Vehicle Tours Research Articles

A behavioral micro simulation case study: Distribution center location

The potential negative externalities that can be produced by urban freight transportation make it critical to investigate the impact of urban freight and land use-related decisions on energy efficiency, congestion, and emissions. To advance such investigation, the authors developed a Behavioral Microsimulation Software (BMS) that simulates freight vehicle tours generated in a study area. At the most general level, the BMS provides aggregate performance metrics. At the detailed level, it outputs the freight vehicle tours and their corresponding characteristics. This study sheds light on the impact of the location of logistic facilities on the delivery operation to businesses in metropolitan areas. Thus, the goal of this research is to quantify the impacts of three scenarios in a metropolitan area: (i) location of a new distribution center in the outskirts, (ii) location of a new distribution center in the core, (iii) relocation of an existing distribution center from the outskirts to the core. The impacts discussed are vehicle miles traveled, deliveries per tour, and the number of tours. The authors developed a case study on the New York State Capital Region (USA). The results indicate a reduction in total vehicle miles traveled when locating distribution centers closer to the area's urban core, where demand is concentrated, compared to when located in the outskirts.

Enhancing dealership management software with AI integration for improved customer service and future innovations

This review paper explores the integration of Artificial Intelligence (AI) into dealership management software to enhance customer service and foster future innovations. The primary objective is to synthesize existing research and industry practices to understand how AI technologies can streamline operations, personalize customer interactions, and drive business growth within automotive dealerships. The paper reviews key AI applications, including AI-driven chatbots and virtual assistants that provide automated support, predictive analytics for customer behavior and sales forecasting, and personalized marketing strategies. The analysis reveals that AI significantly improves customer service efficiency by reducing response times and enhancing customer satisfaction. Predictive maintenance and inventory management powered by AI algorithms ensure optimal resource allocation, minimizing downtime and operational costs. Additionally, the paper identifies potential future innovations facilitated by AI advancements, such as augmented reality (AR) for virtual vehicle tours and blockchain for secure transaction management. These innovations promise to elevate the customer experience and streamline dealership processes, creating a competitive edge in the market. The integration of AI into dealership management software presents substantial benefits in improving customer service and operational efficiency. The paper emphasizes the need for continuous investment in AI technologies and employee training to fully harness these advantages. Future research should focus on exploring emerging AI applications and their long-term impact on the automotive industry, ensuring dealerships remain adaptable and competitive in a rapidly evolving technological landscape. Keywords: Artificial Intelligence, Dealership Management Software, Customer Service, Predictive Analytics, Augmented Reality, Blockchain, Automotive Industry.

Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning

Multi-depot vehicle routing problem with time windows (MDVRPTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive problems instantly. Thus, this article presents a novel reinforcement learning algorithm integrated with a multi-head attention mechanism and a local search strategy to solve the problem efficiently. The routing optimization was regarded as a vehicle tour generation process and an encoder-decoder was used to generate routes for vehicles departing from different depots iteratively. A multi-head attention strategy was employed for mining complex spatiotemporal correlations within time windows in the encoder. Then, a decoder with multi-agent was designed to generate solutions by optimizing reward and observing transition state. Meanwhile, a local search strategy was employed to improve the quality of solutions. The experiments results demonstrate that the proposed method can significantly outperform traditional methods in effectiveness and robustness.

Towards Autonomous, Optimal Water Sampling with Aerial and Surface Vehicles for Rapid Water Quality Assessment

Highlights A practical workflow for optimizing sampling tours for a team of surface and aerial vehicles was developed. Proposed workflow considers unique sensing capabilities of surface vehicles when assigning sampling locations. Likely optimal tours can be found in less than 30 s for practical water quality sampling requirements. Abstract. Most current marine aquaculture operations are located in coastal estuarine areas within one mile of the shoreline, and water quality in these production areas can quickly become unfavorable due to hydrodynamic processes and excessive runoff. The deployment of autonomous, robotic systems can improve the speed and spatiotemporal resolution of water sampling and sensing in mariculture production areas to assess water quality in the context of food safety. Specifically, teams of both aerial and surface vehicles can be deployed simultaneously to capitalize on the benefits of each system; however, a method to optimally design a feasible sampling tour for each robot is needed to maximize sample capacity and ensure efficient water sampling missions. This research brief presents the problem formulation and a solution method to determine optimal tours for a team of aquatic surface and aerial vehicles while considering different vehicle sampling capacities and endurance constraints. This method was implemented to design sampling missions of 15, 20, and 30 samples in both a 0.25 km2 and 3.9 km2 site, using sampling capacity and endurance constraints corresponding to real-world robots used for water sampling in mariculture environments. Results indicate that this optimization problem can be solved in near-real time in the field and yields feasible sampling tours for surface and aerial vehicles under different constraints. This work is a practical step towards developing teams of collaborative robots to persistently monitor adverse mariculture growing conditions so producers can implement data-driven, timely management strategies. Keywords: Mariculture, Robotics, Traveling salesperson problem, Vehicle routing.

The p-hub centre routing problem with emissions budget: Formulation and solution procedure

In recent years, the use of fossil fuels has led to a drastic increase in the emissions of CO2 and other greenhouse polluting gases. The transportation sector stands out as one of the main contributors to this pollution. Thus, several network design problems are being revisited to uncover cost and energy-efficient solutions. In this paper, we formulate and solve a p-hub centre routing problem under a CO2 emissions budget. The aim is to locate hub nodes, allocate client nodes in local tours of capacitated vehicles, and decide on vehicle speeds during transportation. The objective is to minimise the maximum time of service subject to a budget constraint on the total CO2 emissions cost of the hub network. To solve the introduced problem, we implement an efficient simulated annealing algorithm with a temperature-dependent penalty cost function, a fixed size prohibited solutions list, and a speed-based reparation heuristic. Additionally, we present a novel clustering-based construction heuristic to generate initial starting solutions for our algorithm, while hub location–allocation, vehicle routing, and speed optimisation operators are considered during the local search step. Extensive computational experiments on adapted AP data set instances show that the proposed solution approach outperforms a state-of-art solver in terms of CPU time and solution quality. Finally, we study the trade-off between the quality of service and the CO2 emission costs and discuss the effect of the CO2 budget on the design of hub networks.

Clustering and routing in waste management: A two-stage optimisation approach

This paper proposes a two-stage model to tackle a problem arising in Waste Management. The decision-maker (a regional authority) is interested in locating sorting facilities in a regional area and defining the corresponding capacities. The decision-maker is aware that waste will be collected and brought to the installed facilities by independent private companies. Therefore, the authority wants to foresee the behaviour of these companies in order to avoid shortsighted decisions. In the first stage, the regional authority divides the clients into clusters, further assigning facilities to these clusters. In the second stage, an effective route is defined to serve client pickup demand. The main idea behind the model is that the authority aims to find the best location–allocation solution by clustering clients and assigning facilities to these clusters without generating overlaps. In doing so, the authority tries to (i) assign the demand of clients to the facilities by considering a safety stock within their capacities to avoid shortages during the operational phase, (ii) minimise Greenhouse Gases emissions, (iii) be as compliant as possible with the solution found by the second stage problem, the latter aiming at optimising vehicle tour lengths. After properly modelling the problem, we propose a matheuristic solution algorithm and conduct extensive computational analysis on a real-case scenario of an Italian region. Validation of the approach is achieved with promising results.

Using Large-Scale Drone Data to Monitor and Assess the Behavior of Freight Vehicles on Urban Level

Compared with public transport operations, urban freight traffic and its associated delivery operations seem to be frequently overlooked in urban traffic management and traffic flow theory. One explanation for this is certainly the lack of available data, as the competitive freight transport market is fragmented and several actors are unwilling to collect or share tactical and operational data. In this study, we use the unique pNEUMA drone data set from Athens, Greece, to shed light on urban freight operations. We discuss macroscopic traffic indicators in a multimodal context. As the vehicle stopping behavior can adversely influence traffic flow, we reveal the stopping behavior of the different modes represented in the data set using clustering techniques. We find that urban freight vehicles’ stopping frequency lies between the stopping frequencies of cars and buses. We reveal the distribution of stopping times for loading and unloading stops in Athens to have a mean of around 380 seconds. Clustering all loading and unloading stops further reveals three groups of loading and unloading stops that could be labeled by incorporating knowledge and expertise about local particularities. The limited flight time of drones, owing to their battery capacities, did not allow reconstruction of longer vehicle routes, such as an entire vehicle tour within the network. However, this could be addressed in future research by realizing continuous large-scale monitoring routines. The revealed vehicle behavior parameters can be used in traffic models to generate further insights into the impacts of urban freight transport to inform public sector decision makers.

Screenline-Based Two-Step Calibration and its Application to an Agent-Based Urban Freight Simulator

Calibration is an essential process in making an agent-based simulator operational. In particular, the calibration for freight demand is challenging because of the model complexity and the shortage of available freight demand data compared with passenger data. This paper proposes a novel calibration method that relies solely on screenline counts, named screenline-based two-step calibration (SLTC). SLTC consists of two parts: (1) tour-based demand adjustment; and (2) model parameter updates. The former generates screenline-based tours by cloning/removing instances of the simulated goods vehicle tours, aiming to minimize the gaps between the observed and the simulated screenline counts. The latter updates the parameters of the commodity flow model which generates inputs to simulate goods vehicle tours. To demonstrate the practicality of the proposed method, we apply it to an agent-based urban freight simulator, SimMobility Freight. The result shows that SLTC allows the simulator to replicate the observed screenline counts with reasonable computational cost for calibration.

The parallel drone scheduling problem with multiple drones and vehicles

Delivery of goods into urban areas constitutes an important issue for logistics service providers. One of the most talked-about developments in recent years has been the potential use of unmanned aerial vehicles, or drones, for transporting packages, food, medicine, and other goods. Delivery by drones offers new possibilities, but also induces new challenging routing problems. In this paper, we address and extend the so-called Parallel Drone Scheduling Traveling Salesman Problem. Basically, in this problem, deliveries are split between a vehicle and one or several drones. The vehicle performs a classical delivery tour from the depot, while the drones are constrained to perform back and forth trips. The objective is to minimize the completion time. We extend the problem by considering several vehicles. We call it Parallel Drone Scheduling Multiple Traveling Salesman Problem. We propose a hybrid metaheuristic for its solution. The procedure starts by building a giant tour visiting all customers. Then, the giant tour is split in order to determine a set of vehicles tours (each vehicle tour following the order defined by the giant tour) and a set of customers assigned to drones. Thirdly, an improvement step move customers between vehicles or between vehicles and drones. We also propose a Mixed Integer Linear Programming formulation and a simple branch-and-cut approach. The proposed approach is validated via an experimental campaign on instances taken from the CVRPLIB11Capacited Vehicle Routing Problem LIBrary library. Computational experiments comparing several variants of the hybrid metaheuristic give some insights on this drone delivery system.

Optimizing the electrification of roads with charge-while-drive technology

Given the current state of battery technology, applying electric trucks for long-haul transportation is a cumbersome task. Either the driving ranges are too short or high-capacity batteries are so heavy that payloads are limited. An old, yet recently revitalized charging infrastructure, currently evaluated on multiple test tracks all around the globe, allows to charge electric trucks while driving. However, installing the charging infrastructure along highways, namely, overhead wiring or road-embedded power lines both accessed by trucks equipped with a movable contact arm, is costly. This paper provides a detailed modeling approach based on continuous variables to minimize the installation costs for electrified highway kilometers while still providing sufficient energy for a given set of representative tours of electric vehicles. We apply our solution approach to show that investment costs can considerably be reduced along a European highway mainline while still allowing electrified transport among most adjacent major cities. Furthermore, we quantify the loss in precision, if instead of our detailed approach a more aggregate model based on discrete decision variables is applied.

Assessing the operational impact of tactical planning models for bike-sharing redistribution

Station-based bike-sharing systems provide users with inexpensive one-way bike rides. A major challenge for operators of BSSs lies in redistributing bikes so that users may take the bike rides they request. Existing research on tactical planning proposes optimization models for designing redistribution plans that a vehicle fleet implements on a daily basis. The purpose of this paper is to identify the value and limitations of stochastic programming for bike-sharing redistribution and to understand the efficacy of the obtained plans once they are implemented. To this end, we first analyze the variability in recorded ride data from three North American bike-sharing systems which mainly differ in the intensity of commuting. The results of the data analysis show that stations that are mainly used by commuters display less variability in demand than stations that are mainly used for other ride purposes like errands and leisure. To assess the effect of demand variability on the operational implementation of redistribution plans, we rely on agent-based simulation. In the simulation, vehicle tours are implemented as planned. However, since redistribution plans are designed based on demand forecasts of ride requests, guidance is needed about how to adjust the bike quantities to pick up from or deliver to each station when actual ride requests are observed. Therefore, we propose rule-based procedures to adjust redistribution decisions when the numbers of bikes at stations and vehicle loads differ from the setting considered by optimization. We show that demand variability is a leading indicator about whether redistribution plans perform well operationally.

Asymmetric Multidepot Vehicle Routing Problems: Valid Inequalities and a Branch-and-Cut Algorithm

In “Asymmetric Multidepot Vehicle Routing Problems: Valid Inequalities and a Branch-and-Cut Algorithm,” Uit het Broek, Schrotenboer, Jargalsaikhan, Roodbergen, and Coelho present a generic branch-and-cut framework to solve routing problems with multiple depots on directed graphs. They present new valid inequalities that eliminate subtours, enforce tours to be linked to the same depot, and enforce bounds on the number of customers in a vehicle tour. This is embedded in a branch-and-cut scheme that also contains generalized and adapted versions of valid inequalities that are well known for related routing problems. The authors show that the new inequalities tighten root node relaxations considerably. In combination with a simple but effective upper-bound procedure, only requiring a MIP solver and a smart reduction of the problem size, the authors show that the overall framework solves instances of considerably larger size to optimality than have been reported in the literature.

ATM cash replenishment under varying population coverage requirements

Inspired by an automated teller machine (ATM) cash replenishment problem involving population coverage requirements (PCRs) in the Netherlands, we propose the vehicle tour problem with minimum coverage requirements. In this problem, a set of minimum-cost routes is constructed subject to constraints on the duration of each route and the population coverage of the replenished ATMs. A compact formulation incorporating a family of valid inequalities and an efficient tour-splitting metaheuristic are proposed and tested on 77 instances derived from real-life data involving up to 98 ATMs and 237,604 citizens and on 144 newly generated synthetic instances. Our results for the real-life instances indicate significant cost differences in replenishing ATMs for seven major Dutch cities when the PCRs vary. Additionally, we illustrate the impact of different PCRs on the ATM replenishment costs for seven major cities in the Netherlands by presenting an aggregated cost evaluation of 11 PCRs involving 1,003,519 citizens, 338 ATMs, and 19 cash distribution vehicles.

Improving Sharing Rates of a Dial-a-Ride Problem implemented for an Austrian Mobility Provider

The Dial-a-Ride Problem (DARP) aims to find a set of minimal cost tours for passenger vehicles in order to satisfy a set of transport requests. Each request requires to pick up one or more passengers at a defined pick-up point and then drop off the passengers at the desired destination. In this work, we consider a DARP that has been implemented for an Austrian mobility provider. The company focuses on rural regions that suffer from insufficient public transportation and offers a sustainable form of mobility. The provider is especially interested in improving the sharing rates of the mobility service. Therefore, we propose a Large Neighborhood Search for solving the respective DARP. In a computational study, we compare different configurations of the service and identify the most promising configurations regarding sharing rates, passenger convenience and, hence, the overall efficiency of the service.

Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach

Multi-vehicle routing problem with soft time windows (MVRPSTW) is an indispensable constituent in urban logistics distribution systems. Over the past decade, numerous methods for MVRPSTW have been proposed, but most are based on heuristic rules that require a large amount of computation time. With the current rapid increase of logistics demands, traditional methods incur the dilemma between computational efficiency and solution quality. To efficiently solve the problem, we propose a novel reinforcement learning algorithm called the Multi-Agent Attention Model that can solve routing problem instantly benefit from lengthy offline training. Specifically, the vehicle routing problem is regarded as a vehicle tour generation process, and an encoder-decoder framework with attention layers is proposed to generate tours of multiple vehicles iteratively. Furthermore, a multi-agent reinforcement learning method with an unsupervised auxiliary network is developed for the model training. By evaluated on four synthetic networks with different scales, the results demonstrate that the proposed method consistently outperforms Google OR-Tools and traditional methods with little computation time. In addition, we validate the robustness of the well-trained model by varying the number of customers and the capacities of vehicles.

Development of freight travel demand model with characteristics of vehicle tour activities

The actual movements of freight vehicles demonstrate comprehensive tour activities instead of a single trip. Freight vehicle tours activity represent trip chaining relationship between shippers, receivers, and carriers as a journey of tours. In India, identifying and modeling tour activity is complex because disaggregated freight tour activity data are generally not available to access. Hence, there is a need to identify the most suitable and sufficient modeling framework, which explains the non-linear behavior and complexity of tour-based freight travel demand. Thus, the objectives of this study are to identify the driver, vehicle, and journey characteristics of freight vehicle movement and their tour activities using the conventional non-linear and soft computing methods to estimate the number of tours and to identify the most suitable and effective modeling method. The questionnaire form designed, and study locations were selected in Tiruchirappalli, India. The selected survey locations were identified based on the availability of fright drivers who were making tours. An extensive face to face data collection process was performed, and a total of 600 drivers' data were collected from survey. A detailed description of the collected data was obtained through descriptive analysis. Next, significant relationship between characteristics of freight vehicle drivers and their tour activities was identified based on various correlation tests. Further, a non-linear regression method was adopted for explaining non-linear behavior and complexity of tour-based freight travel demand. Followed by soft computing models using Support-vector machines (SVM), k-Nearest Neighbors (k-NN), and Artificial Neuron Network (ANN) algorithms were developed to predict tour-based freight travel demand, which performed better than conventional non-linear model. Among all, ANN model outperformed and explained the variations with better accuracy. Finally, the outcome of this study will be helpful to transport planners and researchers for understanding the characteristics of freight vehicle tour activity and estimating tour-based freight travel demand.

Approximation Algorithms for the Min-Max Cycle Cover Problem With Neighborhoods

In this paper we study the min-max cycle cover problem with neighborhoods, which is to find a given number of $K$ cycles to collaboratively visit $n$ Points of Interest (POIs) in a 2D space such that the length of the longest cycle among the $K$ cycles is minimized. The problem arises from many applications, including employing mobile sinks to collect sensor data in wireless sensor networks (WSNs), dispatching charging vehicles to recharge sensors in rechargeable sensor networks, scheduling Unmanned Aerial Vehicles (UAVs) to monitor disaster areas, etc. For example, consider the application of employing multiple mobile sinks to collect sensor data in WSNs. If some mobile sink has a long data collection tour while the other mobile sinks have short tours, this incurs a long data collection latency of the sensors in the long tour. Existing studies assumed that one vehicle needs to move to the location of a POI to serve it. We however assume that the vehicle is able to serve the POI as long as the vehicle is within the neighborhood area of the POI. One such an example is that a mobile sink in a WSN can receive data from a sensor if it is within the transmission range of the sensor (e.g., within 50 meters). It can be seen that the ignorance of neighborhoods will incur a longer traveling length. On the other hand, most existing studies only took into account the vehicle traveling time but ignore the POI service time. Consequently, although the length of some vehicle tour is short, the total amount of time consumed by a vehicle in the tour is prohibitively long, due to many POIs in the tour. In this paper we first study the min-max cycle cover problem with neighborhoods, by incorporating both neighborhoods and POI service time into consideration. We then propose novel approximation algorithms for the problem, by exploring the combinatorial properties of the problem. We finally evaluate the proposed algorithms via experimental simulations. Experimental results show that the proposed algorithms are promising. Especially, the maximum tour times by the proposed algorithms are only about from 80% to 90% of that by existing algorithms.

Electric Vehicle Tour Planning Considering Range Anxiety

In this study, the tour planning problem for electric vehicles is investigated. We aim to derive the optimal route and thus, to maximize profitability and minimize range anxiety within the time horizon. To solve this problem, a bi-objective mixed integer model is proposed. Specifically, we first introduced the reliability of route planning and quantified it as a cost with specific functions. The nonlinear model was then converted into a bi-objective mixed integer linear program, and an interactive branch and bound algorithm was adopted. Numerical experiments conducted on different networks have shown that the model that considers range anxiety offers more effective solutions. This means that our model is able to plan the routes with high reliability and low risk of profit loss and accidents.

Assessing Overnight Parking Infrastructure Policies for Commercial Vehicles in Cities Using Agent-Based Simulation

Urban freight transport is primarily fulfilled by commercial road vehicles. Within cities, overnight parking is a critical element influencing commercial vehicle operations, particularly for heavy vehicles with limited parking options. Providing adequate overnight parking spaces for commercial vehicles tends to be a challenge for urban planners. Inadequate parking supply can result in illegal parking and additional vehicle kilometers traveled, contributing to traffic congestion and air pollution. The lack of tools for evaluating the impacts of changing parking supply is an impediment in developing parking-related solutions that aim to minimize the negative externalities. In this study, we develop an overnight parking choice model for heavy commercial vehicles and integrate it with SimMobility, an agent-based urban simulation platform, demonstrating the potential of this tool for policy evaluation. Using simulations applied to a case study in Singapore, we compare two parking supply scenarios in terms of vehicle kilometers traveled due to changes in the first and last trips of vehicle tours, as well as resulting impacts in traffic flows.

An exact algorithm for the electric-vehicle routing problem with nonlinear charging time

In this paper, we consider the Electric-Vehicle Routing Problem (EVRP) with nonlinear charging time. Due to their limited travel ranges, electric vehicles have to be recharged (possibly multiple times) at specific recharging points, which incurs a routing problem for which the recharging constraint and time have to be addressed. It is well-known that the recharging of the battery of EVs takes considerable time, so it cannot be ignored. Moreover, the recharging time required to travel a given distance is highly nonlinear due to the battery charging mechanism. The goal of this study is to develop an algorithm that minimizes the total travel and charging times without approximation of the charging time function. Our solution approach is based on the segmentation of the vehicle tour. We then construct an extended charging stations network where any path in this network is also a tour in the original network. We develop the branch-and-price method on the extended charging station network to solve the problem to optimality. An extensive computational study on well-known benchmark problems confirms that the proposed approach can solve moderate-sized problems to the optimality.