Planning Allocation Research Articles

Leveraging BIM for Enhanced Camera Allocation Planning at Construction Job Sites: A Voxel-Based Site Coverage and Overlapping Analysis

In the construction industry, the imperative for visual surveillance mechanisms is underscored by the need for safety monitoring, resources, and progress tracking, especially with the adoption of vision intelligence technology. Traditional camera installation plans often move toward coverage and cost objectives without considering substantial coverage overlap, inflating processing and storage requirements, and complicating subsequent analyses. To address these issues, this research proposes a voxel-based site coverage and overlapping analysis for camera allocation planning in parametric BIM environments, called the PBA approach. The first step is to collect information from the BIM model, which is the input for the parametric modeling step. After that, the PBA approach simulates the virtual devices and the construction layout by employing visual language programming and then generates a coverage area. Lastly, the performance simulation and evaluation of various placement scenarios against predefined criteria are conducted, including visual coverage and overlapping optimization for eliminating data redundancy purposes. The proposed approach is evaluated through its application to construction projects. The results from these various implementations indicate a marked decrease in data overlap and an overall enhancement in surveillance efficacy. This research contributes a novel, BIM-centric solution to visual information adoption in the construction industry, offering a scalable approach to optimize camera placement while mitigating overlapping areas.

Fixed cost allocation based on a data envelopment analysis aggressive game approach

The literature on organizational strategy and production operations suggests that establishing a shared platform would be beneficial for addressing competition and mitigating risks. However, the creation of a common platform inevitably incurs expenditures and costs, prompting the natural question of how to equitably distribute the total fixed cost among all participating units. While the fixed cost allocation (FCA) problem has been explored in the context of data envelopment analysis (DEA), this paper addresses a distinct decentralized environment wherein there is no central decision-maker, and each individual decision-making unit (DMU) pursues a self-interested strategy to minimize its allocated cost share. In tackling the challenge of proposing an acceptable allocation plan within this decentralized setting, we initially improve the non-egoistic principle by considering the perspective of operational size. This improvement mandates that each DMU bears the maximum fixed cost per unit of operational size in its own allocation proposal. The improved non-egoistic principle implicitly addresses the platform constraint and fairness concerns, while each DMU remains committed to maximizing other DMUs’ cost shares and minimizing their own performance efficiency. Additionally, we introduce an aggressive game process to amalgamate all DMUs’ allocation proposals into the DEA efficiency analysis. A DEA aggressive game approach and its corresponding computational algorithm are developed to facilitate consensus and determine the final FCA plan. Mathematically, we prove that all average post-allocation efficiencies, incorporating fixed costs, converge to the same aggressive game cross-efficiency for each DMU in the iterative algorithm. To the best of our knowledge, this paper represents the first study that employs an aggressive strategy in the FCA problem to minimize efficiency scores, while existing literature predominantly focuses on maximizing or maintaining efficiency scores. Finally, we apply the proposed approach to a classical numerical example and an empirical study involving nine independent truck fleets to showcase its characteristics and efficacy.

Coupling hydrological methods with ‘Flow Health’ to simulate ecological flow in the Weihe River Basin, China

AbstractReducing river flows significantly affects livelihoods, ecology and industrial production. Therefore, allowing for ecological flow requirements has been applied to limiting human water consumption and is incorporated into water allocation planning. However, the unavailability of ecological data greatly limits e‐flow studies and this lack of sufficient ecosystem data is mostly addressed by using hydrological metrics as surrogates of the river ecosystem. This study employed the nine indicators of the ‘Flow Health’ model to assess river hydrological health and explore optimal ecological flow regimes. The modelling estimated the required flow and the extra flood flows of ecosystem conservation under default and custom thresholds. Ecological flows were calculated for five stations of the Weihe River through coupling with three hydrological methodologies. The index of flow deviation indicated that the flow regimes in Weihe River have partially changed from the reference period. Fish and other aquatic habitats have changed by reduced flows, but the health of the floodplain ecosystem has not been seriously affected. The ecological flow modelling results showed that the extra flow required for the ecology restoration in five stations is concentrated from July to September. The optimal ecological flows in dry seasons by coupling three hydrological methods were less than the average current flow, which is reasonable. This coupling provides ideas on how environmental flow assessments are undertaken in similar regions.

Integrated optimization of berth allocation problem with berth shifting strategies and ballast water management for dry bulk ports

In maritime operations, ballast water operations and cargo handling operations are carried out simultaneously to determine the operational efficiency of vessels in port. However, the ballast water pumping rate lags behind the growing efficiency of bulk cargo handling, resulting in operational interruptions and extended stay times at ports. To address this challenge, this paper introduces a novel ballast water management strategy: vessels first handle a portion of their cargo and then shift to a lay-by berth for mid-pumping. This strategy results in multiple shifting movements of vessels within the berth allocation plan. Therefore, to better implement this strategy, we develop an integrated model of berth allocation and ballast water pumping management, with the special considerations of multi-berth shifting strategies and ballast water weight. To efficiently solve this model, we present a hybrid heuristic algorithm based on adaptive genetic algorithms and the critical path method. The computer experiments reveal that the mid-pumping strategy effectively circumvents untimely pumping, shortens the vessel port stay time, and yields a 9.42% reduction in the total stay time. This research further elucidates managerial insights to inform and enhance port operations.

Revealing the heterogeneity of periodic visiting patterns across different ages and spaces using a flexible detection method: A case study of Beijing

Periodic visiting pattern (periodic pattern) means a person revisits a place to conduct a specific activity periodically (possibly non-continuous), in which the time interval could differ slightly from one occurrence to the other (possibly unstable). Perceiving periodic patterns is the key to understanding residents' daily needs and living range, which provides evidence for more reasonable resource allocation and urban planning. For example, if a group of people visits medical centres continuously and stably at short intervals, the allocation of medical resources should lean toward this group of people. However, previous methods are too rigid for detecting periodic patterns. They cannot distinguish the stability and continuity of periodic patterns, resulting in the inability to differentiate the significance level of certain patterns to some people and the omission of some vital but imperfect patterns. In addition, previous studies ignore analysing the heterogeneity of periodic patterns across different places and age groups, which is crucial to the spatial and personalized analysis of daily needs. Thus, we first design a novel method to flexibly detect the periodic pattern from the trajectory data. Then, we compare the proportions of each type of activity supported by the corresponding built environment among different places and age groups. With the mobile phone data during three months in Beijing, we found significant imbalances in the spatial scope of periodic patterns and differences in periodic patterns across different age groups, reflecting the residents’ diverse needs for each functional area and the potential inconvenience caused by the irrational spatial distribution of resources.

Impact of Institutional Practices and Surgical Complexity on Sarcoma Surgery Costs: Driving Efficiency in Value-Based Healthcare.

Sarcomas present a unique challenge within healthcare systems due to their rarity and complex treatment requirements. This study explores the economic impact of sarcoma surgeries across three Swiss tertiary healthcare institutions, utilizing a consistent surgical approach by a single surgeon to eliminate variability in surgical expertise as a confounding factor. By analyzing data from 356 surgeries recorded in a real-world-time data warehouse, this study assesses surgical and hospital costs relative to institutional characteristics and surgical complexity. Our findings reveal significant cost variations driven more by institutional resource management and pricing strategies than by surgical techniques. Surgical and total hospitalization costs were analyzed in relation to tumor dignity and complexity scores, showing that higher complexity and malignancy significantly increase costs. Interestingly, it was found that surgical costs accounted for only one-third of the total hospitalization costs, highlighting the substantial impact of non-surgical factors on the overall cost of care. The study underscores the need for standardized cost assessment practices and highlights the potential of predictive models in enhancing resource allocation and surgical planning. By advocating for value-based healthcare models and standardized treatment guidelines, this research contributes to more equitable and sustainable healthcare delivery for sarcoma patients. These insights affirm the necessity of including a full spectrum of care costs in value-based models to truly optimize healthcare delivery. These insights prompt a reevaluation of current policies and encourage further research across diverse geographical settings to refine cost management strategies in sarcoma treatment.

Multiple Task Assignment Algorithms for Unmanned Aerial Vehicles

To fulfill the requirement of assigning task the auction algorithm is widely used. There are many classical auction algorithms those performances not up to the mark while dealing with multi-UAVs dynamic task assignment. SWARM UAVs are made up of a large number of small UAVs with limited mission resources that can operate in an autonomous, appropriate and universal manner. Based on the in-depth research of the traditional auction algorithm CAA, this paper proposes an iterative method that can improve the task allocation efficiency of multi-UAV, namely the two-stage auction algorithm. At the same time, in order to improve the daily management of airborne computing and communication resources of UAV, this paper overcomes the difficulties caused by data coupling between task allocation and path planning, and proposes a decentralized task allocation algorithm, that is, UAV re-checks the unreasonable task allocation results within the task allocation cycle. This method has the advantages of algorithm security and unpredictability, and it can control the error of task assignment evaluation within a specific range through finite complexity calculation. Simulation results show that the algorithm is effective in computing efficiency and task execution efficiency.

A survey on collaborative hunting with robotic swarm: Key technologies and application scenarios

Compared with a single robot, robotic swarm realizes multi-agent cooperative operations through information interaction, and has such characteristics of wide combat monitoring range, flexible combat organization and strong reconfiguration. As the intelligence of robotic swarm, collaborative hunting tasks have been used in the fields of intrusion countermeasures, target elimination, scientific research, target rescue and etc. A comprehensive overview of current state of researches on collaborative hunting tasks with robotic swarm would be beneficial to researchers. Therefore, we provide an overview of research progress related to collaborative hunting tasks of robotic swarm in this paper. Specifically, we analyze key technologies in the collaborative hunting tasks from the perspectives of target searching, hunting task allocation and tracking path planning. Moreover, we summarize the differences of hunting strategies in different scenarios (i.e., air scenario, sea scenario and ground scenario), so it has important guiding significance for future researches. Finally, we discuss future research directions for collaborative hunting tasks of robotic swarm.

A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem

The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors—such as traffic congestion, weather conditions, and battery level of each vehicle—to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25% better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems.

DRL-Based Resource Allocation and Trajectory Planning for NOMA-Enabled Multi-UAV Collaborative Caching 6G Network

DRL-Based Resource Allocation and Trajectory Planning for NOMA-Enabled Multi-UAV Collaborative Caching 6G Network

Global, regional, and national burdens of cancer in children aged zero to nine years from 1990 to 2019.

The description of long-term trends in the cancer burden among children aged zero to nine years from 1990 to 2019 reveals significant changes in children's health. It helps in resource allocation and health policy planning. We analysed data on the incidence, mortality, and disability-adjusted life years (DALYs) by sex and age group in children aged zero to nine. Estimates of DALYs for children aged zero to nine years, appeared as part of the Global Burden of Diseases, Injuries, and Risk Factor Study 2019, by age, sex, and location for 1990-2019. We also provided estimations by the sociodemographic index (SDI) quintile, a systematic measure to indicate educational attainment, income per capita, and total fertility rate for those younger than 25 years. We used age-period-cohort models to investigate paediatric cancers prevalence, incidence, mortality, and DALYs rates and auto-regressive integrated moving average models to predict cancer in children of different age groups in males and females. A total of 6 224 010 DALY numbers for cancer cases occurred globally in 2019 among children aged zero to nine years. Additionally, the incidence of paediatric cancers in 2019 in the middle SDI countries was the highest, including 60 662 cases, and the highest mortality and DALYs cases of paediatric cancers were in the low SDI countries (25 502 and 2 199 790). The joinpoint regression analysis revealed that the trend of total cancer burden in age-standardised mortality rates and age-standardised DALYs rates showed a significant decrease with an average annual percentage change of -2.10 and -2.03 from 1990 to 2019. Furthermore, the paediatric cancer spectrum was changing. Other malignant neoplasms and other leukaemia were the major components of cancer in all age groups of children. The disease burden in children aged zero to nine years decreased significantly globally from 1990 to 2019. However, the overall prediction of childhood cancer increased slightly from 2020 to 2040. Our findings may help guide investments and inform policies. This highlights the necessity to improve current treatment measures and establish effective prevention strategies to reduce the cancer burden among children aged zero to nine years.

Joint operational situation assessment and firepower allocation method based on enhanced potential energy model

The assessment of battlefield situations and the allocation of firepower are pivotal prerequisites for conducting joint operational planning. In response to the challenges posed by multi-service joint operations, this paper presents a methodology based on enhanced operational potential energy model for the assessment of battlefield situations and the generation of optimal firepower allocation plan. We focuses on the three-fold factors influencing operational effectiveness: spatial, informational, and environmental factors, and completes the enhanced operational potential energy model by integrating the assessment of joint operational situations and the generation of optimal firepower allocation plans. Finally, the effectiveness of this methodology is demonstrated through operational simulations in a joint anti-ship combat scenario.

Health Service Use and Costs During Pregnancy Among Privately Insured Individuals With Congenital Heart Disease

Individuals with congenital heart disease (CHD) are increasingly reaching childbearing age, are more prone to adverse pregnancy events, and uncommonly undergo recommended cardiac evaluations. Data to better understand resource allocation and financial planning are lacking. To examine health care use and costs for patients with CHD during pregnancy. This retrospective cohort study was performed from January 1, 2010, to December 31, 2016, using Merative MarketScan commercial insurance data. Participants included patients with CHD and those without CHD matched 1:1 by age, sex, and insurance enrollment year. Pregnancy claims were identified for all participants. Data were analyzed from September 2022 to March 2024. Baseline characteristics (age, US region, delivery year, insurance type) and pregnancy-related events (obstetric, cardiac, and noncardiac conditions; birth outcomes; and cesarean delivery). Health service use (outpatient physician, nonphysician, emergency department, prescription drugs, and admissions) and costs (total and out-of-pocket costs adjusted for inflation to represent 2024 US dollars). A total of 11 703 pregnancies (mean [SD] maternal age, 31.5 [5.4] years) were studied, with 2267 pregnancies in 1785 patients with CHD (492 pregnancies in patients with severe CHD and 1775 in patients with nonsevere CHD) and 9436 pregnancies in 7720 patients without CHD. Compared with patients without CHD, pregnancies in patients with CHD were associated with significantly higher health care use (standardized mean difference [SMD] range, 0.16-1.46) and cost (SMD range, 0.14-0.55) except for out-of-pocket inpatient and ED costs. After adjustment for covariates, having CHD was independently associated with higher total (adjusted cost ratio, 1.70; 95% CI, 1.57-1.84) and out-of-pocket (adjusted cost ratio, 1.40; 95% CI, 1.22-1.58) costs. The adjusted mean total costs per pregnancy were $15 971 (95% CI, $15 480-$16 461) for patients without CHD, $24 290 (95% CI, $22 773-$25 806) for patients with any CHD, $26 308 (95% CI, $22 788-$29 828) for patients with severe CHD, and $23 750 (95% CI, $22 110-$25 390) for patients with nonsevere CHD. Patients with vs without CHD incurred $8319 and $700 higher total and out-of-pocket costs per pregnancy, respectively. This study provides novel, clinically relevant estimates for the cardio-obstetric team, patients with CHD, payers, and policymakers regarding health care and financial planning. These estimates can be used to carefully plan for and advocate for the comprehensive resources needed to care for patients with CHD.

Admission Prediction Using Time Series Analysis

Efficient resource allocation and strategic planning in educational institutions heavily rely on accurate predictions of student admissions. This paper presents a detailed investigation into the application of time series analysis techniques for admission prediction. We explore the utilization of historical admission data, spanning multiple academic cycles, to train and evaluate several time series models.The study encompasses a comparative analysis of traditional statistical models such as autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) alongside more advanced deep learning techniques like long short-term memory (LSTM) networks. By evaluating the performance metrics, including accuracy, robustness, and computational efficiency, we aim to identify the most suitable model for admission prediction tasks. Moreover, the research delves into the impact of various external factors such as changes in the academic calendar, socio-economic indicators, and demographic shifts on admission patterns. Understanding these factors is crucial for enhancing the predictive capabilities of the models and enabling institutions to adapt their strategies accordingly. The experimental results and comparative analysis provide valuable insights for educational institutions, enabling them to make data-driven decisions regarding enrollment management strategies and resource allocation. Ultimately, this research contributes to the advancement of admission prediction methodologies, facilitating more efficient and informed decision-making processes in the educational domain.

Multi-AUV underwater static target search method based on consensus-based bundle algorithm and improved Glasius bio-inspired neural network

This research introduces a hierarchical strategy for static target searches with multi-autonomous underwater vehicles (AUVs) to optimize cumulative search rewards. The approach comprises two primary elements: task allocation and path planning. A Voronoi diagram segments regions based on peak detection via a maximum filter in the task allocation stage. Then, a consensus-based bundling algorithm ensures the load-balanced distribution of peak sub-regions across AUVs, while a dynamic cooperation mechanism allows for dynamic adjustment of task allocation, thereby increasing the system's operational flexibility. Path planning employs an improved Glasius bio-inspired neural network, leveraging analogies to convolution processes and incorporating mean pooling, multiple convolutions, and resampling. This method enhances global information propagation and optimizes path point selection through a discounted reward function evaluating adjacent nodes, thus boosting the search efficiency of individual AUVs. Simulation experiments validate the method's effectiveness and robustness in multi-AUV static target searches, demonstrating its potential to improve search efficiency.

Behind the Scenes: Facilitators and Barriers to Developing State Scarce Resource Allocation Plans for the COVID-19 Pandemic

In response to COVID-19, many states revised, developed, or attempted to develop plans to allocate scarce critical care resources in the event that crisis standards of care were triggered. To our knowledge, no prior analysis has assessed this plan development process, including whether plans were successfully adopted. How did states develop or revise scarce resource allocation plans during the COVID-19 pandemic, and what were the barriers and facilitators to their development and adoption at the state level? Plan authors and state leaders completed a semistructured interview February to September 2022. Interview transcripts were qualitatively analyzed for themes related to plan development and adoption according to the principles of grounded theory. Thirty-six participants from 34 states completed an interview, from states distributed across all US regions. Among participants' states with plans that existed prior to 2020 (n= 24), 17 were revised and adopted in response to COVID-19. Six states wrote a plan de novo, with the remaining states failing to develop or adopt a plan. Thirteen states continued to revise their plans in response to disability or aging bias complaints or to respond to evolving needs. Many participants expressed that urgency in the early days of the pandemic prevented an ideal development process. Facilitators of successful plan development and adoption include: coordination or support from the state department of health and existing relationships with key community partners, including aging and disability rights groups and minoritized communities. Barriers include: lack of perceived political will to adopt a plan and development during a public health emergency. To avoid repeating mistakes from the early days of the COVID-19 response, states should develop or revise plans with community engagement and consider maintaining a standing committee with diverse membership and content expertise to periodically review plans and advise state officials on pandemic preparedness.

New Mean and Median Techniques to Solve Multiobjective Linear Fractional Programming Problem and Comparison with Other Techniques

In the field of operation research, both linear and fractional programming problems have been more encountered in recent years because they are more realistic in expressing real-life problems. Fractional programming problem is used when several rates need to be optimized simultaneously such as resource allocation planning, financial and corporate planning, healthcare, and hospital planning. There are several techniques to solve the multiobjective linear fractional programming problem. However, because of the use of scalarization, these techniques have some limitations. This paper proposed two new mean and median techniques to solve the multiobjective linear fractional programming problem by overcoming the limitations. After utilizing mean and median techniques, the problem is converted into an equivalent linear fractional programming problem; then, the linear fractional programming problem is transformed into linear programming problem and solved by the conventional simplex method or mathematical software. Some numerical examples have been illustrated to show the efficiency of the proposed techniques and algorithm. The performance of these solutions was evaluated by comparing their results with other existing methods. The numerical results have shown that the proposed techniques are better than other techniques. Furthermore, the proposed techniques solve a pure multiobjective maximization problem, which is even impossible with some existing techniques. The present investigation can be improved further, which is left for future research.

Establish an agricultural drought index that is independent of historical element probabilities

AbstractCurrently, there are three main shortcomings in meteorological drought indices: first, they rely on historical climate probability functions; second, the timescale used in calculations has a certain degree of subjectivity; third, the same index value may correspond to vastly different levels of actual drought in different climate types of regions. The purpose of this article is to establish a meteorological drought index that does not rely on historical meteorological element probability functions. Through theoretical derivation, four drought‐level maintenance lines are established on the cumulative precipitation‐cumulative water surface evaporation coordinate plane, and the coordinate quadrant is divided into five drought‐level areas. Through forward daily rolling accumulation, the maximum distance point is selected from the dynamically changing coordinate points to determine the corresponding cumulative precipitation and cumulative evaporation. The meteorological drought index is established by the distance from the selected coordinate point to each drought‐level maintenance line. Using daily precipitation and evaporation data from meteorological observation stations, the index is calculated based on the established meteorological drought index model, and compared with actual drought evolution and drought disaster records. The results show that the index can capture the development of drought well, and its changes are very consistent with drought disaster records. The index is of great significance for drought monitoring or assessment, and can provide guidance for water resource allocation, crop layout, and urban planning. Furthermore, it can also provide a way of thinking that does not rely on historical element probabilities for future drought research.

Wonorejo dam operation and storage analysis against climate change

Climate change affecting rain intensity within catchment and increase the potential hydrometeorology disasters. Wonorejo Reservoir as a multipurpose dam requires an operational plan and water allocation to fulfill downstream needs, such as irrigation, hydropower, microhydropower, domestic industry uses and flood control. Wonorejo Dam is a backfill type dam with High Water Level (HWL) elevation is +183.00 mSHVP and Low Water Level (LWL) elevation is +141.00 mSHVP, Total storage capacity Wonorejo Reservoir is 109.118 million m3 (2021) or 89.4% from initial total storage capacity (2000), in 2050 predicted sediment volume increase to 17.73 million m3 or 17.6% reservoir capacity and 2050 capacity volume is 100,585 million m3 or 82.4% from initial total storage capacity. Annual Reservoir Operation Plan and Annual Water Allocation Plan for Wonorejo Reservoir in 2022/2023 as water services December 2022 to November 2023 periode, Wonorejo Reservoir elevation at the end of May 2023 reaches HWL and lowest water elevation is +162.64 mSHVP in the first week of November 2023.

Multi-Domain Collaborative Task Allocation and Conflict Resolution in Unmanned Systems under Complex Constraints

Under the background that multi-domain collaborative tasks with complex constraints executed by heterogeneous unmanned systems, based on a consensus-based bundle algorithm, a consensus-based synergy algorithm is proposed to solve the distributed multi-domain collaborative task planning problem. First, a multi-domain collaborative task allocation model and score evaluation system are established by considering task resource requirement constraints, task timing constraints, and path threat constraints. Second, the time sequence constraints of collaborative tasks are transformed into elastic time window constraints. The bidding method based on collaborative constraints is used for task allocation, and the improved consistency conflict resolution principle is adopted to realize distributed multi-domain collaborative task allocation conflict resolution. Finally, the path planning is coupled to the task allocation process by using the Bezier curve path, and the results of multi-domain collaborative task allocation and path planning are obtained synchronously. Simulation results show that the proposed algorithm can effectively solve the problem of multi-domain collaborative task planning for heterogeneous unmanned systems.