Allocation Model Research Articles

An inventory allocation model for cost reduction in the supply chain of a payment means company

The Supply Chain Management (SCM) has been gaining greater relevance in the sector, leading companies to improve the level of customer service and keep costs under control. However, it was identified that SCM is a less explored field in the acquirer market, so this work is an opportunity to contribute to the academic research. It was observed the opportunity to build an inventory model considering transportation as a variable cost, unlike most models found in literature. The objective of the study was to develop an inventory allocation model for the SIMcard chain in a Brazilian company in the acquiring sector, keeping the service level agreed with the customer. Inventory and transport costs were calculated for different supply cycles for all SIMcards operators and transportation hubs, to obtain the lowest total cost configuration in the supply chain. The results show that the costs cannot be considered individually and considering both costs, transport and inventory, there is an opportunity to increase efficiency in the studied operation of this work.

Heuristic Optimal Scheduling for Road Traffic Incident Detection Under Computational Constraints

The intelligent monitoring of road surveillance videos is a crucial tool for detecting and predicting traffic anomalies, swiftly identifying road safety risks, rapidly addressing potential hazards, and preventing accidents or secondary incidents. With the vast number of surveillance cameras in operation, conducting traditional real-time video analysis across all cameras at once requires substantial computational resources. Alternatively, methods that employ periodic camera patrol analysis frequently overlook a significant number of anomalous traffic events, thereby hindering the effectiveness of traffic event detection. To overcome these challenges, this paper introduces a heuristic optimal scheduling approach designed to enhance traffic event detection efficiency while operating within limited computational resources. This method leverages historical data and prior knowledge to compute a weighted event feature value for each camera, providing a quantitative measure of its detection efficiency. To optimize resource allocation, a cyclic elimination mechanism is implemented to exclude low-performing cameras, enabling the dynamic reallocation of resources to higher-performing cameras, thereby enhancing overall detection performance. Finally, the effectiveness of the proposed method is validated through a case study conducted in a representative region of a major metropolitan city in China. The results revealed a substantial improvement in traffic event detection efficiency, with increases of 40%, 28%, 17%, and 28% across different time periods when compared to the pre-optimized state. Furthermore, the proposed method outperformed existing resource scheduling algorithms in terms of average load degree, load balance degree, and higher computational resource utilization. By avoiding the common issues of resource wastage and insufficiency often found in static allocation models, this approach offers greater flexibility and adaptability in computational resource scheduling, thereby effectively addressing the practical demands of traffic anomaly detection and early warning systems.

Concurrent Mentions of Vaping and Alcohol on Twitter: Latent Dirichlet Analysis.

Co-use of alcohol and e-cigarettes (often called vaping) has been linked with long-term health outcomes, including increased risk for substance use disorder. Co-use may have been exacerbated by the COVID-19 pandemic. Social networking sites may offer insights into current perspectives on polysubstance use. The aims of this study were to investigate concurrent mentions of vaping and alcohol on Twitter (subsequently rebranded X) during a time of changing vaping regulations in the United States and the emergence of the COVID-19 pandemic. Tweets including both vape- and alcohol-related terms posted between October 2019 and September 2020 were analyzed using latent Dirichlet allocation modeling. Distinct topics were identified and described. Three topics were identified across 6437 tweets: (1) flavors and flavor ban (n=3334, 51.8% of tweets), (2) co-use discourse (n=1119, 17.4%), and (3) availability and access regulation (n=1984, 30.8%). Co-use discussions often portrayed co-use as positive and prosocial. Tweets focused on regulation often used alcohol regulations for comparison. Some focused on the perceived overregulation of vaping (compared to alcohol), while others supported limiting youth access but not at the expense of adult access (eg, stronger age verification over product bans). Across topics, vaping was typically portrayed as less harmful than alcohol use. The benefits of flavors for adult smoking cessation were also discussed. The distribution of topics across time varied across both pre- and post-regulatory change and pre- and post-COVID-19 pandemic declaration periods, suggesting shifts in topic focus salience across time. Co-use discussions on social media during this time of regulatory change and social upheaval typically portrayed both vaping and alcohol use in a positive light. It also included debates surrounding the differences in regulation of the 2 substances-particularly as it related to limiting youth access. Emergent themes from the analysis suggest that alcohol was perceived as more harmful but less regulated and more accessible to underage youth than vaping products. Frequent discussions and comparisons of the 2 substances as it relates to their regulation emphasize the still-evolving vaping policy landscape. Social media content analyses during times of change may help regulators and policy makers to better understand and respond to common concerns and potential misconceptions surrounding drug-related policies and accessibility.

Impact of Arable Land Abandonment on Crop Production Losses in Ukraine During the Armed Conflict

The outbreak of Russia-Ukraine conflict casted an impact on the global food market, which was believed to be attributed to that Ukraine has suffered significant production losses due to cropland abandonment. Nevertheless, recent outbreaks of farmer protests against Ukraine’s grain exports demonstrated that the production losses might not be as severe as previous estimates. By utilizing the adaptive threshold segmentation method to extract abandoned cropland from the Sentinel-2 high-resolution imagery and calibrating the spatial production allocation model’s gridded crop production data from Ukraine’s statistical data, this study explicitly evaluated Ukraine’s crop-specific production losses and the spatial heterogeneity. The results demonstrated that the estimated area of abandoned cropland in Ukraine ranges from 2.34 to 2.40 million hectares, constituting 7.14% to 7.30% of the total cropland. In Ukrainian-controlled zones, this area spans 1.44 to 1.48 million hectares, whereas in Russian-occupied areas, it varies from 0.90 to 0.92 million hectares. Additionally, the total production losses for wheat, maize, barley, and sunflower amount to 1.92, 1.67, 0.70, and 0.99 million tons, respectively, with corresponding loss ratios of 9.10%, 7.48%, 9.54%, and 8.67%. Furthermore, production losses of wheat, barley, and sunflower emerged in both the eastern and southern states adjacent to the conflict frontlines, while maize losses were concentrated in the western states. The findings imply that Ukraine ought to streamline the food transportation channels and maintain stable agricultural activities in regions with high crop production.

Internal physiological drivers of leaf development in trees: Understanding the relationship between non‐structural carbohydrates and leaf phenology

Abstract Plant phenology is crucial for understanding plant growth and climate feedback. It affects canopy structure, surface albedo, and carbon and water fluxes. While the influence of environmental factors on phenology is well‐documented, the role of plant intrinsic factors, particularly internal physiological processes and their interaction with external conditions, has received less attention. Non‐structural carbohydrates (NSC), which include sugars and starch essential for growth, metabolism and osmotic regulation, serve as indicators of carbon availability in plants. NSC levels reflect the carbon balance between photosynthesis (source activity) and the demands of growth and respiration (sink activity), making them key physiological traits that potentially influence phenology during critical periods such as spring leaf‐out and autumn leaf senescence. However, the connections between NSC concentrations in various organs and phenological events are poorly understood. This review synthesizes current research on the relationship between leaf phenology and NSC dynamics. We qualitatively delineate seasonal NSC variations in deciduous and evergreen trees and propose testable hypotheses about how NSC may interact with phenological stages such as bud break and leaf senescence. We also discuss how seasonal variations in NSC levels, align with existing conceptual models of carbon allocation. Accurate characterization and simulation of NSC dynamics are crucial and should be incorporated into carbon allocation models. By comparing and reviewing the development of carbon allocation models, we highlight the shortcomings in current methodologies and recommend directions to address these gaps in future research. Understanding the relationship between NSC, source–sink relationships, and leaf phenology poses challenges due to the difficulty of characterizing NSC dynamics with high temporal resolution. We advocate for a multi‐scale approach that combines various methods, which include deepening our mechanistic understanding through manipulative experiments, integrating carbon sink and source data from multiple observational networks with carbon allocation models to better characterize the NSC dynamics, and quantifying the spatial pattern and temporal trends of the NSC‐phenology relationship using remote sensing and modelling. This will enhance our comprehension of how NSC dynamics impact leaf phenology across different scales and environments. Read the free Plain Language Summary for this article on the Journal blog.

A multiobjective optimization model for allocating water quantity and quality in the Yangtze and Yellow River basins

Rapid urbanization and industrial growth have led to water quantity and quality problems. Most current water allocation models focus solely on physical water and neglect the optimization of the water quantity and quality considering the water footprint. Therefore, this study aimed to establish an optimal water resource allocation model for industrial sectors based on the water footprint to identify the most effective strategies for determining the water quantity and quality distribution in river basins. First, we constructed an improved water footprint accounting system using input‒output tables to quantify the total water footprint, grey water footprint, and virtual water trade. Second, we constructed a multiobjective optimal water resource allocation model by considering the water footprint as a decision variable and combining regional economic development and productivity differences. Finally, we compared the water allocation results obtained for the Yellow River Basin, a typical “water-quantity water-scarce region” in China, with those of the Yangtze River Basin, a “water-quality water-scarce region.” The results indicate that the total water footprints of the Yellow and Yangtze River Basins were 211.37 and 317.83 billion m³, respectively, prior to optimization. After optimization, the footprints were 199.54 and 306.03 billion m³, respectively. Water resources have been reallocated through virtual water trade strategies to effectively alleviate both water quantity and quality problems. Virtual water trade strategies have emerged as policy tools capable of mitigating mismatches between industrial systems and regional water resource allocation and providing a solution to physical–virtual water system challenges within river basins.

Optimal allocation model of port emergency resources based on the improved multi-objective particle swarm algorithm and TOPSIS method

The busy maritime traffic and occurrence of ship accidents have led to a growing recognition of the necessity to maritime emergency resources allocation. The port emergency resource allocation is of significant importance for the maritime safety. This paper presents an optimized allocation model for port emergency resources based on the improved multi-objective particle swarm optimization (IMOPSO). The model introduces the crowding distance and improves the external archive update strategy. The particle inertia weight is adjusted and a dynamic mutation operator is incorporated. The entropy-weighted technique for order preference by similarity to an ideal solution method is also employed to identify the optimal solution. A comprehensive comparison with MOPSO has been presented and discussed. Three metrics of generational distance (GD), spacing (SP) and delta indicator (Δ) were employed for performance evaluation. The results demonstrated that the proposed IMOPSO algorithm exhibited superior performance and robustness, with average values of GD = 0.0386, SP = 0.0023 and Δ = 0.6468 for ZDT test functions. The model efficacy is further validated by a case study of oil spill dispersant configuration at Zhanjiang Port, China. Seven alternative schemes have been obtained, among which the optimal scheme is selected by the entropy-weighted TOPSIS method. The overall cost is potentially to be reduced by approximately 33.03 %. The present study would provide a reference for the water pollutant control and environmental management in port waters.

Research progress and hot topics of distributed photovoltaic: Bibliometric analysis and Latent Dirichlet Allocation model

Research progress and hot topics of distributed photovoltaic: Bibliometric analysis and Latent Dirichlet Allocation model

Two‐Way Option Contracts That Facilitate Adaptive Water Reallocation in the Western United States

AbstractMany water markets in the western United States (U.S.) have the ability to reallocate water temporarily during drought, often as short‐term water rights leases from lower value irrigated activities to higher value urban uses. Regulatory approval of water transfers, however, typically takes time and involves high transaction costs that arise from technical and legal analyses, discouraging short‐term leasing. This leads municipalities to protect against drought‐related shortfalls by purchasing large volumes of infrequently used permanent water rights. High transaction costs also result in municipal water rights rarely being leased back to irrigators in wet or normal years, reducing agricultural productivity. This research explores the development of a multi‐year two‐way option (TWO) contract that facilitates leasing from agricultural‐to‐urban users during drought and leasing from urban‐to agricultural users during wet periods. The modeling framework developed to assess performance of the TWO contracts includes consideration of the hydrologic, engineered, and institutional systems governing the South Platte River Basin in Colorado where there is growing competition for water between municipalities (e.g., the city of Boulder) and irrigators. The modeling framework is built around StateMod, a network‐based water allocation model used by state regulators to evaluate water rights allocations and potential rights transfers. Results suggest that the TWO contracts could allow municipalities to maintain supply reliability with significantly reduced rights holdings at lower cost, while increasing agricultural productivity in wet and normal years. Additionally, the TWO contracts provide irrigators with additional revenues via net payments of option fees from municipalities.

The potential mechanisms underlying the effect of acute alcohol use on duration perception.

Acute alcohol consumption has been found to cause duration perception distortions, but the directions of these distortions are not consistent. The mechanisms underlying this effect are also unclear. The present study seeks to elucidate the effect of acute alcohol consumption on duration perception and the mechanisms involved. Forty-one participants in the placebo group and 40 in the alcohol group completed time bisection tasks, attentional network tests, digit span backward tests and arousal reports to evaluate their duration perception, attentional network, working memory capacity and arousal. The results showed that the alcohol group overestimated duration compared to the placebo group. The alcohol group also showed increased arousal, impaired executive control of attention and reduced working memory capacity. Arousal mediated the effect of acute alcohol consumption on duration perception, whilst working memory capacity masked this effect. The findings are discussed based on the Scalar Timing Model and the Cognitive Resource Allocation Model.

Resilience enhancement of cyber-physical distribution systems via mobile power sources and unmanned aerial vehicles

Mobile power sources (MPS) and unmanned aerial vehicles (UAV) are critical and flexible resources to enhance the resilience of cyber–physical distribution systems. However, they are usually independently planned and dispatched. In this paper, considering the cyber–physical interdependence, topology reconfiguration and planned distribution generator islanding, an allocation and dispatch strategy of MPSs and UAVs is proposed. Before an event, a two-stage stochastic optimization based allocation model is built to pre-position MPSs and UAVs considering the uncertainty of events. After the event, a dispatch model is proposed to identify routings of MPSs and UAVs to restore electricity services. Note that both the models are mixed-integer nonlinear three-dimensional (3D) problems. As the optimal service radius and height of a UAV are independent with other variables, these two models are decomposed into two parts, i.e., one part to calculate the optimal service radius and height, and the other to identify resilience enhancement strategy. Then the two models are transformed into a mixed-integer convex programming solved by Progressive Hedging algorithm and a mixed-integer second-order cone programming, respectively. The effectiveness is verified on the modified IEEE 33-node and 123-node test systems. Numerical results highlight the necessity of co-optimizing MPSs and UAVs on cyber–physical distribution systems.

Impact of on-topic and off-topic discussions on member participation and contribution in a common-identity online community

This study investigates the effects of various discussion topics on individuals’ decisions to join and contribute to an online community. Based on their relevancy to the community's interests and goals, we distinguish between on-topic (relevant) and off-topic (irrelevant) discussions. The relationships among group discussion, new member acquisition, and current member contribution are simultaneously modeled and estimated using data collected from a question-and-answer website. We employ a latent Dirichlet allocation model to identify different topics posted by community members. The relevancy of each topic is assessed based on relevancy scores collected through a survey. The results suggest that on-topic discussions generally increase the number of new and active members. Intriguingly, off-topic discussions can influence new member acquisition both positively and negatively, while their effect on current member contributions appears to be positive. The implications of this study for sustaining online communities are discussed.

Exploring the Spatially Compounding Multi‐Sectoral Drought Vulnerabilities in Colorado's West Slope River Basins

AbstractThe state of Colorado's West Slope Basins are critical headwaters of the Colorado River and play a vital role in supporting Colorado's local economy and natural environment. However, balancing the multi‐sectoral water demands in the West Slope Basins while maintaining crucial downstream deliveries to Lake Powell is an increasing challenge for water managers. Internal variability of the hydroclimatic system and climate change complicate future vulnerability assessments. This work contributes a detailed accounting of multi‐sectoral drought vulnerability in the West Slope Basins and the impacts of drought on downstream deliveries. We first introduce a novel multi‐site Hidden Markov Model (HMM)‐based synthetic streamflow generator to create an ensemble of streamflows for all West Slope basins that better characterizes the region's drought extremes. We capture the effects of climate change by perturbing the HMM to generate an ensemble of streamflows reflecting plausible changes in climate. We then route both ensembles through StateMod, Colorado's water allocation model, to evaluate spatially compounding drought impacts across the West Slope Basins. Our results illustrate how drought events emerging from the system's stationary internal variability in the absence of climate change can significantly impact local water uses and deliveries to Lake Powell, exceeding extreme conditions in the historical record. Further, we find that even modest climate change can cause a regime shift where historically low downstream delivery volumes and extreme drought impacts become routine. These results can inform future Colorado River planning efforts, and our methodology can be expanded to other snow‐dominated regions that face persistent droughts.

빅데이터 기반 지역사회개발 이슈 분석의 의미: 청도군 지역사회개발에 관한 LDA와 DMR 토픽모델링을 사례로

This study aims to discover the utility of big data analysis in research related to community development and to derive meaningful implications. To achieve this, the case of Cheongdo-gun, despite of a designated depopulation area, experiencing a rapid increase in temporary residents, was examined. Alongside a literature review on the region, Latent Dirichlet Allocation (LDA) and Dirichlet Multinomial Regression (DMR) topic modeling were conducted on 7,599 news articles from 89 media outlets collected from BigKinds between 2014 and 2023. This analysis enabled an understanding of the interrelations and changes among community development issues in Cheongdo-gun, such as cultural and artistic content, ecological and historical tourism, population influx policies, local festivals, and community activities. This study is significant in that it demonstrates how LDA and DMR topic modeling, using big data, objectively synthesizes specific issues within the sociocultural context of the community development.

Bridging the Disparity: An Optimization Model for Equitable Allocation of Urban and Rural Educational Resources in Western China

The persistent disparity in educational resources between urban and rural areas in western China remains a critical barrier to achieving educational equity. This study addresses the critical issue by developing an innovative optimization model. The model aims to allocate financial, human, and physical resources effectively to minimize the educational quality gap between these regions. Using information from the China Education Panel Survey (CEPS) and concentrating on Xinjiang as a representative instance, our design integrates budget constraints, minimum resource requirements, and a unique regional prioritization factor. The goal of the objective function is to reduce the squared disparity in educational quality between city and rural regions. It thinks about variables such as Teacher-Student Ratio (TSR), Per Capita Investment (PCI), and Teaching Equipment Perfection (TEP). Our searchings for show that strategic resource distribution can considerably lessen educational inequalities in between metropolitan and country setups, especially by focusing on underdeveloped rural regions. Sensitivity analysis shows that customizing the local prioritization specification makes it possible for versatile policy strategies, ranging from equitable distribution to plainly sustaining rural regions. This study presents a pragmatic structure for boosting resource allotment in education. It supplies vital understandings for policymakers aiming to promote an extra equitable educational environment in Western China.

ON THE SOLUTION UNIQUENESS IN PORTFOLIO OPTIMIZATION AND RISK ANALYSIS

In this paper, we consider the issue of solution uniqueness of the mean-deviation portfolio optimization problem and its inverse for asset returns distributed over a finite number of scenarios. Due to the asymmetry of returns, the risk is assessed by a general deviation measure introduced by Rockafellar et al. [(2006b) Optimality conditions in portfolio analysis with general deviation measures, Mathematical Programming 108, 515–540] instead of the standard deviation as in the classical Markowitz optimization problem. We demonstrate that, in general, one cannot expect the uniqueness of Pareto-optimal profit sharing in cooperative investment and the uniqueness of solutions in the mean-deviation Black–Litterman asset allocation model. For a large class of deviation measures, we provide a resolution of the above nonuniqueness issues based on the principle of law-invariance. We provide several examples illustrating the nonuniqueness and the law-invariant solution.

Mapping the Evolving Landscape of Library-Related Policies in China: A Topic Modeling Approach

This study aims to investigate the thematic characteristics and evolutionary patterns of library policies in China from 1949 to 2023. Employing the Latent Dirichlet Allocation (LDA) model, the research analyzed 2,703 Chinese government-issued policies related to libraries during this period, identifying key topics and tracing their developmental trajectories. The study recognized four distinct developmental phases, five major topics, and the underlying connections among feature terms within different topics. It discussed the corresponding topic strengths and evolving trends in each phase. Library policies in China are predominantly centered on five key domains: 1) spatial planning and architectural integration, 2) information management and transparency, 3) legal regulations and supervisory enforcement, 4) heritage preservation and sustainable development, and 5) community inclusion and accessibility. Notably, spatial planning and architectural integration consistently garner substantial attention, highlighting their enduring prominence, while there is relatively less emphasis on community inclusion and accessibility, as well as heritage preservation and sustainable development. The contribution of this study lies in conducting a semantic exploration of the overall transformation in library-related policies in China, validating the effectiveness of the LDA model in identifying topics within policy texts, and expanding the research perspective on library policy evolution.

Dimensions of Job Demands Among New-Generation Employees Based on Online Reviews by Employees

Based on employees’ online reviews, this article analyzes the dimensions of job demand for new-generation employees, using a combination model of Latent Dirichlet Allocation, decision-making trials and evaluation laboratory, interpretative structural modeling, and cross-impact matrix multiplication (LDA—DEMATEL—ISM—MICMAC). The results show that job demand is composed of 10 dimensions, and there is significant interdependence between the dimensions. Changing one dimension will quickly affect the other dimensions. The dimension with the greatest influence degree is leadership and team atmosphere (s1), while the dimension with the highest affected degree and centrality degree is welfare and promotion (s3). Leadership and team atmosphere (s1), company culture and industry (s4), overall environment and platform (s5), and platform and technology (s7) were identified as the key factors. They play a causal role in job demand and have a significant impact on other dimensions. Dimensions such as working relationship and intensity (s2), welfare and promotion (s3), opportunity and resources (s6), business and industry development (s8), corporate prospects and personal development (s9), and work stress and position (s10) are affected by other dimensions and require special attention. The underlying need is present for the dimension of leadership and team atmosphere (s1), which has a direct or indirect impact on other dimensions in different ways.

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Dynamic Flexible Allocation of Slots in Container Line Transport

Due to the imbalance between supply and demand, liner container transportation often faces the problem of low slot utilization, which will occur in the shipping process, such as dry container demand exceeding the available dry slots and reefer slots not being fully utilized. This makes it important and challenging to maintain a balance between the actual demand and the limited number of slots allocated for liner container transport. Therefore, this study proposes a flexible allocation method: expanding the types of containers that can be loaded in the same slot. This method is suitable for handling each dynamic arrival container booking request by shipping enterprises, making decisions to accept or reject, and flexibly allocating shipping slots. In order to maximize the total revenue generated by accepting container booking requests during the entire booking acceptance cycle, we establish a dynamic programming model for the flexible allocation of slots. For model solving, we use the Q-learning reinforcement learning algorithm. Compared with traditional heuristic algorithms, this algorithm can improve solving efficiency and facilitate decision-making at the operational level of shipping enterprises. In terms of model performance, examples of different scales are used for comparison and training; the results are compared with the model without flexible allocation, and it is proved that the model proposed in this paper can obtain higher returns than the model without flexible allocation. The results show that the model and Q-learning algorithm can help enterprises solve the problem of the flexible allocation of shipping slots, and thus, this research has practical significance.