Joint Routing Research Articles

Joint optimization of truck-drone routing for last-mile deliveries in urban areas

This paper presents a joint routing optimization model to satisfy the on-demand customer requirements by minimising the transportation and time penalty costs of trucks and drones, considering the constraints on paths and delivery time of trucks and drones, the drone battery capacity limit, and the customer demands. The improved genetic algorithm is proposed to solve the optimization model followed by the computational experiments demonstrating the superiority of the improved genetic algorithm over the original genetic algorithm in computational efficiency, economy, and punctuality. Further, the comparative analysis demonstrates that the multi-drone joint delivery mode considering the customer demands performs better than the other delivery modes and that the delivery routes can be re-optimised promptly to satisfy the real-time customer demands. The sensitivity analysis on the drone design parameters provides theoretical insights into deploying the size and speed of drone platoons when promoting truck-drone joint delivery in applications.

Optimization of multi-vehicle charging and discharging efficiency under time constraints based on reinforcement learning

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS.

A memetic algorithm for improved joint route selection and split-level management in next-generation wireless communications

The complexity of next-generation wireless communications, especially Beyond 5G and 6G communication systems, will be handled by artificial intelligence-based management paradigms. The joint selection of routes and functional split levels involves critical decisions that network infrastructure providers need to make to support requests from virtual Mobile Network Operators (vMNOs). These decisions comprise the assignment and configuration of physical network resources, which must comply with the specific quality of service restrictions of each vMNO request. Recent work defined a detailed mathematical model for this complex challenge, its formulation as a constrained, discrete optimization problem, and the first algorithmic approaches. It was also found that an evolutionary algorithm delivers higher-quality solutions than an ad-hoc heuristic, and faster running times compared to a well-known commercial solver. This paper introduces a memetic algorithm that exploits the strengths of the former evolutionary method while incorporating several key innovations: a domain-specific recombination operator; a specialized repairing procedure; an enhanced fitness evaluation scheme; and a multiobjective archiving strategy that preserves promising solution trade-offs. We conduct a comprehensive evaluation of the performance and behavior of this proposal, as well as the contribution of each specific design component. The results highlight that our memetic algorithm consistently outperforms previous approaches from the literature, providing better trade-offs in terms of solution quality and the rate at which vMNO requests are successfully fulfilled.

Disease-modifying rdHSV-CA8* non-opioid analgesic gene therapy treats chronic osteoarthritis pain by activating Kv7 voltage-gated potassium channels.

Chronic pain is common in our population, and most of these patients are inadequately treated, making the development of safer analgesics a high priority. Knee osteoarthritis (OA) is a primary cause of chronic pain and disability worldwide, and lower extremity OA is a major contributor to loss of quality-adjusted life-years. In this study we tested the hypothesis that a novel JDNI8 replication-defective herpes simplex-1 viral vector (rdHSV) incorporating a modified carbonic anhydrase-8 transgene (CA8*) produces analgesia and treats monoiodoacetate-induced (MIA) chronic knee pain due to OA. We observed transduction of lumbar DRG sensory neurons with these viral constructs (vHCA8*) (~40% of advillin-positive cells and ~ 50% of TrkA-positive cells colocalized with V5-positive cells) using the intra-articular (IA) knee joint (KJ) route of administration. vHCA8* inhibited chronic mechanical OA knee pain induced by MIA was dose- and time-dependent. Mechanical thresholds returned to Baseline by D17 after IA KJ vHCA8* treatment, and exceeded Baseline (analgesia) through D65, whereas negative controls failed to reach Baseline responses. Weight-bearing and automated voluntary wheel running were improved by vHCA8*, but not negative controls. Kv7 voltage-gated potassium channel-specific inhibitor XE-991 reversed vHCA8*-induced analgesia. Using IHC, IA KJ of vHCA8* activated DRG Kv7 channels via dephosphorylation, but negative controls failed to impact Kv7 channels. XE-991 stimulated Kv7.2-7.5 and Kv7.3 phosphorylation using western blotting of differentiated SH-SY5Y cells, which was inhibited by vHCA8* but not by negative controls. The observed prolonged dose-dependent therapeutic effects of IA KJ administration of vHCA8* on MIA-induced chronic KJ pain due to OA is consistent with the specific activation of Kv7 channels in small DRG sensory neurons. Together, these data demonstrate for the first-time local IA KJ administration of vHCA8* produces opioid-independent analgesia in this MIA-induced OA chronic pain model, supporting further therapeutic development.

DSJA: Distributed Server-Driven Joint Route Scheduling and Streaming Adaptation for Multi-Party Realtime Video Streaming

DSJA: Distributed Server-Driven Joint Route Scheduling and Streaming Adaptation for Multi-Party Realtime Video Streaming

Joint Routing and Pricing Control in Bimodal Mixed Autonomy Networks with Elastic Demand and Three-Dimensional Passenger Macroscopic Fundamental Diagram

The interaction between mixed autonomy traffic and public transport vehicles competing for limited road space is a less explored area of research. To evaluate the traffic dynamics in bimodal networks, a three-dimensional (passenger) network fundamental diagram, known as 3D-NFD (3D-pNFD), can be estimated that relates the accumulation of cars (autonomous and human-driven) and buses to the network vehicular (passenger) travel production. In this study, we propose a 3D-pNFD-based congestion pricing scheme considering three vehicle classes of buses, system-optimal (SO) seeking connected and automated vehicles (CAVs), and user-equilibrium (UE) seeking human-driven vehicles (HVs). We develop an iterative tri-level modeling framework for mode choice, pricing, and route choice in a mixed autonomy network. The lower level generates mixed equilibrium traffic flow through an integrated mixed equilibrium simulation-based dynamic traffic assignment model and a transit assignment model. The mid-level finds the optimal toll rates through a 3D-pNFD feedback-based controller. A nested logit-based mode choice model is also applied to capture travelers’ preferences toward three available modes and incorporates elastic demand. To encourage CAVs to follow the SO routing mode, they are provided with a discount on the congestion toll whereas UE-seeking HVs are subject to full price toll. Buses are also entirely exempt from the toll. We explore three scenarios with different discount rates on SO-seeking CAVs to investigate the effect of the incentive plans on the mode choice behaviors of road users in the pricing zone. The performance of the proposed model is evaluated in a large-scale network in Melbourne, Australia.

Joint routing and computation offloading based deep reinforcement learning for Flying Ad hoc Networks

Flying ad-hoc networks (FANETs) consisting of multiple Unmanned Aerial Vehicles (UAVs) are widely used due to their flexibility and low cost. In scenarios such as crowdsensing and data collection, data collected by UAVs are transmitted to base stations for processing and then sent to data centers. Still, the deployment of base stations is costly and inflexible. To address this issue, this paper introduces a position-based Computing First Routing (CFR) protocol designed for efficient task transmission and computation offloading in FANETs. This protocol facilitates task processing during data transfer and ensures the delivery of fully processed results to the data center. Considering the dynamically changing topology of the FANETs and the uneven distribution of the UAVs’ computation power, deep reinforcement learning is used to make multi-objective decisions based on the Q-values computed by the model. FANETs are centerless clusters, and two-hop neighbor tables containing position and computing power information are used to make less costly decisions. Simulation experiments demonstrate that CFR outperforms other benchmark schemes with an approximately 6% higher packet delivery rate, an approximately 21% reduction in end-to-end delay, and about a 34% decrease in total cost. Furthermore, it effectively ensures the completion of task offloading before reaching the destination node. This occurs due to the design of a hierarchical reward function that takes into account dynamic changes in delay and energy consumption, as well as the injection of neighbor computing power information into the two-hop neighbor table.

Joint Routing and Charging Optimization of Electric Passenger Vehicles With Uninterruptible Charging Service

Joint Routing and Charging Optimization of Electric Passenger Vehicles With Uninterruptible Charging Service

A Multipolicy Deep Reinforcement Learning Approach for Multiobjective Joint Routing and Scheduling in Deterministic Networks

A Multipolicy Deep Reinforcement Learning Approach for Multiobjective Joint Routing and Scheduling in Deterministic Networks

Dynamic stream partitioning for time-triggered traffic in Time-Sensitive Networking

Time-Sensitive Networking (TSN) is a promising network technology that can ensure bounded latency and jitter for industrial real-time scenarios. It establishes the IEEE 802.1 Qbv standard to precisely control the periodic transmission time of the time-triggered (TT) traffic. However, it is still a challenge to efficiently schedule the TT traffic in large-scale networks with high transmission performance. In this paper, we propose a novel method called dynamic stream partitioning (DSP) to solve this problem. It satisfies the IEEE 802.1 Qbv standard and can significantly reduce the scheduling time by reducing the number of constraints. Based on the DSP method, we analyze the impact of stream partitioning on the transmission performance of the TT traffic, and propose an indicator called Normalized Degradation of Performance (NDOP) to quantify it dynamically. Furthermore, we design a partitioning-aware dynamic routing (PADR) to expand the scheduling space. Integrated with NDOP and PADR, we propose a joint routing and dynamic stream partitioning (JR/DSP) algorithm. Extensive simulation experiments verify that the proposed JR/DSP algorithm has higher scalability, transmission performance, and schedulability compared to the static stream partitioning algorithms.

Sustainable optimizing WMN performance through meta-heuristic TDMA link scheduling and routing

Wireless mesh networks (WMNs) have become a popular solution for expanding internet service and communication in both urban and rural areas. However, the performance of WMNs depends on generating optimized time-division multiple access (TDMA) schedules, which distribute time into a list of slots called superframes. This study proposes novel meta-heuristic algorithms to generate TDMA link schedules in WMNs using two different interference/constraint models: multi-transmit-receive (MTR) and full-duplex (FD). The objectives of this study are to optimize the TDMA frame for packet transmission, satisfy the constraints, and minimize the end-to-end delay. The significant contributions of this study are: (1) proposing effective and efficient heuristic solutions to solve the NP-complete problem of generating optimal TDMA link schedules in WMNs; (2) investigating the new FD interference model to improve the network capacity above the physical layer. To achieve these objectives and contributions, the study uses two popular meta-heuristics, the artificial bee colony (ABC) and/or genetic algorithm (GA), to solve the known NP-complete problems of joint scheduling, power control, and rate control. The results of this study show that the proposed algorithms can generate optimized TDMA link schedules for both MTR and FD models. The joint routing and scheduling approach further minimizes end-to-end delay while maintaining the schedule's minimum length and/or maximum capacity. The proposed solution outperforms the existing solutions in terms of the number of active links, end-to-end delay, and network capacity. The research aims to improve the efficiency and effectiveness of WMNs in most applications that require high throughput and fast response time.

Joint Routing and Scheduling Optimization of In-Vehicle Time-Sensitive Networks Based on Improved Grey Wolf Optimizer

Joint Routing and Scheduling Optimization of In-Vehicle Time-Sensitive Networks Based on Improved Grey Wolf Optimizer

Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

Deep Reinforcement Learning-based Joint Routing and Capacity Optimization in an Aerial and Terrestrial Hybrid Wireless Network

Deep Reinforcement Learning-based Joint Routing and Capacity Optimization in an Aerial and Terrestrial Hybrid Wireless Network

Energy-aware Joint Route Selection and Resource Allocation in Heterogeneous Satellite Networks

Energy-aware Joint Route Selection and Resource Allocation in Heterogeneous Satellite Networks

Optimal routing and heterogeneous resource allocation for computing-aware networks

Computing-aware networking (CAN) is introduced to unite the computing resources distributed in different platforms. This paper proposes a joint routing and resource allocation model to minimize the total operational cost in CAN. We formulate the problem as an integer linear programming problem. We introduce a polynomial-time algorithm for larger-size problems. The numerical results reveal that the introduced algorithm reduces the computation time 9.18 times, with increasing the objective no more than 4% compared to the optimal solution; the proposed model can reduce 70% of the total cost compared to a baseline adopting a two-stage strategy in our examined cases.

МЕТОДИКА ПРОЕКТИРОВАНИЯ КОЛЬЦЕВЫХ МАРШРУТОВ С ОБРАТНЫМ ГРУЗОМ

The method of forming the parameters of a joint ring route with a return loaded mileage is presented. The numerical example is calculated by the method of fictitious nodes and branches. The route length is used as the objective function. The distance and time benefit function is calculated when organizing a joint route with a return cargo.

Improved Heuristic Joint Routing and Scheduling in Time-Sensitive Networking

Improved Heuristic Joint Routing and Scheduling in Time-Sensitive Networking

Reuse-based online joint routing and scheduling optimization mechanism in deterministic networks

Deterministic networks plan the entire network traffic and calculate the scheduling time to meet the critical traffic requirements of specific domains, enabling real-time and deterministic interaction of massive data. However, in dynamic industrial automation scenarios where devices undergo changes, existing mechanisms face challenges in quickly responding to dynamic transmission demand changes caused by rapid traffic migration. To address this issue, this paper proposes a reuse-based online scheduling mechanism that utilizes dynamic path planning of flows and coordinated scheduling of time slots to achieve deterministic transmission of dynamic flows. In the offline phase, the mechanism proposes a backbone link selection and a scalable intelligent routing strategy, constructs a set of routing and scheduling co-design constraints, and generates an offline scheduling table using an iterative scheduling algorithm. In the online scheduling phase, a reuse-based online scheduling algorithm is proposed to achieve rapid scheduling and deterministic transmission of dynamic real-time flows. It utilizes the offline scheduling results and the period offset of migrated flows. The reuse of offline scheduling results reduces computation time and expands the solution space. Experimental results demonstrate that the proposed mechanism achieves a maximum increase in scheduling success rate of 37.3% and reduces time costs by up to 66.6% compared to existing online scheduling algorithms.

Multi-agent reinforcement learning for network routing in integrated access backhaul networks

In this study, we examine the problem of downlink wireless routing in integrated access backhaul (IAB) networks involving fiber-connected base stations, wireless base stations, and multiple users. Physical constraints prevent the use of a central controller, leaving base stations with limited access to real-time network conditions. These networks operate in a time-slotted regime, where base stations monitor network conditions and forward packets accordingly. Our objective is to maximize the arrival ratio of packets, while simultaneously minimizing their latency. To accomplish this, we formulate this problem as a multi-agent partially observed Markov Decision Process (POMDP). Moreover, we develop an algorithm that uses Multi-Agent Reinforcement Learning (MARL) combined with Advantage Actor Critic (A2C) to derive a joint routing policy on a distributed basis. Due to the importance of packet destinations for successful routing decisions, we utilize information about similar destinations as a basis for selecting specific-destination routing decisions. For portraying the similarity between those destinations, we rely on their relational base-station associations, i.e., which base station they are currently connected to. Therefore, the algorithm is referred to as Relational Advantage Actor Critic (Relational A2C). To the best of our knowledge, this is the first work that optimizes routing strategy for IAB networks. Further, we present three types of training paradigms for this algorithm in order to provide flexibility in terms of its performance and throughput. Through numerical experiments with different network scenarios, Relational A2C algorithms were demonstrated to be capable of achieving near-centralized performance even though they operate in a decentralized manner in the network of interest. Based on the results of those experiments, we compare Relational A2C to other reinforcement learning algorithms, like Q-Routing and Hybrid Routing. This comparison illustrates that solving the joint optimization problem increases network efficiency and reduces selfish agent behavior.