Uncertain Task Research Articles

An Asynchronous Shuffled Frog-Leaping With Feasible Jaya Algorithm for Uncertain Task Rescheduling Problem in UAV Emergency Networks

An Asynchronous Shuffled Frog-Leaping With Feasible Jaya Algorithm for Uncertain Task Rescheduling Problem in UAV Emergency Networks

An Integrated CREAM for Human Reliability Analysis Based on Consensus Reaching Process under Probabilistic Linguistic Environment

Human reliability analysis (HRA) is widely used to evaluate the impact of human errors on various complex human–machine systems for enhancing their safety and reliability. Nevertheless, it is hard to estimate the human error probability (HEP) in reality due to the uncertainty of state assessment information and the complex relations among common performance conditions (CPCs). In this paper, we aim to present a new integrated cognitive reliability and error analysis method (CREAM) to solve the HRA problems under probabilistic linguistic environment. First, the probabilistic linguistic term sets (PLTSs) are utilized to handle the uncertain task state assessments provided by experts. Second, the minimum conflict consensus model (MCCM) is employed to deal with conflict task state assessment information to assist experts reach consensus. Third, the entropy weighting method is used to determine the relative objective weights of CPCs. Additionally, the CPC effect indexes are introduced to assess the overall effect of CPCs on performance reliability and obtain the HEP estimation. Finally, the reliability of the proposed CREAM is demonstrated via a healthcare practical case. The result shows that the new integrated CREAM can not only effectively represent experts’ uncertain task state assessments but also determine more reliable HEP estimation in HRA.

Two-list genetic algorithm for optimizing work package schemes to minimize project costs

Optimizing work package schemes is challenging under uncertain task duration. This paper develops a two-list genetic algorithm (TLGA) to optimize work package schemes with minimal project costs under deterministic and stochastic task durations. First, this paper defines the deterministic and stochastic work package scheme problem. Second, the TLGA, comprising a task and a work packaging list, is developed to generate the deterministic work package scheme and issue work package policies through stochastic distribution simulations. Moreover, a graphical user interface with TLGA is developed to enhance its practical application. Finally, experiments show that the TLGA can reduce the total cost by up to 19.57% in the deterministic problem, and the minimum gap between the TLGA and the state-of-the-art heuristics is only 3.91%. However, the TLGA can reduce the running time by about 66%. In the stochastic problem, this paper analyzes the impact of stochastic distributions on work package policies.

GEO satellite on-orbit refueling and debris removal hybrid mission planning under uncertainty

This article proposes two optimization models to solve the multi-satellite on-orbit hybrid service mission planning problem under uncertainty. During the process of on-orbit refueling and debris removal, two categories of uncertainties are taken into account. These uncertainties have an impact on the mass of the service satellite as well as its fuel consumption during the orbit transfer. The two-stage orbit transfer strategy is given to save fuel for maneuvering, which also determines the directed graph between target orbits of satellites or debris. The stochastic expectation model is to get the service sequence with the optimal average benefit, while the robust model considers the worst situation and gives the optimal minimum benefit. Simulations of GEO satellites in orbit are conducted to demonstrate the impact of various models on handling uncertain tasks.

Incentive Mechanism for Uncertain Tasks Under Differential Privacy

Incentive Mechanism for Uncertain Tasks Under Differential Privacy

ACCEPT OR REJECT? WHAT DO ACADEMICS THINK ABOUT UTILISING CHATGPT PUBLICATIONS?

The Chat Generative Pre-Trained Transformer (ChatGPT), a chatbot with artificial intelligence, made its debut in the United States in 2022. The platform created by incorporating human feedback has garnered millions of interactions. ChatGPT can generate a response autonomously, drawing from a vast pool of online sources and frequently without requiring additional input from the user. It has been reported that individuals have utilised ChatGPT to generate academic essays and scholarly publications. AI tools utilising extensive language models, such as ChatGPT, heavily rely on pre-existing textual data available on the internet. Consequently, ensuring their results’ authenticity, credibility, and precision poses a challenging and uncertain task. This study aims to examine the viewpoints of scholars holding various academic titles and affiliated with universities located in diverse regions of Turkey regarding the utilisation of ChatGPT in scholarly publications. A total of 121 academicians participated in the study, and data were gathered in both quantitative and qualitative formats. The findings have indicated that while scholars did not perceive any issue with incorporating ChatGPT into their manuscript drafting procedures, they remained hesitant to embrace this technology by establishing an academic persona and collaborating on scholarly works.

A novel approach for calculating single-source shortest paths of weighted digraphs based on rough sets theory.

Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability of investigating graph theory problems. Recently, some efficient RST approaches for discovering different subgraphs (e.g. strongly connected components) have been presented. This work was devoted to discovering SSSPs of weighted digraphs by aid of RST. First, SSSPs problem was probed by RST, which aimed at supporting the fundamental theory for taking RST approach to calculate SSSPs from weighted digraphs. Second, a heuristic search strategy was designed. The weights of edges can be served as heuristic information to optimize the search way of $ k $-step $ R $-related set, which is an RST operator. By using heuristic search strategy, some invalid searches can be avoided, thereby the efficiency of discovering SSSPs was promoted. Finally, the W3SP@R algorithm based on RST was presented to calculate SSSPs of weighted digraphs. Related experiments were implemented to verify the W3SP@R algorithm. The result exhibited that W3SP@R can precisely calculate SSSPs with competitive efficiency.

A new perspective on classification: Optimally allocating limited resources to uncertain tasks

A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain. Typically, such problems are solved using a classification framework, where task outcomes are predicted given a set of characteristics. Then, resources are allocated to the tasks predicted to be the most likely to succeed. We argue, however, that using classification to address task uncertainty is inherently suboptimal as it does not take into account the available capacity. We present a novel solution that directly optimizes the assignment's expected profit given limited, stochastic capacity. This is achieved by optimizing a specific instance of the net discounted cumulative gain, a commonly used class of metrics in learning to rank. We demonstrate that our new method achieves higher expected profit and expected precision compared to a classification approach for a wide variety of application areas.

Dynamic Job-Shop Scheduling Based on Transformer and Deep Reinforcement Learning

The dynamic job-shop scheduling problem is a complex and uncertain task that involves optimizing production planning and resource allocation in a dynamic production environment. Traditional methods are limited in effectively handling dynamic events and quickly generating scheduling solutions; in order to solve this problem, this paper proposes a solution by transforming the dynamic job-shop scheduling problem into a Markov decision process and leveraging deep reinforcement learning techniques. The proposed framework introduces several innovative components, which make full use of human domain knowledge and machine computing power, to realize the goal of man–machine collaborative decision-making. Firstly, we utilize disjunctive graphs as the state representation, capturing the complex relationships between various elements of the scheduling problem. Secondly, we select a set of dispatching rules through data envelopment analysis to form the action space, allowing for flexible and efficient scheduling decisions. Thirdly, the transformer model is employed as the feature extraction module, enabling effective capturing of state relationships and improving the representation power. Moreover, the framework incorporates the dueling double deep Q-network with prioritized experience replay, mapping each state to the most appropriate dispatching rule. Additionally, a dynamic target strategy with an elite mechanism is proposed. Through extensive experiments conducted on multiple examples, our proposed framework consistently outperformed traditional dispatching rules, genetic algorithms, and other reinforcement learning methods, achieving improvements of 15.98%, 17.98%, and 13.84%, respectively. These results validate the effectiveness and superiority of our approach in addressing dynamic job-shop scheduling problems.

Reinforcement learning algorithms: A brief survey

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential decision-making in complex problems. RL is inspired by trial-and-error based human/animal learning. It can learn an optimal policy autonomously with knowledge obtained by continuous interaction with a stochastic dynamical environment. Problems considered virtually impossible to solve, such as learning to play video games just from pixel information, are now successfully solved using deep reinforcement learning. Without human intervention, RL agents can surpass human performance in challenging tasks. This review gives a broad overview of RL, covering its fundamental principles, essential methods, and illustrative applications. The authors aim to develop an initial reference point for researchers commencing their research work in RL. In this review, the authors cover some fundamental model-free RL algorithms and pathbreaking function approximation-based deep RL (DRL) algorithms for complex uncertain tasks with continuous action and state spaces, making RL useful in various interdisciplinary fields. This article also provides a brief review of model-based and multi-agent RL approaches. Finally, some promising research directions for RL are briefly presented.

Post-COVID-19 Pandemic Era and Sustainable Healthcare: Organization and Delivery of Health Economics Research (Principles and Clinical Practice)

Health services research aims to improve population health by studying the organization, delivery, and financing of healthcare services. While the field has made progress in defining its boundaries and core research topics, our literature survey revealed a lack of attention given to the management, cost, and policy aspects of healthcare systems (SCs). Moreover, the readiness of the system to handle supply policy and device deficiencies, especially during the pandemic, was rarely mentioned. Unplanned urban growth, characterized by reduced open spaces, deteriorating infrastructure, and changes in biological morphology, has led to an uneven distribution of urban amenities, facilities, and healthcare services. This research proposes two reliable models for site selection in a major hospital in Hong Kong, considering uncertainty levels, infeasibility tolerance, and reliability. We examine two categories of uncertainty—symmetric and bounded—and provide a solution with a nominal objective function value of 121.37. By considering 23 uncertain parameters with specific tolerance levels, we extend the processing time of uncertain tasks to ensure feasibility. However, the objective function value decreases due to violations of intermediate due-dates and decreased overall production. A comparative analysis is presented to evaluate the solution and address scheduling challenges under uncertainty using a specified probability distribution function. The study concludes by introducing justice and health problems, outlining four typical strategies, and emphasizing the importance of the effective management of systems, components, and procedures for the production, distribution, and administration of medications and healthcare supplies. This research contributes to fairness in health systems and population health at local, national, and global levels, addressing health inequity and promoting public dialogues on the subject.

Sensitivity Analysis for Dynamic Control of PSTNs with Skewed Distributions

Probabilistic Simple Temporal Networks (PSTN) facilitate solving many interesting scheduling problems by characterizing uncertain task durations with unbounded probabilistic distributions. However, most current approaches assess PSTN performance using normal or uniform distributions of temporal uncertainty. This paper explores how well such approaches extend to families of non-symmetric distributions shown to better represent the temporal uncertainty introduced by, e.g., human teammates by building new PSTN benchmarks. We also build probability-aware variations of current approaches that are more reactive to the shape of the underlying distributions. We empirically evaluate the original and modified approaches over well-established PSTN datasets. Our results demonstrate that alignment between the planning model and reality significantly impacts performance. While our ideas for augmenting existing algorithms to better account for human-style uncertainty yield only marginal gains, our results surprisingly demonstrate that existing methods handle positively-skewed temporal uncertainty better.

Implications of using the clinical target distribution as voxel-weights in radiation therapy optimization

Objective. Delineating and planning with respect to regions suspected to contain microscopic tumor cells is an inherently uncertain task in radiotherapy. The recently proposed clinical target distribution (CTD) is an alternative to the conventional clinical target volume (CTV), with initial promise. Previously, using the CTD in planning has primarily been evaluated in comparison to a conventionally defined CTV. We propose to compare the CTD approach against CTV margins of various sizes, dependent on the threshold at which the tumor infiltration probability is considered relevant. Approach. First, a theoretical framework is presented, concerned with optimizing the trade-off between the probability of sufficient target coverage and the penalties associated with high dose. From this framework we derive conventional CTV-based planning and contrast it with the CTD approach. The approaches are contextualized further by comparison with established methods for managing geometric uncertainties. Second, for both one- and three-dimensional phantoms, we compare a set of CTD plans created by varying the target objective function weight against a set of plans created by varying both the target weight and the CTV margin size. Main results. The results show that CTD-based planning gives slightly inefficient trade-offs between the evaluation criteria for a case in which near-minimum target dose is the highest priority. However, in a case when sparing a proximal organ at risk is critical, the CTD is better at maintaining sufficiently high dose toward the center of the target. Significance. We conclude that CTD-based planning is a computationally efficient method for planning with respect to delineation uncertainties, but that the inevitable effects on the dose distribution should not be disregarded.

Optimizing workforce allocation under uncertain activity duration

Even though warehouses are becoming increasingly automated, humans remain their central and most important resource. Every day, various activities must be carried out by workers. The assignment of individual workers to specific tasks has a major impact on the overall efficiency of a warehouse. The problem of finding an efficient assignment is not trivial and is complicated by task durations being unknown in advance, operational constraints, and the fact that employee well-being must be taken into consideration to maintain employee satisfaction. The method proposed in this paper uses work profiles: ordered lists of task properties such as type and work zone. Each worker is assigned exactly one profile and tasks are dynamically allocated to workers based on their profile. Finding a good profile assignment is crucial, yet the profiles are usually assigned manually by shift supervisors. This paper proposes a framework for automating the assignment of profiles to employees under uncertain task durations. The proposed approach applies a metaheuristic algorithm together with discrete-event simulation to evaluate the quality of a solution. The simulation component is used to address uncertainty of the task durations either by considering multiple scenarios or by using mean task durations. Possible values for task durations originate from distributions which are assumed to be given or learned from historical data. The contributions of this paper are threefold: (1) we propose a profile-assignment framework that deals with uncertainty of the task durations, (2) we study the trade-offs between run time and accuracy within this framework, and (3) we analyze our main design decisions and demonstrate how our method outperforms reconstructed solutions produced by a human expert.

Under which circumstances are enabling control and control extensiveness related to employee performance?

We investigate circumstances that influence the relation between formal controls and performance of operational employees. Building on Adler and Borys (1996), we distinguish enabling control from control extensiveness (few versus many controls). We examine whether and how the relationships of enabling control and control extensiveness with employee performance are moderated by task routineness and control preferences for both enabling control and control extensiveness. Using survey data from 517 employee-manager pairs, we find that enabling control is positively related to the performance of operational employees, especially for uncertain tasks. Contrary to our expectations, employee preference for enabling control does not moderate the relationship between enabling control and employee performance. Control extensiveness is negatively related to performance, but less negatively for uncertain tasks and for employees who prefer extensive controls.

Swarm Game and Task Allocation for Autonomous Underwater Robots

Although underwater robot swarms have demonstrated increasing application prospects, organizing and optimizing the swarm’s scheduling for uncertain tasks are challenging. Thus, we designed robot games and task allocation experiments, where the robots have different cooperative attributes, as some are more selfish and others more altruistic. Specifically, we designed two experiments: target search and target moving, aiming to reveal the relationship between individual cooperation and group task achievement in a robot swarm as a collaborative strategy. The task information is shared among the robots, because performing the tasks consumes a certain amount of energy, reducing the robot’s running speed. Our experiments prove that the robot swarms can evolve and enhance their strategies during the game, and will guide guiding future works in designing more efficient robot swarms.

Predicting Rainfall Using Machine Learning Techniques

Rainfall prediction is one of the challenging and uncertain tasks which has a significant impact on human society. Timely and accurate predictions can help to proactively reduce human and financial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.

Uncertainty-aware Task Offloading for Multi-vehicle Perception Fusion over Vehicular Edge Computing

Multi-vehicle perception fusion is an emerging advanced vehicular application providing Vehicle Users (VUs) with comprehensive driving assistance. This involves each VU's individual vehicular perception tasks and additional fusion tasks at the end. However, performing these perception fusion applications on some VUs may not be feasible due to the applications' high computing complexity. Vehicular Edge Computing (VEC) server, which is collocated with a Road-side Unit, together with VUs' Vehicular Local Computing (VLC) units can be used to support these perception fusion applications through task offloading. Achieving this edge-collaborated perception fusion with task offloading involves multiple challenges: (i) frequently varying uplink channel conditions, (ii) sequential and parallel task dependencies, and (iii) uncertain task composition induced by unknown object detection outputs in the dynamic driving environment. In this paper, a real-time mechanism is proposed to minimize the end-to-end delay of an edge-collaborated multi-vehicle perception fusion application by addressing these challenges. Based on the real-time channel conditions, VEC and VLC server capacities, and number of detected objects, this algorithm jointly determines the bandwidth allocation, task partitioning, offloading, and execution scheduling for all the involved tasks of the multi-vehicle perception fusion application. Numerical results show that the proposed approach can significantly reduce the end-to-end fusion application latency compared to existing techniques.

DRL-OS: A Deep Reinforcement Learning-Based Offloading Scheduler in Mobile Edge Computing.

Hardware bottlenecks can throttle smart device (SD) performance when executing computation-intensive and delay-sensitive applications. Hence, task offloading can be used to transfer computation-intensive tasks to an external server or processor in Mobile Edge Computing. However, in this approach, the offloaded task can be useless when a process is significantly delayed or a deadline has expired. Due to the uncertain task processing via offloading, it is challenging for each SD to determine its offloading decision (whether to local or remote and drop). This study proposes a deep-reinforcement-learning-based offloading scheduler (DRL-OS) that considers the energy balance in selecting the method for performing a task, such as local computing, offloading, or dropping. The proposed DRL-OS is based on the double dueling deep Q-network (D3QN) and selects an appropriate action by learning the task size, deadline, queue, and residual battery charge. The average battery level, drop rate, and average latency of the DRL-OS were measured in simulations to analyze the scheduler performance. The DRL-OS exhibits a lower average battery level (up to 54%) and lower drop rate (up to 42.5%) than existing schemes. The scheduler also achieves a lower average latency of 0.01 to >0.25 s, despite subtle case-wise differences in the average latency.

Task uncertainty on the relationship between social hierarchy and leadership emergence

Self-managing teams have gained popularity, yet those teams in practice often have limited formal hierarchical differentiation, formal order, or imposed coordination. Social hierarchy has functions of establishing order and facilitating coordination, and it plays important roles in teams where team members autonomously interact and manage tasks. Two important forms of social hierarchy, status and power, critically affect how individuals on a team emerge as leaders, and thus help self-managing teams’ coordination. In the current study, two questions are explored: 1) How do informal status and power affect leadership emergence in project teams? 2) How do team task types affect the relationship between informal status/power and leadership emergence in project teams? A field study was conducted in a consulting firm in South Korea. The results show that the high-status individuals are most likely to emerge as leaders of project teams, and that the relationship between status and leadership emergence is stronger in teams dealing with highly uncertain tasks.