Dynamic Priority Research Articles

AGV path planning based on dynamic priority method and environmental weight A-star

In order to plan the shortest path and optimise the search speed, an environmental weight A-star (A*) algorithm is proposed in the paper. This method dynamically adjusts the weights based on the ratio of environmental obstacles at each search, which makes the search time shorter and more stable. To optimise the conflict resolution, the local multi-direction method is introduced on the basis of the one-way map method. This method sets a set of travelling direction rules for the road, combined with the path-finding algorithm, it can get the path that conforms to the rules. The conforming path effectively avoids the opposite direction conflict, and eliminates detour problem. At the same time, a dynamic priority method based on traffic prediction is proposed to solve node conflicts. The method dynamically assigns the priority of AGVs in a node conflict based on the predicted traffic flow. It can improve the conflict resolution efficiency during localised congestion. The results suggest the environmental weight A* algorithm is faster and more stable in searching.

Artificial Neural Network-Based Data-Driven Parameter Estimation Approach: Applications in PMDC Motors

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models’ precision and their controllers’ effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems.

Robotic warehouse systems considering dynamic priority

The research proposes a new methodological framework based on dynamic priority to handle different order classes in robotic warehouse systems. Traditional static priority methods in facility logistics may cause low-priority orders to experience excessive delays and fail to ensure fairness. Our dynamic priority approach addresses this fairness issue by adjusting priorities over time to fulfill orders within promised times, ensuring both high-priority orders and long-waiting low-priority orders receive timely attention. We present stochastic models of dynamic priority queueing networks to describe warehouse systems and estimate throughput times. Experiments validate the analytical stochastic models, and experimental results indicate that the dynamic priority model achieves shorter delay times than the static priority model and the FCFS model. We propose design insights based on experimental results and provide an approach to select the optimal robot number. Furthermore, by employing a fairness index, we develop a new decision support tool for determining warehouse configurations with requested performance objectives. Experimental results demonstrate that dynamic priority can ensure fairness across a wider range of scenarios. Additionally, with insufficient pickers, the system performs better with the put wall than without it.

A dynamic priority bus lane strategy in heterogeneous traffic environments

Allowing connected and automated vehicles (CAVs) to drive in bus lanes can enhance the utilization efficiency of exclusive bus lanes. However, this could also lead to a certain degree of heightened bus operating delays. In a connected and automated environment, vehicle-to-vehicle (V2V) communication is essential for implementing dynamic management strategies for bus lanes. This study focuses on urban arterial roads as the research scenario. Based on the characteristics of the car-following and lane-changing behaviors of human-driven vehicles (HDVs) and CAVs, a simulation platform for heterogeneous traffic flow is established. Four strategies for bus lane control are proposed: exclusive bus lanes (EBLs), bus and CAV mixed lanes (BCMLs), dynamic bus lanes (DBLs), and dynamic priority bus lanes (DPBLs). Through simulation experiments, an analysis is conducted to compare traffic operation characteristics under different bus lane strategies, assess the advantages of these strategies, and determine the traffic density conditions for their implementation. The results indicate that in comparison to the EBL strategy, the other strategies enhance traffic flows but result in different levels of travel delays for buses. Compared to the DBL and BCML strategies, the DPBL strategy is applicable under a broader range of traffic density conditions. Additionally, sensitivity analysis revealed that the effects of the V2V communication distance and bus flows on the applicability of DPBL strategies are relatively minor. These results provide a theoretical basis and practical guidance for future bus lane management.

Multi-Agent Collaborative Path Planning Algorithm with Multiple Meeting Points

Traditional multi-agent path planning algorithms often lead to path overlap and excessive energy consumption when dealing with cooperative tasks due to the single-agent-single-task configuration. For this reason, the “many-to-one” cooperative planning method has been proposed, which, although improved, still faces challenges in the vast search space for meeting points and unreasonable task handover locations. This paper proposes the Cooperative Dynamic Priority Safe Interval Path Planning with a multi-meeting-point and single-meeting-point solving mode switching (Co-DPSIPPms) algorithm to achieve multi-agent path planning with task handovers at multiple or single meeting points. First, the initial priority is set based on the positional relationships among agents within the cooperative group, and the improved Fermat point method is used to locate multiple meeting points quickly. Second, considering that agents must pick up sub-tasks or conduct task handovers midway, a segmented path planning strategy is proposed to ensure that cooperative agents can efficiently and accurately complete task handovers. Finally, an automatic switching strategy between multi-meeting-point and single-meeting-point solving modes is designed to ensure the algorithm’s success rate. Tests show that Co-DPSIPPms outperforms existing algorithms in 1-to-1 and m-to-1 cooperative tasks, demonstrating its efficiency and practicality.

The role of energy consumption in robotic mobile fulfillment systems: Performance evaluation and operating policies with dynamic priority

The robotic mobile fulfillment system (RMFS), with wide application in warehousing and logistics, requires many robots powered by electricity, which significantly impacts energy consumption. This paper investigates the energy consumption in the RMFS under a classic e-business environment, which classifies the orders into regular orders and expedited orders. We evaluate the impact of three dynamic priority policies (the earliest deadline first policy, waiting time-dependent policy, and weighted waiting time first policy) on throughput time and energy consumption. This paper proposes multi-class semi-open queuing network models (SOQN) with dynamic priority policies to investigate energy consumption. We validate the accuracy of the analytical models by simulation models. This paper makes the following contributions: (1) In methodology, we propose new methods to solve the SOQN with dynamic priority policies. (2) In operational planning and control, we are among the earliest to investigate the impact of dynamic priority policies on order throughput time and energy consumption in an RMFS. (3) In design optimization, we propose a decision tool to optimize the robot number for realizing the required throughput time with minimal energy consumption. Our model can also decide the optimal warehouse shape to minimize energy consumption. (4) In system analysis, we estimate the energy consumption per transaction in an RMFS, providing logistics managers insights into energy saving of warehouses.

Circular Economy Strategies for Business Sustainability : A Systematic Review and Meta-analysis

The relationship between corporate sustainability and circular economy (CE) methods is presented in this paper through a systematic review and meta-analysis. The importance of resource and environmental sustainability from a commercial standpoint is covered in the first section of the article, which also emphasizes the increasing interest in the circular economy among researchers. To direct the analysis, a dynamic priority quadrant research framework is presented. Using a systematic literature review and meta-analysis methodology, the study conducts an extensive examination of excellent research pieces published between 2011 and 2020. The findings highlight important concepts that have been studied in the literature and investigate the variety of supply chain, organizational, and functional management tactics that practitioners have used to put the circular economy’s tenets into practice. The study also examines how circular economy methods affect financial, social, and environmental performance metrics used in business sustainability assessments. The results provide insights into the possible advantages of switching to circular business models and management practices, and they also aid in the understanding of the relationship between CE strategies and firm performance. This study highlights the significance of sustainable business strategies based on CE principles and is in line with the research focus on the circular economy. Keywords: Circular economy, business sustainability, resource sustainability, systematic review, meta-analysis, value chains, CE business models

Prioritized experience replay based on dynamics priority

Experience replay has been instrumental in achieving significant advancements in reinforcement learning by increasing the utilization of data. To further improve the sampling efficiency, prioritized experience replay (PER) was proposed. This algorithm prioritizes experiences based on the temporal difference error (TD error), enabling the agent to learn from more valuable experiences stored in the experience pool. While various prioritized algorithms have been proposed, they ignored the dynamic changes of experience value during the training process, merely combining different priority criteria in a fixed or linear manner. In this paper, we present a novel prioritized experience replay algorithm called PERDP, which employs a dynamic priority adjustment framework. PERDP adaptively adjusts the weights of each criterion based on average priority level of the experience pool and evaluates experiences’ value according to current network. We apply this algorithm to the SAC model and conduct experiments in the OpenAI Gym experimental environment. The experiment results demonstrate that the PERDP exhibits superior convergence speed when compared to the PER.

Multi-tier scheduling algorithm of dispatching systems for urban water logging

Due to global warming, considerable amounts of storm rain have occurred, causing urban water logging and flooding. The efficient scheduling of drainage systems among pumping stations is crucial to mitigating flash flooding in urban areas. This study introduces a Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm as a proactive solution for addressing urban flooding through the optimization of drainage system discharge capacities. The algorithm's robustness is guaranteed through the integration of a multi-tier drainage system and dependency relationships. Additionally, the incorporation of an importance parameter is considered for facilitating the practical exploration of flooding risk evaluation. The proposed model was applied to simulate a drainage system in Haining City, and the results indicate that its accuracy, flexibility and reliability outperform that of existing algorithms such as fixed-priority scheduling. Moreover, the proposed approach enabled a considerable reduction in overflow loss and improved the efficiency of the sewage system. This method can improve the responses of cities to the rising problem of urban water logging.

A Dynamic Priority Rule for Congestion-Sensitive Service Systems

A Dynamic Priority Rule for Congestion-Sensitive Service Systems

Multi-priority HVAC temperature control with resource constrained based on coordinated distributed zone MPC

The conventional HVAC system for multi-area buildings includes both centralized and in-situ equipment to collectively regulate indoor air temperature to meet the preferences of its occupants. However, due to the limited capacity of the total supply air mass rate in centralized equipment, maintaining comfortable indoor air temperature for all areas can face conflicts. The first conflict arises between satisfying diverse preferences and limited global resources, while the second conflict is between centralized air supply and distributed regulation. In this paper, a distributed zone model predictive control (DZMPC) with priority conflict resolution is proposed for the multi-area HVAC systems. This strategy is composed of a zone parameter optimization with dynamic priority as a coordinated upper layer and a lower DZMPC layer. In the upper layer, a zone parameter optimization with priority is presented to coordinate sub-controllers by adjusting variable references’ bounds for DZMPC, to solve the resource competition when it occurs. In the lower layer, model predictive control with zone control is formed to mitigate the tension of the shared resource. By integrating dual decomposition and Augmented Lagrange function to address global constraints, a distributed solvable control structure is formed. The resulting optimization can be solved using a distributed primal–dual algorithm. The effectiveness of the proposed scheme is proved by simulation examples.

Boundedness and liveness enforcement for labeled Petri nets using transition priority

This paper deals with the supervisory control problem of discrete event systems modeled by labeled Petri nets. The system is originally unbounded. First, the solvability of the problem is confirmed. A necessary condition is given and proven for the existence of a feasible priority-based controller based on the notions of liveness and transition invariants. Next, a cyclic behavior graph is constructed, which shows the reachable markings that guarantee the maximum liveness of the system within a given bound vector. Finally, an on-line control strategy is proposed to enforce boundedness and liveness to the given system by appending priority relations to transitions. The dynamic priority relation changes flexibly according to the current state of the system and enforces the system evolving in a bounded and live manner. In addition, numerical examples are studied to verify the validity of the proposed approach that remains the structure of the plant net and is efficient for on-line control.

Enhancing Physician Advocacy Through Collaboration With Government Relations: Report From the Health Policy Scholars Program

ObjectiveTo explore barriers, facilitators, and benefits of collaboration between academic pediatricians and institutional offices of government relations (OGR) to enhance policy advocacy efforts. MethodsThe Academic Pediatric Associations' Health Policy Scholars met with the government relations team in their affiliated institutions as part of their experiential learning curriculum. Afterward, they submitted written reflections, which were coded and analyzed using inductive qualitative content analysis to identify key themes. ResultsReflections were completed by 21 of 23 (91.3%) participants. Most participants (76.2%) were faculty at free-standing children’s hospitals and had been at their institutions <5 years (52.3%) or 5 to 10 years (33.3%). Institutional OGR structure varied widely and not all institutions had well-defined priorities. Key themes of the reflections included that OGRs often had dynamic priorities and fiscal considerations frequently took precedence. Barriers to physician involvement with OGR are often related to difficulty identifying the correct staff contact and not having the time and support for advocacy work. Facilitators included leveraging existing relationships and collaborations, including those of peer or mentor connections to the OGR staff. Anticipated benefits to both OGR and physicians included improved knowledge of advocacy opportunities, enhanced advocacy efforts leveraging physicians’ expertise and patient stories, and message alignment and amplification of physician and institutional advocacy work. ConclusionsCollaboration between physician-advocates and institutional OGR is feasible and, with orientation and mentorship, may facilitate improved physician and institutional policy advocacy efforts. Supporting this type of collaboration may enhance physician and institutional advocacy on behalf of their shared patients and communities.

Sustainable Short-Term Production Planning Optimization

This study proposes a framework for short-term production planning of a Portuguese company operating as a tier 2 supplier in the automotive sector. The framework is intended to support the decision-making process regarding a single progressive hydraulic press, which is used to manufacture cold-stamped parts for exhaust systems. The framework consists of two sequential levels: (1) a Mixed-Integer Linear Programming (MILP) model to determine the optimal production quantities per week while minimizing the total cost; (2) a dynamic production sequencing rule for scheduling operations on the hydraulic press. The two levels are combined and implemented in Excel, where the MILP model is solved using the Solver add-in, and the second level uses the optimal production quantities as inputs to determine the production sequence using a dynamic priority rule. To validate the framework, a proposed optimal plan was compared to a real plan executed by the company, and it was found that the framework could save up to 22.1% of the total cost observed in reality while still satisfying demand. To address uncertainties, the framework requires a rolling weekly planning horizon.

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.

A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation

Modeling and control theory applied to precision agriculture irrigation systems have been essential to reduce water consumption while growing healthy crops. Specifically, implementing closed-loop control irrigation based on soil moisture measurements is an effective approach for obtaining water savings in this resource-intensive activity. To enhance this strategy, the work presented in this paper proposed a new set of water management strategies for the case in which multiple irrigation areas share a single water supply source and compared them with heuristic approaches commonly used by farmers in practice. The proposed water allocation algorithms are based on techniques used in real-time computing, such as dynamic priority and feedback scheduling. Therefore, the multi-area irrigation system is presented as a resource allocation problem with availability constraints, where water consumption represents the main optimization parameter. The obtained results show that the data-driven water allocation strategies preserve the water savings for closed-loop control systems and avoid crop water stress due to the limited access to irrigation water.

Managing surgical waiting lists through dynamic priority scoring

Prioritising elective surgery patients under the Australian three-category system is inherently subjective due to variability in clinician decision making and the potential for extraneous factors to influence category assignment. As a result, waiting time inequities can exist which may lead to adverse health outcomes and increased morbidity, especially for patients deemed to be low priority. This study investigated the use of a dynamic priority scoring (DPS) system to rank elective surgery patients more equitably, based on a combination of waiting time and clinical factors. Such a system enables patients to progress on the waiting list in a more objective and transparent manner, at a rate relative to their clinical need. Simulation results comparing the two systems indicate that the DPS system has potential to assist in managing waiting lists by standardising waiting times relative to urgency category, in addition to improving waiting time consistency for patients of similar clinical need. In clinical practice, this system is likely to reduce subjectivity, increase transparency, and improve overall efficiency of waiting list management by providing an objective metric to prioritise patients. Such a system is also likely to increase public trust and confidence in the systems used to manage waiting lists.

Coordination of Mixed Platoons and Eco-Driving Strategy for a Signal-Free Intersection

This paper investigates the collaborative control of vehicular traffic for a signal-free intersection. The traffic under consideration is mixed with connected autonomous vehicles (CAVs) and human-piloted vehicles with advanced driver assistance systems (ADAS). The proposed collaborative control is of two levels. At the upper level, the objective is to minimize total delay via platoon control and dynamic priority control of conflicting movements. We formulate the platoon coordination of mixed traffic as a mixed-integer linear programming problem (MILP). This MILP determines whether adjacent vehicles will form a platoon and prioritize any two conflicting movements subject to lateral safety and rear-end safety constraints. At the lower level, we propose an eco-driving strategy to minimize the energy consumption by optimizing the speed profile of the platoon leading vehicles subject to the dynamic priority control from the upper level, i.e., the entry time to the merging zone. We deduce an analytical solution to the eco-driving problem using optimal control theory. Compared with existing benchmarks, such as the first-come-first-served policy, the proposed method outperforms the state-of-the-art controllers in reducing the total delay. Sensitivity analysis regarding the penetration rate of CAVs shows that substantial improvements can be achieved even at low to medium penetration rates of CAVs.

Cloud-Edge Collaborative Scheduling with a Focus on Clean Energy

With the promotion of the national "double carbon" goal, the power system is developing towards the direction of low-carbon transformation. In order to achieve the goal of "striving to peak carbon dioxide emissions by 2030 and striving to achieve carbon neutrality by 2060", we must actively promote the consumption of a high proportion of renewable energy from the grid, which is the most urgent issue to be addressed. In this chapter, access to clean energy will involve multiple aspects such as source, network, load and storage. However, due to the intermittent, random and volatile nature of wind power and photovoltaic power generation, the challenges faced by significant users of the power system are enormous. Traditional energy Internet dispatching adopts centralized dispatching. In this paper, by deploying edge nodes, the autonomous decision-making and autonomous collaboration of power grid nodes at all levels are enhanced, and the collaborative integration level of source, network, load and storage of smart power grid is improved. Aimed at the current power supply and demand imbalance, new energy access problems and energy storage problems. In this paper, a service adaptation algorithm based on dynamic priority is proposed based on the scenario of load storage integration of source network under renewable energy access. Experimental results show that, compared with other algorithms, this algorithm has lower scheduling time and execution time and better performance under the condition of ensuring the highest clean energy consumption rate and first-order load priority response.

Effective multi-objective inverse lithography technology at full-field and full-chip levels with a hybrid dynamic priority algorithm.

Inverse lithography technology (ILT), such as source mask optimization (SMO), is used to improve lithography performance. Usually, a single objective cost function is selected in ILT, and an optimal structure for one field point is achieved. The optimal structure is not the case for other images at full field points where the aberrations of the lithography system are different, even in high-quality lithography tools. The optimal structure that must match the high-performance images at the full field is urgently required for extreme ultraviolet lithography (EUVL). In contrast, multi-objective optimization algorithms (MOAs) limit the application of multi-objective ILT. Assigning target priority is incomplete in current MOAs, which results in the over-optimization of some targets and under-optimization of others. In this study, multi-objective ILT and a hybrid dynamic priority (HDP) algorithm were investigated and developed. High-performance images with high fidelity and high uniformity were obtained at multi-field and multi-clip areas across the die. A hybrid criterion was developed for the completion and reasonable prioritization of each target to ensure sufficient improvement. Compared to the current MOAs, the uniformity of images at full-field points was improved by up to 31.1% by the HDP algorithm in the case of multi-field wavefront error-aware SMO. The multi-clip source optimization (SO) problem showed the universality of the HDP algorithm to deal with different ILT problems. It acquired higher imaging uniformity than existing MOAs, which indicated that the HDP is more qualified for multi-objective ILT optimization than existing MOAs.