Allocation Model Research Articles

Harnessing Curtailed Wind-Generated Electricity via Electrical Water Heating Aggregation to Alleviate Energy Poverty: A Use Case in Ireland

Ireland experiences high energy poverty rates alongside surplus wind energy resources. With 77% of Irish households equipped with electrical immersion heaters for domestic hot water (DHW) generation, this study proposes an Electrical Water Heating Aggregation (EWHA) scheme. The scheme allocates surplus wind-generated electricity to provide DHW to fuel-poor households, thereby alleviating energy poverty through harnessing curtailed wind energy. Through a developed wind-generated electricity allocation model and half-hourly data analysis for a weather year, this research assesses the feasibility and economic viability of the EWHA scheme, focusing on the householder as the primary benefactor from the scheme (as opposed to ancillary grid service provision). The results suggest an optimal aggregation size where maximum curtailment and carbon offset coincide with maximum benefits for participants. The findings indicate that fuel-poor households in Ireland could receive a full DHW tank every three weeks using surplus wind energy, harnessing 89% of overnight curtailed wind energy and offsetting 33 MkgCO2 annually. Moreover, the scheme could potentially save the Irish state approximately EUR 4 million by 2030, increasing to EUR 11 million by 2050, in carbon costs. Overall, this research demonstrates the potential of EWHA schemes to alleviate energy poverty, optimise wind energy utilisation, and contribute significantly to carbon emission reduction targets.

Overhead and Performance of Dynamic Network Slice Allocation for Mobile Users

Based on virtualisation technologies like SDN and NFV, modern networks like 5G create customised logical networks known as Network Slicing to serve distinct user groups. The mobility of a significant part of these users brings great challenges for that scenario, e.g. demand variations and continuous connectivity. In such a scenario, the dynamic resource allocation of these slices is shown as a possible solution. However, that approach may be sensitive to computing overhead and trade-off scenarios, which are not fully evaluated in the literature. This work focuses on that gap by researching the impact of network slice reconfiguration overhead on the overall network performance. An extensive evaluation of Dynamic Network Slicing in providing user mobility support in Fog computing-based architectures is presented. This paper presents a resource allocation model that calculates the required time to allocate a slice instance. Furthermore, based on those values, this work presents an evaluation of the impact of resource allocation delays in terms of bandwidth allocation, latency, and service outage in different dynamic slicing scenarios that include service migration. Simulations based on realistic values point out that reducing the allocation delay from 60 to 30 s can lead the network to 25% more stable latency to its users in 60 min slice lifetime.

Uncertainty Analysis of Aircraft Center of Gravity Deviation and Passenger Seat Allocation Optimization

The traditional method of allocating passenger seats based on compartments does not effectively manage an aircraft’s center of gravity (CG), resulting in a notable divergence from the desired target CG (TCG). In this work, the Boeing B737-800 aircraft was employed as a case study, and row-based and compartment-based integer programming models for passenger allocation were examined and constructed with the aim of addressing the current situation. The accuracy of CG control was evaluated by comparing the row-based and compartment-based allocation techniques, taking into account different bodyweights and numbers of passengers. The key contribution of this research is to broaden the range of the mobilizable set for the aviation weight and balance (AWB) model, resulting in a significant reduction in the range of deviations in the center of gravity outcomes by a factor of around 6 to 16. The effectiveness of the row-based allocation approach and the impact of passenger weight randomness on the deviation of an airplane’s CG were also investigated in this study. The Monte Carlo method was utilized to quantify the uncertainty associated with passenger weight, resulting in the generation of the posterior distribution of the aircraft’s center of gravity (CG) deviation. The outcome of the row-based model test is the determination of the range of passenger numbers that can be effectively allocated under different TCG conditions.

A two-tier bidding model considering a multi-stage offer‑carbon joint incentive clearing mechanism for coupled electricity and carbon markets

To facilitate the coupling of electricity and carbon markets, a two-layer bidding model considering a multi-stage offer‑carbon joint incentive clearing mechanism is proposed. The upper layer models the bidding strategies of renewable energy units and thermal power generating units, and the lower layer is the market clearing layer. A multi-stage offer‑carbon joint incentive clearing mechanism is proposed. In the pre-clearing stage, the environmental benefit factor of the carbon market is introduced to adjust the bidding strategy of the units, the carbon emission intensity enters the market in the form of price, the environmental benefit of the generating units is fully considered, and the carbon quota allocation model of units is established; in the second stage, carbon trading cost is added based on the pre-clearing model to further optimize the unit output; in the third stage, the clearing model with the minimum cost of electricity energy is established for tracking the clearing price. Under this mechanism, the carbon trading cost is traded on the market and unit offers are combined with carbon element to participate in market trading. Three bidding models with different clearing mechanisms are set up, and the results of the arithmetic example show that the proposed model can realize the balance between the economic and low-carbon goals of market clearing, and can achieve the maximum carbon emission reduction based on no increase in the total cost of electric-carbon coupling. As the share of renewable energy increases, the clearing price will decrease. A reasonable offer from renewable energy sources can avoid their interests being jeopardized. Concurrently, increasing the initial carbon price can effectively guide the electricity market to develop in a cleaner direction.

How government policies promote transportation utilization in the national-level hydrogen supply chain: A case of China

Pipeline transportation is a low-cost option for delivering large volumes of hydrogen (H2), however, the high initial capital cost remains a major impediment to expanding pipeline delivery infrastructure. To quantitatively evaluate the impact of the H2 pipeline on the hydrogen supply chain (HSC), this paper develops an evaluation system for the development potential of the H2 pipeline and simulation models for H2 resource allocation. Furthermore, the HSC system in the southwest of China is used to validate the proposed method. The results demonstrate that the levelized cost range of a given 150–550 km H2 pipeline is 2.76–10.12 CNY/kg, which has price advantages over other transportation modes. Compared with the hydrogen supply chain resource allocation (HSCR) model, putting the H2 pipeline into use can optimize the layout and utilization of different transportation modes. It shows that the proportion of H2 transportation volume distribution in H2 pipelines has increased from 0 % to 73.44 %, while the proportion of HLT has first increased from 18.86 % to 54.27 %, and then decreased to 17.95 %. And then under different policy scenarios, the consideration of pipeline reduces the sensitivity of the H2 blending ratio of natural gas (NG) and NG demand to HSC. The impacts of policies on regional H2 development and regional policy implications are revealed. This approach provides a tool for policymakers to quantify the impacts of policy.

Integrated evaluation of city-level CO2 emissions and sinks from 2010 to 2019: Spatio-temporal patterns in the Yangtze River Economic Belt of China

The intensifying impact of global climate change has led to increased attention being paid to carbon dioxide (CO2) emissions and sinks. However, it remains a challenge to accurately assess and map the spatial distribution of CO2 emissions and sinks. Previous studies have primarily focused on national and provincial level research, leaving city-level studies relatively overlooked. Acknowledging the significance of interregional differences and the crucial ecological and economic aspects of the Yangtze River Economic Belt (YREB), we devised a research framework to analyse spatio-temporal patterns through city-level CO2 emissions and sinks. The city-level CO2 emissions were estimated using provincial data and linear allocation models, while CO2 sinks were estimated based on the areas of ecosystems and their corresponding sink coefficients. The analysis of YERB's spatio-temporal patterns focuses on regional hotspots, outliers, and time-series clustering. The findings show that: (1) Between 2010 and 2019, ecosystems within YREB absorbed approximately 41.17%–46.70% of CO2 emissions, with the increase rate of CO2 emissions being lower than that of sinks. (2) City-level CO2 in Western Region of YREB exhibited low emissions and high sinks, while Eastern Region showed high emissions and low sinks, and these characteristics were intensifying. (3) City-level CO2 emissions and sinks demonstrated a “core-periphery” clustering pattern. (4) The regional CO2 balance was predominantly driven by most cities in Eastern Region. This study provides a comprehensive understanding of CO2 emissions and sinks at the city level and offers valuable insights for developing regional collaborative emission reduction strategies in YREB.

Increasing the performance of a hospital department with budget allocation model and machine learning assisted by simulation

ABSTRACT The COVID-19 pandemic highlighted the critical need for efficient resource management in healthcare. In this study, the internal medicine outpatient clinic in a hospital is modelled by simulation method. Appropriate statistical distributions of the parameters are derived from past data. The results of a limited number of simulation runs are used as training data for machine learning techniques and an estimation model is selected among them. The estimation results are considered as input to a mathematical model which determines the optimal budget allocation for improving the system performance. Analysis considers patient waiting times and system throughput under varied parameters. A significant amount of time is saved by using machine learning to predict the simulation model outcomes, which had previously taken a total of around 7 hours reduced to 30–40 minutes. Time savings through machine learning are projected to be notably greater for more complex simulations comparing to current case.

Water Banking as a Strategy for the Management and Conservation of a Critical Resource: A Case Study from Tunisia’s Medjerda River Basin (MRB)

The increasingly adverse impacts of climate change (e.g., rainfall patterns, droughts, and floods), coupled with the ever-increasing water demands, are often translated into a contingent liability for water users’ communities. Additional complexities arise due to competing priorities, water rights, and transboundary water sources. Therefore, conventional water management practices should shift toward more comprehensive and responsive integrative approaches, even for systems with limited data. Furthermore, water managers must prioritize dynamic and interactive management techniques for existing systems. One such management technique is water banking, which is the focus of this study. Herein, a dynamic interactive water allocation model, which encompasses the water managers and heterogeneous parties with competing demands, is developed. The voluntary sales of water shares between parties are illustrated through the specific case of the Medjerda River in Tunisia, an excellent example of a transboundary basin with limited hydrologic data and conflicting water use requirements between its upstream and downstream sectors. A set of scenarios is developed for the first analysis with this model: two management scenarios that include the no-water trade and the water banking option; three demand scenarios that include a combination of steady-, low-, and high-water demand conditions; and two hydrologic scenarios that include dry and wet conditions. Based on an economic model, the economic impacts of water banking are calculated using estimates of the costs of water shortages brought to users that illustrate the magnitude. The results show that the water banking technique can improve water resource availability by optimizing the management, operation, and conservation of natural and artificial water storage systems and water distribution infrastructure. Specifically, water banking can offset users’ profit losses during severe conditions (i.e., drought), even with limited hydrologic data. This water management technique would allow the Tunisian government to minimize the economic impacts on farmers from drought and to plan for future uncertainties by optimizing the water storage potential in years of abundant rainfall.

Dynamic Energy Budget approach for modeling growth and reproduction of Neotropical stink bugs

Global warming and other climate change drivers have a significant impact on the life cycle of insects which in turn affect their population dynamics and geographic distribution. Among these insects are stink bugs (Hemiptera: Pentatomidae), which are hemimetabolous agricultural pests that cause major damage on crops in many countries. Their life cycle consists of three stages: egg, nymph, and adult. The nymph grows and molts through a small number of instars until reaching emergence, at which point growth ceases. The Dynamic Energy Budget (DEB) theory provides the framework for modeling the life cycle of stink bugs. Nevertheless, determining the most accurate model for nymphal growth and energy allocation during the adult stage remains a challenge, as the best model is not always evident. For two species of the Pentatomidae family, we parameterized and evaluated four DEB models that differ in nymphal growth and the energy allocation rules in adults: isomorphic or V1-morphic nymphal growth combined with the energy allocation scheme in adults that follows the κ-rule or the scheme that the κ-rule is not operational in adults. Overall, all models fit the data, but those with isomorphic nymphal growth reproduce the growth trajectories and instar duration better. However, with the available data we cannot conclude which rule is most likely the energy allocation in adults. Using these models, we further studied the effects of temperature and food availability on individual dynamics and its life cycle. Simulations show that temperature has a higher impact on the duration of life stages and survival, while food availability affects egg production. When simulating a variable environment in terms of temperature for both a summer and a fall generation, the results reveal that the summer generation has higher feeding and egg production rates in comparison to the fall generation. DEB models are valuable tools for simulating individual under various environmental conditions and when combined with appropriate modeling approaches, they can predict population traits and even the potential distribution of a species.

Regional endogenous development in Chile: a simulation analysis of two models of allocation of public funds

Regional endogenous development in Chile: a simulation analysis of two models of allocation of public funds

A budgeting resource allocation model for capacity expansion

The reconfiguration of the global supply chain has created opportunities for newly industrialized economies (NIEs) to strengthen and expand their production capacities. We develop a dynamic modeling framework to study a firm’s resource allocation decisions while building capacity for a portfolio of production lines over multiple decision periods. Our model adds to the literature on versions of this problem in which distributions of market demands are incorporated into the investment–return function, and where no partial order fulfillment is possible. We consider two specific cases: one involving exogenously defined demand distributions, and one where the distributions of the parameters of the demand distribution are updated based on Bayesian inference. For each case, we first identify optimal capacity levels as a function of unit investment cost, holding cost, and the demand distribution of each production line. We then characterize the optimal path to reach these levels under different market conditions by deriving closed-form optimal resource allocation policies.

Asset allocation models for big tech stocks: The importance of lower partial moments and short length windows

The group of companies formed by Meta, Apple, Microsoft, Amazon and Alphabet have become a successful investment alternative in the U.S. stock market. In this context, the aim of this research is to provide investment strategies based on these companies to the challenge of how individual investors should allocate their funds in a portfolio and outperform benchmarks such as the SPY ETF or a naïve portfolio. To this end, we developed a total of 20 asset allocation models and constructed portfolios with different rebalancing periods between April 2014 and June 2022. Our overall results reveal that a combination of a short window length for estimating the parameters of the asset allocation models and a procedure that takes downside risk into account, more precisely the Lower Partial Moment approach, significantly outperforms the alternative of investing in the SPY ETF and also the naïve portfolio.

A modular simulation framework for organ allocation

BackgroundWe describe and validate a new simulation framework addressing important limitations of the Simulated Allocation Models (SAMs) long used to project population effects of transplant policy changes. MethodsWe developed the Computational Open-source Model for Evaluating Transplantation (COMET), an agent-based model simulating interactions of individual donors and candidates over time to project population outcomes. COMET functionality is organized into interacting modules. Donors and candidates are synthetically generated using data-driven probability models which are adaptable to account for ongoing or hypothetical donor/candidate population trends and evolving disease management. To validate the first implementation of COMET, COMET-Lung, we attempted to reproduce lung transplant outcomes for U.S. adults from 2018-2019 and in the six months following adoption of the Composite Allocation Score (CAS) for lung transplant. ResultsSimulated (median [Interquartile Range, IQR]) versus observed outcomes for 2018-2019 were: 0.162 [0.157, 0.167] versus 0.170 waitlist deaths per waitlist year; 1.25 [1.23, 1.28] versus 1.26 transplants per waitlist year; 0.115 [0.112, 0.118] versus 0.113 post-transplant deaths per patient year; 202 [102, 377] versus 165 nautical miles travel distance. The model accurately predicted the observed precipitous decrease in transplants received by type O lung candidates in the six months following CAS implementation. ConclusionsCOMET-Lung closely reproduced most observed outcomes. The use of synthetic populations in the COMET framework paves the way for examining possible transplant policy and clinical practice changes in populations reflecting realistic future states. Its flexible, modular nature can accelerate development of features to address specific research or policy questions across multiple organs.

Mathematical Models for the Single-Channel and Multi-Channel PMU Allocation Problem and Their Solution Algorithms

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0/1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids.

DDPG 및 연합학습 기반 5G 네트워크 자원 할당과 트래픽 예측

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

Malaysian Chinese folk beliefs on Facebook based on LDA topic modelling

In the digital age, as social media evolves into a new and significant centre for the dissemination of Chinese folk beliefs, the Malaysian Chinese have actively shared information about these folk beliefs on their social media platforms. The dissemination has transcended regional barriers, encouraging more Malaysian Chinese across various states to actively participate in public discussions on this topic. This study delves into Malaysian Chinese folk beliefs by analysing data from Facebook. A comprehensive examination of 4012 text posts was conducted using the latent Dirichlet allocation (LDA) model for topic modelling. The analysis identified four main themes on social media: ‘Practitioners Worship’, ‘Temple Activities’, ‘Deity Legends’, and ‘Merchandise about Deity Statues’. Based on integrating social construction theory and media ecology theory, the study first explores the varied constructors, including practitioners, temple organisations, media organisations, and merchants. Secondly, Malaysian Chinese folk beliefs on social media present characteristics of utilitarianism, regional diversity, multiple social functions, flowing realms, strong Taoist elements, commercialisation, and a close relationship with the Spring Festival. Furthermore, ‘Safety and Peace’, ‘Pray for Demands’, and ‘Merits and Virtues’ form an interconnected semantic nexus. Hence, the findings theoretically highlight the interaction and significance of social media in the construction and practice of folk beliefs within the Malaysian Chinese community. Practically, this research provides valuable insights into the understanding and dissemination of Malaysian Chinese religious culture in the digital era.

Delineating Source and Sink Zones of Trip Journeys in the Road Network Space

Source–sink zones refer to aggregated adjacent origins/destinations with homogeneous trip flow characteristics. Current relevant studies mostly detect source–sink zones based on outflow/inflow volumes without considering trip routes. Nevertheless, trip routes detail individuals’ journeys on road networks and give rise to relationships among human activities, road network structures, and land-use types. Therefore, this study developed a novel approach to delineate source–sink zones based on trip route aggregation on road networks. We first represented original trajectories using road segment sequences and applied the Latent Dirichlet Allocation (LDA) model to associate trajectories with route semantics. We then ran a hierarchical clustering operation to aggregate trajectories with similar route semantics. Finally, we adopted an adaptive multi-variable agglomeration strategy to associate the trajectory clusters with each traffic analysis zone to delineating source and sink zones, with a trajectory topic entropy defined as an indicator to analyze the dynamic impact between the road network and source–sink zones. We used taxi trajectories in Xiamen, China, to verify the effectiveness of the proposed method.

Research on Capacity Configuration for Green Power Substitution in an Isolated Grid Containing Electrolytic Aluminum

The deployment of a green power alternative within an isolated network, powered by renewable energy sources, in the “Three North” region of China can facilitate the substitution of high-energy-consuming industrial loads with green power. However, an inadequate power supply configuration may lead to economic and reliability issues. To address this problem, effective capacity allocation within the green power alternative isolated network is proposed. The capacity allocation process starts with the design of a network structure that aligns with local conditions. Subsequently, a capacity allocation model is developed, considering economic factors, renewable energy utilization efficiency, and system reliability. The gray wolf optimizer is enhanced to establish a capacity allocation method for the green power alternative isolated network. This method is then employed to simulate and assess the performance of the network. The results indicate that the green alternative isolated grid can successfully facilitate green power substitution, satisfying the energy requirements of the loads.

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios.

Assessing the needs of patients with breast cancer and their families across various treatment phases using a Latent Dirichlet Allocation model: a text-mining approach to online health communities.

This study aimed to assess the different needs of patients with breast cancer and their families in online health communities at different treatment phases using a Latent Dirichlet Allocation (LDA) model. Using Python, breast cancer-related posts were collected from two online health communities: patient-to-patient and patient-to-doctor. After data cleaning, eligible posts were categorized based on the treatment phase. Subsequently, an LDA model identifying the distinct need-related topics for each phase of treatment, including data preprocessing and LDA topic modeling, was established. Additionally, the demographic and interactive features of the posts were manually analyzed. We collected 84,043 posts, of which 9504 posts were included after data cleaning. Early diagnosis and rehabilitation treatment phases had the highest and lowest number of posts, respectively. LDA identified 11 topics: three in the initial diagnosis phase and two in each of the remaining treatment phases. The topics included disease outcomes, diagnosis analysis, treatment information, and emotional support in the initial diagnosis phase; surgical options and outcomes, postoperative care, and treatment planning in the perioperative treatment phase; treatment options and costs, side effects management, and disease prognosis assessment in the non-operative treatment phase; diagnosis and treatment options, disease prognosis, and emotional support in the relapse and metastasis treatment phase; and follow-up and recurrence concerns, physical symptoms, and lifestyle adjustments in the rehabilitation treatment phase. The needs of patients with breast cancer and their families differ across various phases of cancer therapy. Therefore, specific information or emotional assistance should be tailored to each phase of treatment based on the unique needs of patients and their families.