Simulation-based joint optimization for identifying optimal task allocation strategies to enhance order picking efficiency in robot mobile fulfillment systems

To address the issues of unbalanced residual energy caused by heterogeneous initial robot states and dynamic environmental disturbances, this paper proposes a dynamic task allocation and rescheduling strategy considering energy balance. A Multiple Traveling Salesman Problem (MTSP) mathematical model that incorporates energy constraints and load balancing is established. Furthermore, an Improved Genetic Algorithm (IGA) based on K-Means initialization and adaptive mutation strategies is proposed. By introducing an energy-aware operator, the algorithm achieves energy consumption balance within the robot swarm while optimizing the total path length. In addition, an event-triggered dynamic rescheduling mechanism is designed. When sudden robot failures or task updates are detected, a Local Greedy Insertion (LGI) strategy is activated to achieve rapid task takeover and reallocation. Experimental results show that the proposed IGA consistently reduces the system’s state of charge (SoC) range to less than 1%, significantly outperforming baseline algorithms. It strikes an excellent balance between solution accuracy and computational time overhead. Finally, by simulating sudden new tasks and robot failure scenarios, the effectiveness of the dynamic rescheduling mechanism is verified, ensuring the timeliness and high robustness of the system.

Vehicular Edge Cloud Computing (VECC) has emerged as a promising paradigm to support delay-sensitive and computation-intensive applications in Intelligent Transportation Systems (ITS). However, dynamic traffic patterns, fluctuating network conditions, and uncertain resource availability often result in high task latency and service failures. To address these challenges, this paper proposes a bi-level Deep Q-Network (DQN)-based mobility-aware framework for fault-tolerant task offloading in VECC environments. Unlike existing approaches that offload tasks solely to the receiving Roadside Unit (RSU), the proposed framework introduces a level-1 DQN agent that performs high-level scheduling by selecting the most suitable RSU for task execution based on its workload, network latency, and failure rate. In parallel, level-2 DQN agents at each RSU handle low-level decisions, including task allocation and failure-recovery strategy selection, choosing among First Result, Recovery Block, or Retry mechanisms. To eliminate centralized dependency, the level-1 DQN is replicated across RSUs at the edge layer, ensuring high accessibility and resilience for distributed scheduling. Extensive simulations conducted using an integrated SimPy/SUMO environment demonstrate that, under heavy and imbalanced traffic, the proposed bi-level DQN improves the total reward by 7.7% to 37.8% and reduces the task failure rate by 29% to 63% relative to bi-level PPO, Greedy, and No-Forwarding baselines, based on averages over the final 40 training episodes.

The integration of collaborative robots (cobots) into industrial environments necessitates advanced task allocation strategies to optimize performance and enhance worker satisfaction. Traditional static task allocation methods often fall short in adapting to dynamic operational conditions and addressing the cognitive load on human operators. This study introduces and evaluates a novel adaptive rescheduling mechanism incorporating a neural network-based predictive model. The aim is to address these limitations. The experimental campaign, involving the assembly and disassembly of a multi-component box, tested the system’s effectiveness in real-world scenarios. Results indicate that the adaptive rescheduling mechanism significantly reduced the makespan compared to static allocation methods. This demonstrates the improved operational efficiency. Additionally, human factors were positively impacted, with a notable reduction in participant frustration as measured by the NASA-TLX questionnaire. These findings highlight the potential of predictive analytics in optimizing task allocation, suggesting that adaptive rescheduling mechanisms not only enhance productivity but also contribute to a more supportive and manageable work environment. This research underscores the value of integrating advanced predictive techniques into human-robot collaboration systems and offers a foundation for further exploration and refinement of such approaches to improve both performance and worker well-being in industrial settings.

The exponential growth in demand for data storage and computing has led to a rapid expansion in the energy consumption and carbon emissions of data centers (DCs). Hybrid energy systems that integrate renewable energy sources are regarded as a sustainable and low-carbon solution for powering the DCs. This study proposes an optimal cooperation scheduling strategy for the hybrid energy system powering the DC and electric vehicles (EVs). The strategy is based on load transferring and operates within a carbon trading mechanism, explicitly addressing the coupling between computational loads and power consumption. An optimization model is constructed that considers economic objectives, including operational cost and a stepped carbon trading cost, to obtain optimal energy dispatch and computational task allocation strategies. This framework ensures the economic interests of EVs’ owners while satisfying the energy demands of both the DC and the EVs. The results of a case study based in Shanghai demonstrate that the proposed hybrid energy system with multiple sources has significant economic and environmental advantages in spite of operational complexity. Furthermore, the collaborative strategy further enhances the cost reduction and carbon emission reduction. Specifically, the cooperative strategy achieves a 5.21% reduction in total cost compared to Case 1 (without V2G) and a 22.80% reduction compared to Case 2 (without computing task transferring). By adopting the optimal scheduling solution, carbon emissions can be reduced by 16.74% relative to Case 1 while remaining at a level comparable to Case 2. Furthermore, the impact of the carbon trading mechanism on the system’s cost and carbon emissions is analyzed. The results indicate that while a stricter carbon trading mechanism leads to an increase in the total cost, it also results in a reduction in carbon emission from the DC’s hybrid energy system.

IntroductionRapid advancements in artificial intelligence (AI) have ushered in an era of hyperautomation and intelligent orchestration across multiple engineering domains, with healthcare emerging as one of the most impactful application areas. Among recent developments, Agentic AI has gained attention as a sub-domain of AI capable of autonomous operation, decision-making, and goal-driven behavior with minimal human intervention. This study aims to explore the architectural and functional role of Agentic AI in modern healthcare systems.MethodsThe study adopts a conceptual and analytical approach to examine the core components of Agentic AI, including agent design, decision-making mechanisms, task allocation strategies, agent coordination, and ranking frameworks. It further investigates the integration of emerging 6G networking technologies within Agentic AI architectures. A qualitative case study on remote robotic surgery is presented to illustrate practical applicability. Additionally, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is conducted to assess strategic and operational considerations.ResultsThe analysis demonstrates that Agentic AI architectures, when supported by high-speed and low-latency 6G communication, can enable efficient autonomous decision-making and coordinated task execution in complex healthcare workflows. The case study highlights the feasibility of Agentic AI in enabling remote robotic surgery with enhanced responsiveness, precision, and reliability. The SWOT analysis reveals strong potential for scalability and efficiency while also identifying challenges related to ethical governance, system robustness, and security.DiscussionThe findings suggest that Agentic AI represents a promising paradigm for next-generation healthcare systems, particularly in remote and critical care applications. While the proposed framework offers architectural insights and strategic value, responsible integration requires addressing limitations such as trust, regulatory compliance, and system transparency. Overall, this study provides a holistic understanding of how Agentic AI can be effectively and ethically integrated into healthcare ecosystems.

Purpose This study aims to systematically explore the evolution of job crafting, a proactive behavior enabling employees to modify their tasks, environments, and interactions to align with personal skills and interests. By analyzing publication trends, theoretical frameworks, and contextual factors, this research aims to develop a comprehensive understanding of job crafting, wherein individuals redesign their job roles through physical and cognitive changes. Design/methodology/approach A systematic review of 85 articles published between 2014 and 2024 was conducted using the Scopus database. The Scientific Procedures and Rationales for Systematic Literature Review (SPAR-4 SLR) approach and Theory, Context, Characteristics, Methodology (TCCM) framework guided the analysis, enabling a detailed evaluation of methodologies and practical implications. Findings The review highlights job crafting as a significant mechanism for improving employee well-being and job satisfaction by enabling better alignment between individuals and their roles. It underscores its importance in helping employees adapt to dynamic work environments, mitigating job misfits, and fostering engagement. Practical implications include strategies for recruitment, job design, and task allocation to enhance employees’ performance and productivity. Originality/value This study uniquely synthesizes a decade of job crafting literature, offering actionable insights for organizations to promote employee-driven job redesigning. By addressing theoretical and methodological gaps, it sets a research agenda for future studies, emphasizing the importance of job crafting in modern workplaces for aligning organizational goals with employees’ strengths and preferences.

Human–Robot Collaboration (HRC) has gained increasing attention as it expands from industrial environments to service-oriented settings, where dynamic conditions and diverse operational objectives pose significant challenges for task allocation. Unlike controlled industrial environments, service contexts are characterized by frequent changes, uncertainty, and time-varying priorities, rendering static task allocation strategies ineffective. This paper proposes a method to address the problem of determining the optimal balance between human and robotic task allocation in dynamic service-oriented HRC systems. A preference-controllable multi-objective deep reinforcement learning framework is introduced to formulate task allocation as a dynamic, preference-dependent decision-making process. The proposed approach explicitly captures trade-offs among multiple, potentially conflicting objectives and enables adaptive task allocation under changing operational conditions and service priorities. The framework is evaluated through simulation-based experiments and comparative analysis with baseline strategies using multiple evaluation metrics, complemented by additional validation using external datasets. Experimental results demonstrate the effectiveness and adaptability of the proposed approach across varying preference configurations and workload conditions, supporting its applicability in real-world smart service environments.

Inclusion of Disabled Workers in Production Environments through Industrial Collaborative Robots: a Company Case Study

Assigning tasks to reliable workers to obtain reliable data is a critical issue in Mobile CrowdSensing (MCS). The challenge is compounded by the problem of Information Elicitation Without Verification (IEWV), which renders traditional data quality evaluation methods ineffective. While some studies attempt to address this, they often struggle to assess workers’ dynamic trustworthiness, resulting in unreliable data. To overcome these challenges, we propose the Trust and Time-sharing Task Allocation based Truth Discovery (TTTA-TD) scheme, designed to ensure reliable data collection in MCS. This scheme includes three components: (a) Classification-based Trust Evaluation (CTE) that classifies workers based on behavior and applies tailored penalties—lenient for honest workers and stricter for malicious ones, (b) Trust-based Truth Data Discovery (TTDD), which improves truth data accuracy by integrating trust scores, and (c) Trust and Time-sharing Task Allocation (TTTA) which allocates tasks to ensure data reliability and minimize time-sharing disparities. Experimental results show that the TTTA algorithm reduces average time-sharing by 93.95%. The TTDD algorithm improves truth estimates across all dataset qualities, and the TTTA-TD scheme enhances data reliability by 0.35%, 2.06%, and 7.41% in high, medium, and low-quality datasets respectively.

Artificial intelligence (AI) is becoming increasingly integrated into aviation, transforming numerous operations, including those performed at the flight deck. This paper explores theoretical approaches to optimizing human-AI teamwork in the context of aviation, such as trust and role clarity, to enhance safety and efficiency. It also outlines strategies for effective task allocation in human-AI teams using a previously developed conceptual model. By applying teamwork and cognitive principles, such as situation awareness, it examines the complementary strengths of humans and AI, and addresses how AI can serve both as a tool and as a collaborative teammate in aviation contexts. This paper evaluates human strengths, such as adaptive decision-making and AI capabilities, including real-time data processing, alongside shared limitations like fatigue and inflexibility. It discusses the risks of over-reliance on AI, reduced situational awareness, and cybersecurity threats. Best practices for fostering trust, clear roles, and interdependence are presented, drawing from Crew Resource Management (CRM) principles. This work extends human factors research by applying a novel theoretical framework to human-AI collaboration in aviation. Unlike prior studies focused solely on technological advances, it provides actionable insights for task allocation, risk mitigation, and training, supporting balanced and effective human-AI teams in the flight deck.

The growth of intelligent manufacturing systems has led to a wealth of computation-intensive tasks with complex dependencies. These tasks require an efficient offloading architecture that balances responsiveness and energy efficiency across distributed computing resources. Existing task offloading approaches have fundamental limitations when simultaneously optimizing multiple conflicting objectives while accommodating hierarchical computing architectures and heterogeneous resource capabilities. To address these challenges, this paper presents a cloud–fog hierarchical collaborative computing (CFHCC) framework that features fog cluster mechanisms. These methods enable coordinated, multi-node parallel processing while maintaining data sensitivity constraints. The optimization of task distribution across this three-tier architecture is formulated as a multi-objective problem, minimizing both system latency and energy consumption. To solve this problem, a fractal-based multi-objective optimization algorithm is proposed to efficiently explore Pareto-optimal task allocation strategies by employing recursive space partitioning aligned with the hierarchical computing structure. Simulation experiments across varying task scales demonstrate that the proposed method achieves a 20.28% latency reduction and 3.03% energy savings compared to typical and advanced methods for large-scale task scenarios, while also exhibiting superior solution consistency and convergence. A case study on a digital twin manufacturing system validated its practical effectiveness, with CFHCC outperforming traditional cloud–edge collaborative computing by 12.02% in latency and 11.55% in energy consumption, confirming its suitability for diverse intelligent manufacturing applications.

Abstract This article introduces a psychologically grounded framework to describe cognitive demands in the teleoperation of highly automated agents. It builds on established models of information processing, situation awareness, and occupational stress to explain how remote operators perceive, process, and act upon task demands in dynamic environments. Three scenarios from ground-based transportation illustrate varying operational contexts and support the identification of objective task demands. The framework highlights key human factors such as attention, working memory, and situation understanding, while also accounting for individual differences in cognitive resources. It provides a theoretical foundation for future empirical studies and supports the human-centered design of adaptive teleoperation workplaces. Practical Relevance : The framework supports the design and evaluation of teleoperation workplaces by identifying cognitive demands across diverse scenarios. It provides guidance for designing adaptive interfaces and task allocation strategies in event-driven, safety-critical environments that feature frequent context switches and uncertain sensor-based information. It helps practitioners and system designers to address the specific challenges of teleoperation as a complex socio-technical system.

Coordinating robotic teams across multiple concurrent search tasks is a critical challenge in search and rescue operations. This work presents a new multi-agent framework designed to manage and optimize search efforts when several missing-person reports occur in parallel. The method extends iso-probability curve-based trajectory planning to the multi-target case and introduces a dynamic task allocation scheme that distributes search agents (e.g., UAVs) across tasks according to evolving probabilities of success. Overlapping search regions are explicitly resolved to eliminate duplicate coverage and to ensure balanced effort among tasks. The framework also extends the behavior-based motion prediction model for missing persons and the non-parametric estimator for iso-probability curves to capture more realistic search conditions. Extensive simulated experiments, with multiple concurrent tasks, demonstrate that the proposed method tangibly improves mean detection times compared with equal-allocation and individual static assignment strategies.

This study proposes a task planning approach for a distributed constellation dedicated to space target monitoring, grounded in an adaptive genetic algorithm. The approach is designed to address challenges such as the growing number of space targets and the complex constraints inherent in space target monitoring activities. After reviewing the research progress of distributed satellite task planning and adaptive genetic algorithms, a distributed task model featuring master-slave satellites was developed. This model integrates multi-constraint modeling and aims to optimize key performance indicators: task yield rate, task completion rate, resource utilization rate, and load balancing. To enhance the approach, the contract net algorithm is fused with the adaptive genetic algorithm: Firstly, in the tendering phase, centralized tendering is adopted to reduce communication overhead; Secondly, in the bidding phase, improved genetic mechanisms (e.g., dynamic reverse adjustment of crossover and mutation probabilities) and a dynamic population strategy are employed to generate task allocation schemes; Thirdly, in the bid evaluation and winning phase, differentiated strategies are applied to non-repetitive and repetitive tasks. Simulation validation shows that this approach can complete 80% of space target monitoring tasks, balance satellite loads effectively, and manage space target catalogs efficiently.

This study investigates how various task allocation strategies and communication modalities influence Human-AI Team performance in dynamic dual-task environments. Utilizing a modified Ballas Task simulator, the research involved 32 participants with military or similar backgrounds to evaluate four task allocation strategies – Operator Monitoring, Action Split, Target Split, and Take Over. These strategies were assessed under different levels of transparency and communication modes while participants engaged in concurrent tracking and tactical assessment tasks. The results indicate that the effectiveness of task allocation is contingent upon both transparency and the communication modality employed. Operator monitoring consistently reduced workload and assessment time, while enhancing tracking performance, target accuracy, and situational awareness – particularly when paired with verbal communication under low transparency. In contrast, non-verbal communication improved situational awareness and reduced tracking errors in the action split and target split strategies. Although take over yielded fewer advantages compared to the other strategies, it still surpassed the performance of an all-human baseline. These findings provide actionable insights for enhancing human-AI collaboration in high-stakes, time-sensitive contexts, with relevance extending beyond military operations to a variety of dynamic dual-task environments.

Aiming at the collaborative optimization of energy efficiency and path planning for automated guided vehicles (AGV), a multi-mode collaborative optimization model is proposed to enhance system-level, energy-time, trade-offs and dynamic adaptability. Based on hierarchical reinforcement learning and dynamic task allocation strategy, a dynamic task allocation strategy is developed to reduce idle loss, which combines the enhanced ant colony optimization (ACO) and entropy-based weighting method to optimize energy consumption of the path. Compared with the benchmark genetic algorithm method, simulation results show that the energy consumption of AGV is reduced by 21.3% (p<0.05), and the task response time of AGV is shortened by 17.8% (p<0.05), to verify the effectiveness of the multi-mode collaborative optimization model. The proposed method provides theoretical support for the quantity configuration and path optimization design of AGV in intelligent manufacturing scenarios, avoiding path conflict-induced efficiency loss observed in high-density deployments.

In this paper, an optimization method based on heuristic genetic algorithm is proposed for AGV multi-objective dynamic balanced scheduling. Firstly, the warehouse environment is constructed by raster modeling method, and the AGV path planning is transformed into a node traversal situation in a two-dimensional coordinate system, and the spatial relationship of multiple elements is defined. Then, a single-objective constraint model is established to minimize the longest path AGV picking time as the objective, and the objective function is constructed by combining the Manhattan distance and task processing time. Then the ant colony algorithm is introduced to solve the path planning, and the optimization efficiency of the algorithm is improved by dynamically adjusting the parameters. Further, a multi-objective scheduling model is constructed, taking into account the three indexes of total path length, task load balance and maximum time consumption, and a genetic algorithm is used to solve the problem. The task allocation strategy is optimized through a series of operations, and the fitness function is designed to achieve multi-objective transformation. This research provides theoretical support and practical reference for the efficient scheduling of unmanned warehousing system.

Task allocation for Unmanned Aerial Vehicle (UAV) swarms is a complex challenge. Effective distribution of tasks among multiple UAVs necessitates the optimization of conflicting objectives, such as task balance, flight distance, cost, and target benefits. This multi-objective optimization problem is inherently intricate due to strong interdependencies, nonlinear relationships, and dynamic environments. Traditional methods, including heuristic algorithms, supervised machine learning, and basic reinforcement learning mechanisms, often suffer from low efficiency and strict requirements for data sources or supervision mechanisms. They also struggle to adapt to problems with large discrete action spaces. To address this challenge, this paper proposes a novel approach that integrates swarm intelligence simulation with reinforcement learning. The core of this approach is a reinforcement learning algorithm based on the Deep Deterministic Policy Gradient (DDPG) framework. A key innovation is a customized DDPG variant specifically designed to handle the large, discrete action spaces inherent in UAV task allocation. Additionally, a behavior tree model is employed to simulate UAV swarm interactions, providing a realistic and verifiable environment for evaluating task allocation strategies. This simulation generates a rich dataset for training and testing reinforcement learning algorithms. The combined approach efficiently addresses the multi-objective optimization problem, enabling optimal task distribution for numerous UAVs. Compared to existing heuristic methods, the proposed approach demonstrates superior computational efficiency and solution quality, while maintaining reasonable convergence stability and environmental adaptability. Simulation-based validation ensures the approach’s robustness in dynamic environments, a critical factor for practical applications. Overall, the integration of simulation and reinforcement learning provides a powerful framework for tackling the complex task allocation problem in UAV swarms.

In practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task Allocation (MRTA). Researchers have devised a range of methodologies to tackle MRTA problems, aiming to achieve optimal solutions, yet there remains room for further enhancements in this field. Among the complex challenges in MRTA, the identification of an optimal coalition formation (CF) solution stands out as one of the (Nondeterministic Polynomial) NP-hard problems. CF pertains to the effective coordination and grouping of agents or robots for efficient task execution, achieved through optimal task allocation. In this context, this paper delivers a succinct overview of dynamic task allocation and CF strategies. It conducts a comprehensive examination of diverse strategies employed for MRTA. The analysis encompasses the advantages, disadvantages, and comparative assessments of these strategies with a focus on CF. Furthermore, this study introduces a novel classification system for prominent task allocation methods and compares these methods with simulation analysis. The fidelity and effectiveness of the proposed CF approach are substantiated through comparative assessments and simulation studies.