Optimal Assignment Research Articles

Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm

As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision variables, we achieve a mathematical description of UAV task paths and propose collaborative action constraints. To balance the benefits and efficiency of task assignment, we introduce a novel method: the Adaptive Sampling-Based Task Rationality Review Algorithm (ASTRRA). In the ASTRRA, to address the issue of high-value tasks being easily overlooked when the sampling probability decreases, we propose an adaptive sampling strategy. This strategy increases the sampling probability of high-value targets, ensuring a balance between computational efficiency and maximizing task value. To handle the coherence issues in UAV task paths, we propose a task review and classification method. This method involves reviewing issues in UAV task paths and conducting classified independent auctions, thereby improving the overall task assignment value. Additionally, to resolve the crossover problems between UAV task paths, we introduce a crossover path exchange strategy, further optimizing the task assignment scheme and enhancing the overall value. Experimental results demonstrate that the ASTRRA exhibits excellent performance across various task scales and dynamic scenarios, showing strong robustness and effectively improving task assignment outcomes.

Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting

Channel assignment in Wi-Fi 6 dense networks is a challenging problem which critically impacts the performance of WLAN communications. Although there have been many proposals in the area of IEEE 802.11 channel assignment, LCCS has emerged as the de facto standard for distributed channel selection. Notwithstanding, centralized optimizers overcome the performance of distributed channel assignment algorithms, so their study is of utmost importance. One of the most successful optimization proposals in the state-of-the-art for Wi-Fi 4 is based on simulated annealing (SA). However, SA presents a significant limitation: its parameters must be adjusted manually for each scenario, otherwise yielding suboptimal solutions. In this paper, we propose a new technique for channel assignment optimization of Wi-Fi dense networks, called Thermodynamic Threshold Accepting (TTA), including modifications inspired by Threshold Accepting (TA) and Thermodynamic Simulated Annealing (TSA). With respect to SA, TTA avoids manual tuning, being able to adapt to the specific setting automatically. We evaluate TTA in a dense Wi-Fi model and, compared to SA, we show that our approach reaches optimal results without requiring any prior fine-tuning. We also show that TTA is even able to slightly overcome the performance of an accurately calibrated SA optimizer for the same number of iterations.

Estimating optimal decision trees for treatment assignment: The case of K > 2 treatment alternatives.

For many problems in clinical practice, multiple treatment alternatives are available. Given data from a randomized controlled trial or an observational study, an important challenge is to estimate an optimal decision rule that specifies for each client the most effective treatment alternative, given his or her pattern of pretreatment characteristics. In the present paper we will look for such a rule within the insightful family of classification trees. Unfortunately, however, there is dearth of readily accessible software tools for optimal decision tree estimation in the case of more than two treatment alternatives. Moreover, this primary tree estimation problem is also cursed with two secondary problems: a structural missingness in typical studies on treatment evaluation (because every individual is assigned to a single treatment alternative only), and a major issue of replicability. In this paper we propose solutions for both the primary and the secondary problems at stake. We evaluate the proposed solution in a simulation study, and illustrate with an application on the search for an optimal tree-based treatment regime in a randomized controlled trial on K = 3 different types of aftercare for younger women with early-stage breast cancer. We conclude by arguing that the proposed solutions may have relevance for several other classification problems inside and outside the domain of optimal treatment assignment.

Traffic assignment optimization to improve urban air quality with the unified finite-volume physics-informed neural network

Emissions by road transportation constitute the major contributor of air pollutants to threat the public health, especially on mega cities with high-rise buildings and growing population. A practicable and economical remedy is assigning the traffic volume judiciously, termed traffic assignment optimization (TAO). Incorporating the computational fluid dynamics (CFD), this method provides precisely optimized traffic volumes under versatile meteorological conditions. However, the high-dimensional degree of freedoms (DoF) involved in CFD hampers its further application toward the large-scale problem where a massive urban area is considered. With the more complicated urban traffic network, the larger decision variable dimension also impedes the time-efficient acquisition of the optimization results. To alleviate this curse-of-dimensionality, the current work proposes an optimization framework with the surrogate model based on the unified finite-volume physics-informed neural networks (UFV-PINN). The UFV-PINN plays the dual role as the partial differential equation (PDE) solver and the surrogate model for pollutant concentration prediction. It reduces the DoF within the PDE solution process and enables the gradient-based optimization. The current work optimizes the average CO concentration and accumulative travel time in Kowloon peninsula of Hong Kong, using the multiple-gradient descent algorithm (MGDA). This optimization framework is tested with various working conditions. Results indicate that the proposed method attain high accuracy solutions, comparable to the heuristic algorithms. This study is the first attempt to incorporate UFV-PINN into the TAO with air quality consideration. Endowed with the differentiability by UFV-PINN, the framework will facilitate the TAO to provide precise suggestions toward large-scale problems.

Alternative service network design for bus systems responding to time-varying road disruptions

In practice, road disruptions occur frequently, interrupting multiple bus routes at the same time and causing widespread passenger delays. Typically, these disrupted roads are repaired sequentially and then gradually put into service. In response to such time-varying road disruptions, this paper aims to assist bus operators in developing effective alternative service networks for passengers. The proposed approach involves the joint optimization of service-based route adjustments, bus timetables, and passenger assignment to minimize the total passenger cost and weighted bus operation time. Specifically, a novel service-based adjustment strategy is introduced to flexibly adapt each bus service to time-varying road disruptions. An integer programming model is built for the studied problem based on the set of passengers’ time-space itineraries. To efficiently generate these time-space itineraries and solve models for large-scale problems, this paper develops a hierarchical solution framework. The framework consists of three key parts: (1) a column generation procedure to iteratively explore passengers’ spatial paths; (2) a customized extension algorithm to extend these spatial paths to time-space itineraries; and (3) a tailored adaptive large neighbourhood search heuristic to solve the final itinerary-based model. After that, the overall methodology is tested with both an illustrative example and a real-world example in Beijing. Experimental results show that our methodology produces a high-performance solution with only 7.3% of unserved passengers. Besides, compared to the two benchmark adjustment strategies, our service-based adjustment strategy reduces the average itinerary cost for all passengers by 27.0% and 43.3%, respectively.

Three-Dimensional Coverage Path Planning for Cooperative Autonomous Underwater Vehicles: A Swarm Migration Genetic Algorithm Approach

Cooperative marine exploration tasks involving multiple autonomous underwater vehicles (AUVs) present a complex 3D coverage path planning challenge that has not been fully addressed. To tackle this, we employ an auto-growth strategy to generate interconnected paths, ensuring simultaneous satisfaction of the obstacle avoidance and space coverage requirements. Our approach introduces a novel genetic algorithm designed to achieve equivalent and energy-efficient path allocation among AUVs. The core idea involves defining competing gene swarms to facilitate path migration, corresponding to path allocation actions among AUVs. The fitness function incorporates models for both energy consumption and optimal path connections, resulting in iterations that lead to optimal path assignment among AUVs. This framework for multi-AUV coverage path planning eliminates the need for pre-division of the working space and has proven effective in 3D underwater environments. Numerous experiments validate the proposed method, showcasing its comprehensive advantages in achieving equitable path allocation, minimizing overall energy consumption, and ensuring high computational efficiency. These benefits contribute to the success of multi-AUV cooperation in deep-sea information collection and environmental surveillance.

Eigenstructure assignment of constrained second-order linear systems: an optimised positively-invariant set control approach

Second-order systems constitute a mathematical model class used to represent a variety of physical processes to be controlled, such as vehicle damping systems, hydraulic pump flow, and robot manipulators. Due to the practical limitation inherent to physical systems (or their mathematical representation) and constraints on the input variables, the constrained control theory became an important research field. In this work, based on set-invariance theory, we propose an eigenstructure assignment optimisation using a state-feedback control law that guarantees the respect of state and control constraints for second-order systems. Positive invariance is the property of keeping the trajectory of the system states inside the set. This characteristic is incorporated into the analysis and design of the control law to ensure local stability and constraint fulfilment.

Current Effect-Eliminated Optimal Target Assignment and Motion Planning for a Multi-UUV System

Current Effect-Eliminated Optimal Target Assignment and Motion Planning for a Multi-UUV System

Grant-Free NOMA Through Optimal Partitioning and Cluster Assignment in STAR-RIS Networks

Grant-Free NOMA Through Optimal Partitioning and Cluster Assignment in STAR-RIS Networks

Multiple Object Tracking in Drone Aerial Videos by a Holistic Transformer and Multiple Feature Trajectory Matching Pattern

Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions.

Integrated optimization of worker assignment, batch splitting and scheduling for a hybrid assembly line-seru production system

This paper studies an integrated optimization problem of worker assignment, batch splitting and scheduling for a hybrid assembly line-seru production system, which includes multiple serus and a short assembly line. The worker assignment involves assigning each multi-skilled worker to a seru or the assembly line, and assigning the worker tasks to perform. The batch splitting determines the number of subbatches split for each batch, the size of each subbatch, and in which seru the production of each subbatch is performed. The batch scheduling aims to optimize the process sequences of batches in serus and on the assembly line. For the integrated optimization problem, we propose a mixed-integer nonlinear programming model that minimizes the makespan. To solve large-scale instances of the problem, we develop a tailored hybrid variable neighborhood search algorithm with a four-layer solution coding scheme and four novel neighborhood structures. We validate the effectiveness of our model and solution algorithm on two sets of instances. The experimental results indicate that the proposed hybrid metaheuristic achieves a significant reduction in makespan, with an improvement of up to 20.94% compared to the solutions obtained by the Gurobi optimization solver within one hour. In addition, we demonstrate the advantage of optimized batch splitting by comparing it with no batch splitting and equal-size batch splitting strategies in the literature.

Enhanced Energy Transfer Efficiency for IoT-Enabled Cyber-Physical Systems in 6G Edge Networks with WPT-MIMO-NOMA

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections’ impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks.

Integrated planning approach for fleet sizing and fleet management of freight railcars

AbstractDespite the general support for rail as sustainable and efficient transport mode, its share on modal split remains rather low. One of the reasons for this is the low flexibility and adaptability of rail system to changes in, e.g., demand due to the complex multi-stage planning processes that are partly done manually. In our paper we focus on the fleet planning process for freight railcars and we propose an integrated planning approach consisting of two parts. Firstly, a fleet sizing optimization model is applied to obtain the optimal assignment of railcars to customers’ orders. Within this model, we consider a heterogeneous fleet and take into account substitution between clusters and customers’ preferences. The optimal fleet size is computed on a monthly basis for a planning horizon of multiple years, accounting also for railcar flows between months and years. Secondly, a fleet management model in form of knowledge-based decision support system is applied to help the planner to make changes in the fleet mix by investing into or leasing new railcars, performing revisions and decommissioning of old or unutilized railcars. As also illustrated in the designed real-world case study, the integrated planning approach is able to provide the planner with an overview of possible actions including their direct impact on the fleet mix for a mid-term planning horizon.

An optimization approach to evaluating accessibility to community pharmacies

ABSTRACT Accessibility to community pharmacy is key to community health and well-being. An optimal community-pharmacy assignment (OCPA) model is developed to measure the geographical accessibility of community pharmacies in a regional population network. The model minimizes a risk-adjusted distance between census tracts and community pharmacies by optimally matching census tracts to single or multiple pharmacies. Social determinants of health (SDOH), such as vehicle ownership, access to public transit, and insurance, are used as risk factors for adjusting the geographic distance. We present a novel definition of ‘pharmacy desert’ based on the distance between census tracts and their matching community pharmacies. Extensive computational experiments are conducted based on a case study in Louisville, Kentucky. Benchmark studies with leading literature and other public health indicators validate the proposed model. Extensive sensitivity analyses suggest our model for identifying pharmacy desert is effective and can be a valuable tool for pharmacy and public health stakeholders.

Shelter location–allocation problem for disaster evacuation planning: A simulation optimization approach

Immediately after a major earthquake strikes, a segment of the people in the disaster region must evacuate to relief shelters. If the evacuation proceeds in an efficient and safe manner, many lives can potentially be saved as the effects of secondary factors, e.g., secondary collapses, stampedes, and aftershocks, can largely be mitigated, while at the same time the people taking refuge can quickly gain access to vital relief goods. Formulating the best possible protocols for post-earthquake evacuation is a crucial, yet highly challenging problem considering the profound uncertainty which is present in the post-disaster environment, including the location and degree of infrastructural damage, the number and location of people who need to evacuate, the behavior and psychology of evacuees, etc. In this research, we formulate a two-stage stochastic evacuation simulation model capable of credibly accounting for these uncertainties. We develop a solution methodology to solve the shelter location–allocation problem represented by the two-stage model, finding the optimal location of shelters to be open as the first stage solution and the optimal assignment of evacuees to shelters as the second stage solution to minimize the total expected cost. The cost-based objective function consists of the cost of opening shelters, a deprivation cost component, the cost of trapped evacuees, and the cost of shelters exceeding capacity. In developing this framework, we collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in utilizing GIS-based probabilistic models of road damage. We apply the proposed framework to run an extensive numerical study and present various valuable observations and insights which can be gained.

A general solution to the quasi linear screening problem

We provide an algorithm for solving multidimensional screening problems which are intractable analytically. The algorithm is a primal–dual algorithm which alternates between optimizing the primal problem of the surplus extracted by the principal and the dual problem of the optimal assignment to deliver to the agents for a given surplus. We illustrate the algorithm by solving (i) the generic monopolist price discrimination problem and (ii) an optimal tax problem covering income and savings taxes when citizens differ in multiple dimensions.

Deep reinforcement learning and ant colony optimization supporting multi‐UGV path planning and task assignment in 3D environments

AbstractWith the development of artificial intelligence, the application of unmanned ground vehicles (UGV) in outdoor hazardous scenarios has received more attention. However, the terrains in these environments are often complex and undulating, which also pose higher challenges to the multi‐UGV path planning and task assignment (MUPPTA) optimization. To efficiently improve the multi‐UGV collaboration in 3D environments, a MUPPTA method is proposed based on double deep Q learning network (DDQN) and ant colony optimization (ACO) to jointly optimize the path planning and task assignment decisions of multiple UGVs. The authors first comprehensively consider the characteristics of the 3D environments, and model the MUPPTA problem as a combinatorial optimization problem. To tackle it, the original problem is decomposed into the multi‐UGV path planning sub‐problem and task assignment sub‐problem, and solve them separately. First, the path planning sub‐problem in the 3D environments is transformed into a Markov decision process (MDP) model, and a multi‐UGV path planning algorithm based on DDQN (MUPP‐DDQN) is proposed to obtain the optimal paths and actual path costs between tasks through extensive offline learning and training. Based on this, a multi‐UGV task assignment algorithm is further proposed based on ACO (MUTA‐ACO) to solve the task assignment sub‐problem and achieve the optimal task assignment solution. Simulation results show that the proposed method is more cost‐effective and time‐saving compared to other comparison algorithms.

Physicist's view on the unbalanced k-cardinality assignment problem.

The k-cardinality unbalanced assignment problem asks for assigning k "agents" to k "tasks" on a one-to-one basis, while minimizing the total cost associated with the assignment, with the total number of agents N and the total number of tasks M possibly different and larger than k. While many exact algorithms have been proposed to find such an optimal assignment, these methods are computationally prohibitive when the problem is large. We propose an approach to solving the k-cardinality assignment problem using techniques adapted from statistical physics. This paper provides a full description of this formalism, including all the proofs of its main claims. We derive a strongly concave free-energy function that captures the constraints of the k-assignment problem at a finite temperature. We prove that this free energy decreases monotonically as a function of β, the inverse of temperature, to the optimal assignment cost, providing a robust framework for temperature annealing. We also prove that for large enough β values the exact solution to the k-assignment problem can be derived using simple round-off to the nearest integer of the elements of the computed assignment matrix. We show that this framework can be adapted to handle degenerate k-assignment problems. We describe a computer implementation of our framework that is optimized for the GPU parallel architecture, using the library CUDA. This implementation is found to be as efficient as state-of-the-art implementations of parallel Hungarian algorithms on generic assignment problems, and orders of magnitude faster than those algorithms for pathological assignment cases.

Multi-Objective Optimization Methods for Train Load Planning in Seaport Container Terminals

In this paper we study the planning problem of loading trains in seaport container terminals, which consists in assigning containers to the wagon slots. The train load can be realized considering different objectives, in this work we focus on two very important objectives for terminal managers, that are the minimization of the number of reshuffles in the storage area and the minimization of the total distance covered by the handling equipment between the storage area and the wagons. To address this problem, some multi-objective methods are investigated in the paper, based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -constraint approach and the lexicographic algorithm, allowing to find different sets of efficient solutions to be provided to train planners. The experimental tests, devised to compare the proposed multi-objective methods, show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -constraint method integrated with a lexicographic approach is the most efficient choice since it allows to obtain a higher number of efficient solutions in shorter computational times. This method can be applied in decision support systems for train planners in order to allow them to decide the train load in an efficient way. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The automation and the process optimization in seaport container terminals is a relevant issue for terminal managers in order to face the strong competitiveness in the logistics and shipping sector and the new emerging problems due to the presence of mega vessels. Among the different operations to be optimally managed in a terminal, the loading of trains is becoming more and more significant, also due to the increase of freight traffic by rail, seen as a cheaper and more sustainable option compared with road transport. The optimal assignment of containers present in a storage area to the wagon slots is a crucial decision in order to reduce operational times and costs in a container terminal. In this paper, we address the train load planning problem considering two important objectives to be minimized, i.e. the number of reshuffles and the distance covered by the handling equipment. The multi-objective methods proposed in the paper are devised to find different efficient solutions in short computational times. The proposed algorithms are intended to be implemented within a decision support tool in which the solutions found are shown to the train planner who chooses the preferred option depending on personal evaluations and on the basis of information coming in real time.

Maximin headway control of automated vehicles for system optimal dynamic traffic assignment in general networks

This study develops the headway control framework in a fully automated road network, as we believe headway of Automated Vehicles (AVs) is another influencing factor to traffic dynamics in addition to conventional vehicle behaviors (e.g. route and departure time choices). Specifically, we aim to search for the optimal time headway between AVs on each link that achieves the network-wide system optimal dynamic traffic assignment (SO-DTA). To this end, the headway-dependent fundamental diagram (HFD) and headway-dependent double queue model (HDQ) are developed to model the effect of dynamic headway on roads, and a dynamic network model is built. It is rigorously proved that the minimum headway could always achieve SO-DTA, yet the optimal headway is non-unique. Motivated by these two findings, this study defines a novel concept of maximin headway, which is the largest headway that still achieves SO-DTA in the network. Mathematical properties regarding maximin headway are analyzed and an efficient solution algorithm is developed. Numerical experiments on both a small and large network verify the effectiveness of the maximin headway control framework as well as the properties of maximin headway. This study sheds light on deriving the desired solution among the non-unique solutions in SO-DTA and provides implications regarding the safety margin of AVs under SO-DTA.