Optimal 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.

An interaction-enhanced co-evolutionary algorithm for electric vehicle routing optimization

Recently, the electric vehicle routing problem (EVRP) has emerged as a significant challenge in the transportation field. Unlike traditional vehicle routing problems, EVRP requires optimizing not only the customer route but also the charging scheme. This implies that routes must be planned while considering the availability of charging stations, and charging decisions must be made based on the specific route structure. Although the dual-population-based co-evolutionary algorithm (DPCA) has been proposed to address this coupling relationship, there is still room for further performance improvement. Therefore, this paper proposes an interaction-enhanced co-evolutionary algorithm (IECA) that facilitates a more effective collaborative search between routing and charging optimizations. For routing optimization, we propose an improved ant colony optimization method with a novel pheromone update approach to generate diverse route solutions. Specifically, we utilize information from the charging population to guide the pheromone update process. In contrast to DPCA, which solely relies on the best solution, we establish interaction among multiple solutions from both the routing and charging populations, which can better contribute the information interaction between two populations. Furthermore, we devise a novel population update strategy that enhances the selection of promising solutions. Experimental results validate the effectiveness of the proposed algorithm by demonstrating its ability to avoid local optima. Moreover, it achieves a reduction of approximately 4% in the average route distance across two public test suites. The source code is available at https://github.com/ShouLiang-Z/IECA.

Benchmark of mixed-integer linear programming formulations for district heating network design

Optimal routing and investment decisions are key design criteria to reduce the high investment costs of district heating systems. However, these optimization problems have prohibitively high computational costs for large districts. Four different mixed-integer linear optimization frameworks are benchmarked in this study in order to compare their computational scaling. The frameworks exhibit significant differences in solving times for synthetic benchmarks and real-world urban districts of up to 9587 potential edges. The new open-source framework topotherm, developed for this work, exhibits the best computational performance when only one time step is optimized. The comparison between the models Résimont, DHmin, DHNx, and topotherm shows two main trends. First, fewer integer variables do not necessarily translate to lower solving times, and second, using redundant binary variables, which introduce symmetries into the constraints, leads to higher solving times. None of the considered optimization frameworks is able to solve the largest benchmark problems for five time steps within the allowed time limit and tolerance. These findings highlight the challenges of and pressing need to develop efficient models for simultaneous optimization of district heating network topology, pipe sizing, and operation.

Optimal routing and end-to-end entanglement distribution in quantum networks

Quantum networks are designed to transmit quantum bits (qubits) among quantum devices to enable new network resources for the applications. Entanglement distribution and entanglement swapping are fundamental procedures that are required in several network operations. However, they are probabilistic operations, which can lead to severe network performance degradation. This article investigates the engineering problem of resource allocation in quantum networks, considering factors like entanglement distribution probability, quantum memory characteristics, and fidelity. We model this as an optimization model to obtain an optimal solution. In particular, we formulate an integer linear programming (ILP) and develop a heuristic algorithm, aiming to minimize the number of required entangled qubit pairs (Bell pairs or EPR pairs) in any adjacent pair in the quantum network. Extensive simulations are performed to compare the performance of proposed ILP and heuristic. In all the cases, the heuristic produces a comparable solution to the optimal one. Simulation results ensure that the value of maximum utilized Bell pairs in a quantum network highly depends on the value of the probability of entangled pairs established, considering the time in the quantum memory besides the number of incoming requests.

Optimizing energy efficiency and routing in wireless sensor networks through genetic algorithm-based cluster head selection in a grid-based topology

Wireless sensor networks (WSNs) struggle with energy efficiency because of limited node power. This paper presents an approach that uses evolutionary algorithms to choose the Cluster Head (CH) and optimize routing in wireless sensor networks (WSNs) using grid-based topologies. The proposed method repeatedly develops solutions based on criteria for node density, distance, and energy level by using the evolutionary capabilities of the genetic algorithm. A fitness function that considers latency, coverage, and energy efficiency is used to evaluate the solutions. The process selects CHs dynamically and uses GA-guided optimization to construct paths. Simulation results indicate improved network performance and energy efficiency over existing protocols. Evolutionary algorithm integration enables flexibility and optimization for energy-efficient CH selection and routing in WSNs with a grid-based design.

Research on Fresh Produce Delivery Scheduling Problems under the Perspective of Community E-commerce

This paper addresses the challenges associated with the high costs and logistical difficulties in the distribution of fresh agricultural products from the perspective of community e-commerce. The paper takes the perspective of merchant scheduling and integrates a classical vehicle scheduling model. The study meticulously examines the various costs associated with the distribution process, encompassing fixed costs, transportation costs, refrigeration costs, storage costs and time subsidies. A vehicle routing optimisation model was developed with the dual objectives of cost control and product quality assurance. The use of a genetic algorithm for analysis and solution indicates that this approach can achieve superior cost efficiency and more optimal delivery routes. The results not only confirm the validity of the model and the effectiveness of the solution algorithm, but also provide theoretical underpinnings and viable optimisation strategies for analogous route optimisation challenges. This research has significant reference value and practical relevance for improving delivery efficiency and cost management in community e-commerce.

Multiobjective Routing Optimization to Support IoUT Applications in UWSNs

Multiobjective Routing Optimization to Support IoUT Applications in UWSNs

Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

SummaryClustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision‐making process. The design and development of an energy‐efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non‐deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end‐to‐end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation.

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Collaborative Routing Optimization for Heterogeneous Trucks–Drones Under Urban Regional Restrictions

Collaborative Routing Optimization for Heterogeneous Trucks–Drones Under Urban Regional Restrictions

A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning

Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a ‘cyborg walk’ method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this ‘cyborg walk’ experiment and the seminal cyborg metaphor proposed by Donna Haraway.

Combining Bayesian Networks and MCDA methods to maximise information gain during reconnaissance in emergency situations

In the immediacy of an event that disrupts the operation of an infrastructure, the time between its occurrence and the arrival of qualified personnel for emergency response can be valuable. For example, it can be used for gathering information about the status of the infrastructure by using automated reconnaissance devices. In an operation which precedes the intervention of human first responders, such devices can gather information about the situation, providing knowledge about the locations of stressors (e.g. fire), about the inaccessibility of parts of the infrastructure or about the presence of hazardous materials. In this study, we show how a Bayesian Networks can be used for knowledge representation and how it can be combined with methods from the realm of Multi-Criteria Decision Analysis (MCDA) for situation reconnaissance and route-optimisation in emergency situations, where different criteria (current belief about the location of zones of special interest, such as emergency exits, distance to the next point of interest, etc.) can be considered. As an example, we consider the case of an outbreak of a fire in a building. A pedantic check of all rooms by an automated reconnaissance device would take too long and thus delay intervention. Due to the limited time in which the building can be explored, the route is optimised to gather the greatest possible amount of information in the available time window. Results show how it is possible to maximise the information collected in a limited time window. This is done by discovering the location of fire and any hazardous materials through causal inferences automatically calculated by the Bayesian network. Route optimisation is facilitated by sequential MCDA using a parameter selection that meets the priorities of the specific application example.

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Distributed Routing Optimization Algorithm for FANET Based on Multiagent Reinforcement Learning

Distributed Routing Optimization Algorithm for FANET Based on Multiagent Reinforcement Learning

Electric vehicle routing optimization under 3D electric energy modeling

Electric vehicle routing optimization under 3D electric energy modeling

Automated mobile robots routing and job assignment in automated factory

In the era of Industry 4.0, smart manufacturing and automated factories require advanced software and hardware resources. The decisions on AMR routing and job assignment are two of the key issues in automated factories. In this paper, we investigate the key issues by establishing a mixed integer linear programming model to minimize the makespan for a batch of products. As the problem considers the joint optimization of AMR routing, job-to-AMR assignments, job-to-machine assignments, and job sequencing on machines, the proposed model is comprehensive but complex, with million integer variables and constraints. To tackle this complexity, we introduce a hybrid heuristic approach based on variable neighborhood search and adaptive large neighborhood search algorithms. Three accelerating tactics, two specific neighborhood structures, and six tailored operators are devised to handle large-scale instances efficiently. Remarkably, our metaheuristic can handle 280 jobs within 800 s, resulting in twice the efficiency achieved by traditional factories. Some managerial implications are also obtained based on sensitivity analysis, which may be potentially useful to increase the operational efficiency in automated factory management.

Integrated optimization of pilot and pilot carrier routing in seaports

Pilotage is an important service for vessels to enter and leave seaports. When vessels entering or leaving their berths, pilots are assigned to provide assistance on board to maneuver the vessels. Pilot carriers, such as pilot boats and helicopters are utilized to transport pilots from the pilot station to the vessels at appointed ground. With the increasing number of calling vessels, the pilotage plan should be made properly considering the limited pilots and carriers resources in seaports to enhance timing reliability, which is a key port performance indicator. This paper studies the pilot and pilot carrier routing problem arising in seaports, where both the routes of pilots and carriers are determined jointly to achieve the lowest pilotage costs. The required service time intervals of vessels, the synchronization between pilots and carriers and the limited working duration for both pilots and carriers are considered. The problem is formulated as an arc-flow model, and a hybrid multi-start adaptive large neighborhood search and local search algorithm (mALNS-LS) is developed. Efficient departure time and cost computation methods are designed based on a set of forward and backward functions, and a model-based post optimization is applied. Computational experiments verify the performance of the proposed mALNS-LS in terms of solution quality and efficiency.

SDNRoute: Proactive routing optimization in Software Defined Networks

Despite opening attractive perspectives, the concept of Software Defined Networking raises doubts about the performance and practical feasibility. To contradict these concerns, we propose a deployment-ready system aimed at proactive and periodic optimization of flow paths. The modular system consists of modules responsible for traffic prediction, static optimization, measurements, flow management, and validation of optimization results. To make the system efficient, we resolved several scientific issues and proposed novel and valuable solutions, for example methods for efficient proactive flow management and periodic re-optimization of routing policies. Simultaneously, to make the system production-ready and create a reliable research environment, we provide solutions to several technical obstacles.We validate the proposed system with three network topologies, each with three load levels, following real-life traffic models. We consider Equal-Cost Multi-Path Routing as a baseline. The results indicate that the system allows network operators to handle more traffic (packet loss reduced by up to 30%), improve quality of service (less congested links resulted in even 2.5 times lower latency), and reduce operational expenses (energy consumption lowered by up to 10%).

An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

Levy flight based momentum search and Circle Rock Hyrax swarm optimization algorithms for energy effective clustering and optimal routing in wireless body area networks

In recent years, the connectivity of a new dimension has been brought to Wireless Body Area Networks (WBANs) through the concept of the Wearable Internet of Things (WIoT). WIoT offers valuable functionalities like data collection, analysis, and transmission for real-time health monitoring services. However, WBANs are characterized by a large number of battery-powered sensor nodes, making them resource-constrained. While keeping standard WSN abilities, WBAN's significant obstacles are data communication, social mobility, high interference in dense deployment, latency, and energy consumption of sensor nodes. As a result, the implementation and design of an application require dependable data transfer and an energy-efficient architecture. It proved critical to have as little delay as possible while delivering correct data. To overcome these issues, this study developed a novel energy-efficient clustered and optimized routing scheme in WBAN. Due to its effective scalability, this work employs an Improved Self-Executing Active Cross-Propagated (ISAC) clustering approach to form clusters in Wireless Body Area Networks (WBANs). Subsequently, the selection of the best Cluster Heads (CHs) is facilitated by the Levy Flight-based Momentum Search Algorithm (LFMSA). The Circle Rock Hyrax Swarm Optimization (CRH) algorithm is then utilized for energy-efficient routing. In determining the CH in each cluster, consideration is given to both residual energy and connectivity. The proposed framework minimizes the number of transmissions and overall transmission distance, optimizing inter and intra-cluster transmission distances. Additionally, the framework ensures energy-efficient packet delivery from CHs to the sink node. Performance comparisons, conducted using various statistical measures and simulated on the NS-2 platform, demonstrate that the proposed framework outperforms existing routing protocols and clustering methodologies. In comparison to previous methodologies, the proposed framework consistently achieves superior results.