Guohua Wu

Central South University | CSU · School of Traffic and Transportation Engineering

The analysis of satellite coverage to determine the visibility of ground targets from satellites has been extensively studied, particularly through geometric-based methods that provide analytical results. However, there exists a key limitation that these methods do not precisely consider the elevation-angle constraint. To address this limitation, t...

Super-agile satellites are high-performance Earth observation satellites with active push-brooming capability and a real-time attitude control system. The highly flexible attitude maneuver capability of super-agile satellites have aggravated the complexity of the observation schedule. To solve the multiple superagile satellites cooperative scheduli...

The multi-access edge computing (MEC) provides opportunities for unmanned aerial vehicles (UAVs) to perform computing-intensive and delay-sensitive applications. To further reduce the time delay and energy consumption of UAVs, in this paper, we study the long-term optimization problem of joint task offloading and resource allocation in a multi-UAV...

When considering disintegration of spatial networks, the topological relationships between nodes and their geographical positions are taken into account. In the case of regional attacks, the targeted nodes are determined based on the size of the region and the geographical relationships between nodes. In this paper, a new metric is proposed on top...

Owing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of “last-mile” delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a...

The practical engineering of satellite tracking telemetry and command (TT&C) is often disturbed by unpredictable external factors, including the temporary rise in a significant quantity of satellite TT&C tasks, temporary failures and failures of some TT&C resources, and so on. To improve the adaptability and robustness of satellite TT&C systems whe...

The vehicle routing problem with backhauls (VRPBs) is a challenging problem commonly studied in computer science and operations research. Featured by linehaul (or delivery) and backhaul (or pickup) customers, the VRPB has broad applications in real-world logistics. In this article, we propose a neural heuristic based on deep reinforcement learning...

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the insta...

Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To address the underlying task scheduling problem, conventional exact and heuristic algorithms encounter challenges such as rapidly increasing computation time and heavy reliance on domain knowledge, particularly when dealing with large-scale problem...

Recently, with the enhancement of drone's payload and endurance capability, drone logistics has received extensive attention from giant logistics enterprises. This research delves into the Heterogeneous multi-Drone Delivery Pickup Problem (HDDPP) through a novel distribution mode. Here, a large drone ferries multiple smaller drones to a specified s...

Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution. 3-D path planning problems for UAVs can be treated as constrained multi-objective optimization problems. However, due to the complexity of real-world problems, the Pareto front frequently exhibits irregularity. For path planning problems ch...

In fleet air defense, the efficient coordination of multiple ships to complete weapon-target assignment has always been a critical challenge, primarily due to the varying combat capabilities and duties associated with each ship. Consequently, the traditional “weapon-target” assignment mode has turned into a “ship-weapon-target” assignment mode in t...

Unmanned aerial vehicles (UAVs) have emerged as promising platforms for fast, energy-efficient, and cost-effective package delivery. Path planning in 3-D urban environments is critical to drone delivery. The paper proposes a novel tangent-based (3D-TG) method for UAV path planning in 3-D urban environments. When a drone encounters an obstacle, a ta...

The agile earth observation satellite scheduling problem (AEOSSP) is a combinatorial optimization problem with time-dependent constraints. Recently, many construction heuristics and meta-heuristics have been proposed; however, existing methods cannot balance the requirements of efficiency and timeliness. In this paper, we propose a graph attention...

System disturbances, such as the change of required service durations, the failure of resources, and temporary tasks during the scheduling process of data relay satellite network (DRSN), are difficult to be predicted, which may lead to unsuccessful scheduling of tasks. A high-efficiency and robust DRSN calls for smarter and more flexible disturbanc...

To efficiently implement the truck-drone collaborative logistics system, we introduce a multi-objective truck-drone collaborative routing problem with delivery and pick-up services (MCRP-DP). A truck collaborating with a fleet of drones serves three types of customers that require delivery, pick-up, and simultaneous delivery & pick-up services, res...

The application of drones in last-mile distribution has been a contentious research topic in recent years. Existing urban distribution modes mostly depend on trucks. This paper proposes a novel package pickup and delivery mode and system wherein multiple drones collaborate with automatic devices. The proposed mode uses free areas on top of resident...

Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution. 3-D path planning is a key challenge for task decision-making. This paper proposes an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) with an adaptive areal weight adjustment (AAWA) strategy to make a tradeof...

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses. Real-world emergency material scheduling (EMS) problems are typically large-scale and possess complex constraints. An evolutionary algorithm (EA) is one of the effective methods for solving EMS problems. Ho...

Data relay satellite networks (DRSNs) face the challenge of increasing relay mission demands in space networks. To improve the task scheduling efficiency of DRSN further, we propose a novel task scheduling framework, wherein a scheduling sequence is generated by selecting one antenna and selecting one task for the antenna in each step. Subsequently...

In this study, we investigate an unrelated parallel batch processing machines scheduling problem (UPBPMSP). A set of jobs with non-identical sizes and arbitrary ready times are scheduled on unrelated parallel batch processing machines with different capacities to minimize the makespan (i.e., the completion time of the last batch). Existing studies...

This paper studies a scheduling problem with non-identical job sizes, arbitrary job ready times, and incompatible family constraints for unrelated parallel batch processing machines, where the batches are limited to the jobs from the same family. The scheduling objective is to minimize the maximum completion time (makespan). The problem is importan...

Metaheuristics are popularly used in various fields, and they have attracted much attention in the scientific and industrial communities. In recent years, the number of new metaheuristic names has been continuously growing. Generally, the inventors attribute the novelties of these new algorithms to inspirations from either biology, human behaviors,...

A variable reduction strategy (VRS) drives evolutionary algorithms (EAs) to evolve more effectively by simplifying optimization problems. To represent a decision space with the smallest set of variables via VRS, a variable reduction optimization problem (VROP) has been defined, which could be handled by a heuristic rule-based automatic variable red...

Efficient scheduling is critical for the effective use of heterogeneous UAVs equipped with various sensors. The collaborative “electronic signal guided imaging” reconnaissance mode is first discussed in this paper, where electronic signal reconnaissance equipment is initially used to locate electromagnetic targets over long distances, offering appr...

Metaheuristics are popularly used in various fields, and they have attracted much attention in the scientific and industrial communities. In recent years, the number of new metaheuristic names has been continuously growing. Generally, the inventors attribute the novelties of these new algorithms to inspirations from either biology, human behaviors,...

The COVID-19 pandemic calls for contactless deliveries. To prevent the further spread of the disease and ensure the timely delivery of supplies, this paper investigates a collaborative truck-drone routing problem for contactless parcel delivery (CRP-T&D), which allows multiple trucks and multiple drones to deliver parcels cooperatively in epidemic...

Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment, and other related domains. Many unpredictable factors, such as changes in observation task requirements, bad weather, and resource malfunctions, may cause the scheduled observation scheme to become infeasible. In these cases, it is crucial to...

Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multisatellite observation scheduling problem, this article proposes an ensemble of metaheuristic and exact algorithms based on a divide-and-conquer framework (EHE-DCF), including a task allocation phase and a...

In China, logistics platforms are an effective way to solve vehicle capacity utilization using information sharing. However, most logistics platforms do not possess operational sustainability due to excessive profit-seeking. To address this problem, conflicts of interest among freight transportation participants are discussed using a stakeholder ap...

Unmanned Aerial Vehicles, commonly known as drones, have been widely used in transmission line inspection and traffic patrolling due to their flexibility and environmental adaptability. To take advantage of drones and overcome their limited endurance, the patrolling tasks are parallelized by concurrently dispatching the drones from a truck which tr...

This paper studies deep reinforcement learning (DRL) for the task scheduling problem of multiple unmanned aerial vehicles (UAVs). Current approaches generally use exact and heuristic algorithms to solve the problem, while the computation time rapidly increases as the task scale grows and heuristic rules need manual design. As a self-learning method...

With the rapid development of drone technology, logistics giants like Amazon and SF Express have applied drones to parcel delivery. Drone delivery could eliminate delivery delays caused by traffic lights and traffic jams on ground vehicles, and it can deliver parcels in case of road damage caused by natural disasters. Based on this motivation, we s...

In light of the existing practical applications of the two-dimensional loading on vehicle scheduling and many-to-many supply-demand relationships between suppliers and customers, we address a many-to-many heterogeneous vehicle routing problem with cross-docking and two-dimensional loading constraints (or the heterogeneous 2L-MVRPCD). The newly prop...

The collaboration of drones and trucks for last-mile delivery has attracted much attention. In this paper, we address a collaborative routing problem of the truck-drone system, in which a truck collaborates with multiple drones to perform parcel deliveries and each customer can be served earlier and later than the required time with a given toleran...

The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed significantly to the development of this field, these approaches either are limited in problem size or need manual intervention...

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task sched...

Through urban traffic patrols, problems such as traffic congestion and accidents can be found and dealt with in time to maintain the stability of the urban traffic system. The most common way to patrol is using ground vehicles, which may be inflexible and inefficient. The vehicle–drone coordination maximizes utilizing the flexibility of drones and...

Earth observation satellite (EOS) systems often encounter emergency observation tasks oriented to sudden disasters (e.g., earthquake, tsunami, and mud-rock flow). However, EOS systems may not be able to provide feasible coverage time windows for emergencies, which requires that an appropriately selected satellite transfers its orbit for better obse...

With the development of drone technology, drones have been deployed in civilian and military fields for target surveillance. As the endurance of drones is limited, large-scale target surveillance missions encounter some challenges. Based on this motivation, we proposed a new target surveillance mode via the cooperation of a truck and multiple drone...

The application of drones in the last-mile distribution is a research hotspot in recent years. Different from the previous urban distribution mode that depends on trucks, this paper proposes a novel package pick-up and delivery mode and system in which multiple drones collaborate with automatic devices. The proposed mode uses free areas on the top...

Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation...

In the latest years, Unmanned Aerial Vehicles (UAVs) are widely served to provide network services as aerial base stations for terrestrial users. The deployment optimization for UAVs is essential to improve the service efficiency, which is to resolve the location of UAVs subject to their battery limitation, environmental constraints and the distrib...

In this paper, we address a collaborative routing problem of the truck-drone delivery system, in which a truck collaborates with multiple drones to perform parcel deliveries. To meet the practical demands of non-emergency deliverirs, each vehicle (i.e., a truck or a drone) is allowed to serve a customer earlier and later than the required time wind...

The problem that searches for the shortest delivery time of cooperative routes of trucks and drones is called the cooperative delivery routing prob-lem of trucks and drones (CDRP-T&D). The CDRP-T&D is an extension of the vehicle routing problem which involves multiple trucks and multiple drones. In CDRP-T&D, each truck serves as a moving depot and...

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently,...

The commodity storage assignment problem (CSAP), which assigns stock-keeping units (SKUs) to a suitable location for matching the customer demand patterns, is crucial for improving the order picking efficiency. In this study, we jointly consider the SKUs classification and correlation, and propose a new scattered storage policy named scattered-corr...

A variable reduction strategy (VRS) is an effective method to accelerate the optimization process of evolutionary algorithms (EAs) by simplifying the corresponding optimization problems. Unfortunately, the VRS is manually realized in a trial-and-error manner currently. To boost the efficiency of VRS and enable a more extensive application, we propo...

Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments cannot see through the cloud, the cloud coverage brings a significant influence on the satellite observation missions. The scheduling of multiple agile EOSs (AEOSs) is already complic...

At present, independent scheduling of earth-observation resources (EORs) is usually difficult to satisfy diverse observation requirements and cannot realize the full potential of space-air resource networks. To utilize EORs comprehensively, this study constructs a divide and conquer framework (DCF) for a coordinated scheduling of air and space obse...

Deploying Unmanned Aerial Vehicles (UAVs) for traffic monitoring has been a hotspot given their flexibility and broader view. However, a UAV is usually constrained by battery capacity due to limited payload. On the other hand, the development of wireless charging technology has allowed UAVs to replenish energy from charging stations. In this paper,...

Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with adaptive locality ability, which enables the measurement of the importance of neighboring nodes to the target node by a node-specific mechanism. However, the current node-specific mechanisms are deficient in distinguish...

Remembering and forgetting mechanisms are two sides of the same coin in a human learning-memory system. Inspired by human brain memory mechanisms, modern machine learning systems have been working to endow machine with lifelong learning capability through better remembering while pushing the forgetting as the antagonist to overcome. Nevertheless, t...

Content distribution in vehicular ad hoc networks (VANET) plays an important role to achieve both safety and non-safety types of services. A high-quality scheduling scheme for content distribution can improve transmission efficiency. In this context, we propose a data transmission scheduling approach named data transmission scheduling considering b...

The split delivery vehicle routing problem with two-dimensional loading constraints (2L-SDVRP) is a complex practical problem in the field of logistics. It aims at finding the optimal vehicle routes and two-dimensional packing layouts of items in each vehicle to minimize the transportation cost. In this work, we present a mathematical model of 2L-S...

The task-schedulingalgorithm is a key module to satisfy various complex user requirements, and improve the usage flexibility and efficiency of data relay satellites networks (DRSN). In this context, we first propose a novel application mode for DRSN, in which users are allowed to submit multiple optional service time windows and specify a preferred...

Effective constraint handling techniques (CHTs) are of great significance for evolutionary algorithms (EAs) dealing with constrained optimization problems (COPs). To date, many CHTs, such as penalty function, superiority of feasible solutions, and $\epsilon $ -constraint (EC), have been designed. However, different CHTs are usually suited to diff...

Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the performance of metaheuristics. However, these SBPs may include various unrealistic properties. As a consequence, performance assessment may lead to underestimation or overestimation. To address this issue, few benchmark suites containing real-world problems have been proposed...

Vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve VRP and variants. Although existing approaches has contributed a lot to the development of this field, these approaches either are limited in problem size or need manual intervening in choosing parameter...

Coronavirus disease 2019 has brought a great challenge to the supply of daily necessities and medical items for home-quarantined people. Considering the unmanned operation, agility and use of clean energy of drones, we propose a novel truck-and-drone coordinated delivery system that does not require direct human contact during the delivery process....

Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with the adaptive locality ability, which enables measuring the importance of neighboring nodes to the target node by a node-specific mechanism. However, the current node-specific mechanisms are deficient in distinguishing t...

Incomplete data are frequently encountered and bring difficulties when it comes to further processing. The concepts of granular computing (GrC) help deliver a higher level of abstraction to address this problem. Most of the existing data imputation and related modeling methods are of numeric nature and require prior numeric models to be provided. T...

So far, there has been only one optimization objective in belief rule base (BRB) learning-related studies, either modeling accuracy via parameter learning, modeling complexity via structure learning, or a unified objective representing them both. This article proposes a new cooperative and distributed multiobjective approach for heterogeneous BRB (...

Agile satellites are the next generation of Earth observation satellites (EOSs) because they can rotate on three axes while EOSs cannot. Due to this improvement, agile EOS scheduling has attracted much attention. Meanwhile, the influence of cloud coverage for EOS is inevitable for most satellites equipped with optical remote sensing instruments, ca...

There hardly exists a general solver that is efficient for scheduling problems due to their diversity and complexity. In this study, we develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together to efficiently deal with complex scheduling problems. The scheduling pro...

One of the key problems of GNNs is how to describe the importance of neighbor nodes in the aggregation process for learning node representations. A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network. The basic idea of implicit GNNs...

Multi-objective multi-modal optimization problems have recently received increasing attention in the field of evolutionary computation. Addressing such problems is not easy for existing evolutionary multi-objective algorithms (EMOAs) since they require finding solutions with good convergence and diversity in both objective and decision spaces. This...

For many-objective optimization problems (MaOPs), the proportion of non-dominated solutions in a population scales up sharply with the increase in the number of objectives. Besides, for an MaOP with a fixed number of objectives, the proportion of non-dominated solutions may also grow to a high level with the progressing of the evolutionary process,...

The increasing demands for space-based data transmission pose a great challenge to task scheduling of tracking and data relay satellites (TDRSs). In order to improve the working efficiency and task completion rate of the data relay satellite network (DRSN), for the first time, we propose a novel application mode for DRSN, in which data breakpoint t...

Task scheduling of multiple UAVs has become a highly active area of research in recent years. Previous research has generally solved the problem in a whole manner, which makes it hard to efficiently generate high-quality task scheduling schemes due to prohibitive computational complexity. By contrast, the paper constructs a novel divide and conquer...

The analysis for social networks, such as the sensor-networks in socially networked industries, has shown a deep influence of intelligent information processing technology on industrial systems. The large amounts of data on these networks raise the urgent demands of analyzing the topological content effectively and efficiently in Industrial Interne...

Sorting solutions play a key role in using evolutionary algorithms (EAs) to solve many-objective optimization problems (MaOPs). Generally, different solution-sorting methods possess different advantages in dealing with distinct MaOPs. Focusing on this characteristic, this article proposes a general voting-mechanism-based ensemble framework (VMEF),...