilization rate of vehicles and projects operation benefits. Accurate prediction based on the existing operating data can reduce unnecessary delivery. Since the use of shared bicycles is susceptible to time dependence and external factors, most of the existing works only consider some of the attributes of shared bicycles, resulting in insufficient modeling and unsatisfactory prediction performance. In order to address the aforementioned limitations, this paper establishes a novelty prediction model based on convolutional recurrent neural network with the attention mechanism named as CNN-GRU-AM. There are four parts in the proposed CNN-GRU-AM model. First, a convolutional neural network (CNN) with two layers is used to extract local features from the multiple sources data. Second, the gated recurrent unit (GRU) is employed to capture the time-series relationships of the output data of CNN. Third, the attention mechanism (AM) is introduced to mining the potential relationships of the series features, in which different weights will be assigned to the corresponding features according to their importance. At last, a fully connected layer with three layers is added to learn features and output the prediction results. To evaluate the performance of the proposed method, we conducted massive experiments on two datasets including a real mobile bicycle data and a public shared bicycle data. The experimental results show that the prediction performance of the proposed model is better than other prediction models, indicating the significance of the social benefits.

An Algorithm to Solve Heterogeneous Vehicle Routing Problem With Second Trip

The realistic logistics not only involves vehicle capacity and working hours, but also factors such as the second trip of vehicles and heterogeneous vehicles. Aiming at the constraints of these VRP variants, an algorithm is proposed to solve the heterogeneous vehicle routing problem with a second trip. In the process of calculating the initial solution, the concept of k-ring shaped Voronoi neighbors reduces the computational complexity and avoids the problems caused by outliers. When allocating customers, the idea of “Borrow-In and Borrow-Out” proposed in this article is adopted to rationalize the route planning results. In the calculation process of considering the second trip, the customers closer to the depot are regarded as the final order. For heterogeneous vehicles, this article adopts a greedy strategy. The experimental results of multiple instances show that if the depot provides a heterogeneous fixed vehicles fleet, compared to when only a single vehicle type is provided, the result is a reduced number of vehicles used by 4.46% on average, and the loading utilization rate of vehicles is increased by 4.02% on average. And after the calculation of considering the second trip, the number of vehicles used is reduced by 5.09% on average, and the loading utilization rate of vehicles is increased by 4.41% on average. It can be seen that the algorithm proposed in this article can obtain high-quality route allocation results for large-scale orders.

Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand

With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers. Without macroguidance, the role of the taxi system cannot be fully utilized. Many scholars have studied taxi behaviors to find better operational strategies for drivers, but their researches rely on local optimization methods to improve the profit of drivers, which will lead to imbalance between supply and demand in the city. To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis. Different from other methods of scheduling taxis based on the real-time location of orders, our model first predicts the demand for taxis in different regions in the next period and then dispatches taxis in advance to meet the future requirement; thus, the number of taxis needed and available in different regions can be balanced. Besides, in order to reduce computational complexity, we propose several methods to reduce the state space and action space of reinforcement learning. Finally, we compare our method with another taxi dispatching method, and the results show that the proposed method has a significant improvement in vehicle utilization rate and passenger demand satisfaction rate.

Electric vehicle car-sharing optimization relocation model combining user relocation and staff relocation

ABSTRACT Factors including unbalanced user travel demand, vehicle charging status, and high operating cost of enterprises have restricted the development of electric vehicle car-sharing. This paper comprehensively considers the impact of multiple dynamic constraints such as user demand, state of charge of electric vehicles, and operating profit on vehicle relocation. The travel behavior of consumers is studied through Multinomial Logistic Regression method. User demand for electric vehicle car-sharing is forecast through Hidden Markov Model. A new vehicle relocation strategy combining staff relocation and user relocation is formulated. With the goal of maximizing enterprise profit, an electric vehicle car-sharing optimization relocation model in region level is finally established. Taking Anting Town as an example to verify the model, the results show that this new vehicle relocation strategy can effectively reduce the operating cost of enterprises, improve the circulation rate and utilization rate of vehicles, and reduce unnecessary waste of resources.