Allocation Model Research Articles

A method for exploring consumer satisfaction factors using online reviews: A study on anti-cold drugs

Anti-cold drugs prove effective in alleviating cold symptoms and minimizing complications. In recent years, there has been a growing tendency among the public to purchase these drugs online, driven by the prevalence of influenza. Maintaining a high level of consumer satisfaction is crucial for online pharmaceutical platforms. This study introduces a Multi-Attribute Decision Making (MADM) method to analyze online reviews from 31,392 consumers at JD Pharmacy. Unlike previous studies, multiple analytical tools are employed jointly to identify factors influencing consumer satisfaction. Four dosage forms of anti-cold drugs (granule, tablet, syrup, and spray) were analyzed using the Latent Dirichlet Allocation Model (LDA). Three tools (topic coherence curve, topic perplexity curve, and pyLDAvis visualization) were jointly applied. Then five satisfaction factors were identified as logistics distribution, drug efficacy, online consultation service, drug prices, and packaging quality. PROMETHEE II method was applied to rank the above factors. The results revealed variations in consumer satisfaction among different drug dosage forms. We conducted a detailed analysis of these distinctions. The results from this study will provide an effective reference to improve consumer satisfaction.

Robust portfolio optimization model for electronic coupon allocation

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six type of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean–variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.

Optimizing Data Pipeline Efficiency with Machine Learning Techniques

In the era of big data, efficient data processing is crucial for timely insights and decision-making. Traditional data pipelines face challenges such as latency, scalability, and fault tolerance. This paper explores the application of machine learning (ML) techniques to optimize data pipeline efficiency. We propose a framework that integrates ML models for predictive resource allocation, anomaly detection, and dynamic scaling within data pipelines. Our experiments demonstrate significant improvements in processing speed, resource utilization, and reliability. Key Words: Data Engineering, Data Pipelines, Machine Learning, Predictive Resource Allocation, Anomaly Detection, Dynamic Scaling

Renewable energy allocation in Multi-Owned Buildings for Sustainable Transition: A novel Evidence-Based Decision-Making framework

Adopting Renewable Energy Systems (RES) in Multi-Owned Buildings (MOBs) is critical for achieving sustainability goals, yet the equitable allocation of energy from a jointly-owned RES to individual apartments remains overlooked in practice and literature. Current practices, rooted in models of common cost allocation, fail to address the dynamic traits of energy allocation and disregard the energy entitlement of each apartment, necessitating a tailored approach for renewable energy allocation in MOBs. This paper emphasises energy entitlement and introduces a novel, evidence-based decision-making framework assessing nine distinct energy allocation models for their suitability in diverse MOB typologies, characterised by physical and social factors, and presents a ranked list. Our findings reveal extensive variation in model suitability depending on the building typology. Equal energy allocation minimised financial disparities, while demand-based allocation was significantly effective for older, mid-rise buildings with fewer tenants. Conversely, the flat-fee model was found unsuitable regardless of building type. Furthermore, the study demonstrates that the suitable model for a building typology may not always align with the objectives of RES installation, thus endorsing an ‘objective proximity’ analysis. The proposed framework serves as a valuable guide for stakeholders, including the owners’ corporations, policymakers, and industries, to make well-informed decisions for a smooth transition to renewable energy. It lays the foundation for potential expansion to other building types while underscoring the necessity for adaptive policies in promoting RES adoption.

Case Article—Three Mountain Communications: Fairness Considerations for Workplace Task Allocation

The case study has two primary objectives: incorporating fairness considerations in managerial decision making and introducing task allocation using an optimization model. The case focuses on a call center that faces the issue of daily task allocation. The current self-selection-based allocation protocol has created friction among employees. To enable employee satisfaction and boost team morale, the call center needs to develop an allocation model that is perceived to be fair by employees. The case tends to elicit enthusiastic participation from students, especially on the theme of workplace fairness and the role of gender and individual constraints on employees’ ability to excel at the workplace. Overall, students believed the case was challenging and that it provided them skills to combine (i) optimization modeling and (ii) qualitative considerations for workplace fairness that they studied in management and leadership courses. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ited.2023.0044ca .

Multi-objective double layer water optimal allocation and scheduling framework combing the integrated surface water – groundwater model

Balancing the water consumption of agricultural and ecological is the key point of sustainable social and economic development in an inland river basin. The growth of desert riparian forests in inland river basins mainly depends on a certain phreatic water table depth (PWTD). The main object of this study was to allocate and schedule water resources to regulate the PWTD and satisfy agricultural water demand. Therefore, a multi-objective double layer optimal allocation and scheduling framework based on the computationally efficient integrated surface water-groundwater model (ISGWM), which can simulate the surface water processes, groundwater recharge and discharge processes, and PWTD changes, was constructed and applied to the mainstream of Tarim River Basin (TRB). The top layer model of the framework is an optimal ecological water allocation model, and its optimal allocation results are used as the initial solution of the bottom layer model. The results show that under 5 different inflow frequencies, the agricultural water shortage rate is 0, 17.38 %, 17.41 %, 14.06 %, and 19.94 %, respectively. The PWTD regulation has a great performance. After the optimal scheduling, the proportions of good growth of the control area behind the gate under different inflow frequencies were 98.18 %, 98.18 %, 98.18 %, 90.91 %, and 94.55 %. Agricultural water shortage is mainly due to the non-uniformity distribution of intra-annual inflow and the lack of controlling hydraulic engineering. The regulation of PWTD can guarantee the growth of desert riparian forests on both sides of the mainstream of TRB. Besides, we explored the feasibility of exploiting groundwater to supplement agricultural water consumption. The groundwater exploitation should be controlled within the scope of not causing excessive increase of PWTD (difference between PWTD and target depth <1 m), due to the groundwater exploitation to supplement agricultural water will lead to the increase of PWTD. Overall, this framework, which regulates the PWTD with the change of ecological water supply based on the ISGWM, provides a new idea for the allocation and scheduling of agricultural and ecological water resources in arid inland river basins. It also provides a new method for the coupled cooperative operation of surface water and groundwater.

Research on emergency decision quality evaluation and optimization basing on public sentiment big data analysis

The increasingly frequent occurrence of emergencies has led to significant losses, both human and economic. It is of the utmost importance to scientifically and effectively formulate emergency decision to prevent and reduce these losses. In this study, an evaluation model is developed for evaluating emergency decision quality based on fuzzy comprehensive evaluation method. The proposed model takes into consideration of the impact of public sentiment appeals on emergency decision in emergencies with the purpose of improving emergency decision quality. The “3·21” major explosion accident in Xiangshui County, Jiangsu Province, China, in March of 2019, is selected as a typical case, and the public sentiment big data is obtained from Weibo. The Latent Dirichlet Allocation (LDA) model and the TF-IDF algorithm are utilized to develop an emergency decision quality evaluation index system, from which public sentiment appeals are extracted and quantified. Furthermore, this study explores the changes of public sentiment attitude towards emergency decision through static and dynamic public sentiment analysis. Based on this, the emergency decision quality for the case event is evaluated by the proposed model. The effectiveness of the proposed approach is demonstrated with in-depth discussion and validation through this real case. The results indicate that the emergency decision quality of the Chinese government in this case is fairly good, yet exhibit room for further improvement. Optimization advice for emergency decision in emergencies is proposed accordingly. Empirical evidence suggests that the model proposed in this study can provide theoretical and methodological support for emergency response and government decision-making in the era of big data.

Principles and Optimization of China’s Unconventional Water Management: From a Brand-New Perspective of Responsibility Allocation

Unconventional water includes reclaimed water, harvested rainwater, desalinated seawater, and mine water. Unconventional water use is considered more of a “mandatory responsibility” in China. The initial allocation of unconventional water emphasizes quantity-centered responsibility allocation while the minimum utilization reflects this responsibility. The unconventional water use responsibility (UWUR) should be tailored to the characteristics of each area, moving away from a ‘more is better’ mindset. However, there is a large research gap in this field. This paper first presented six fundamental principles for unconventional water allocation. Ensuring fairness in allocation involves aligning the allocated amount with urban water usage characteristics. Hence, based on four key features, this paper integrated various socioeconomic and environmental factors to build an initial allocation model. To enhance efficiency, an optimal allocation model was constructed using the zero-sum gains–data envelopment analysis (ZSG-DEA) method. The models were then applied to Jiangsu Province, China, to verify their applicability. The results showed that the projected minimum UWUR allocation (unit: 100 million m3) for each city in 2025 is 1.482 (Nanjing), 1.501 (Wuxi), 0.919 (Xuzhou), 1.029 (Changzhou), 2.977 (Suzhou), 1.497 (Nantong), 0.818 (Lianyungang), 0.766 (Huai’an), 0.875 (Yancheng), 0.920 (Yangzhou), 0.790 (Zhenjiang), 0.858 (Taizhou), and 0.766 (Suqian). The rational and feasible results indicated that the allocation framework proposed in this paper has a certain practicability. Lastly, this paper considered the differences in unconventional water utilization conditions across 13 cities and proposed corresponding measures to improve the utilization. This paper represents a tentative exploration of unconventional water allocation in China and offers theoretical and practical insights for policy-makers to improve territorial spatial planning and sustainable water management.

Predictive air combat decision model with segmented reward allocation

In air combat missions, unmanned combat aerial vehicles (UCAVs) must take strategic actions to establish combat advantages, enabling effective tracking and attacking of enemy UCAVs. Currently, a lot of reinforcement learning algorithms are applied to the air combat mission of unmanned fighter aircraft. However, most algorithms can only select policies based on the current state of both sides. This leads to the inability to effectively track and attack when the enemy performs large angle maneuvering. Additionally, these algorithms cannot adapt to different situations, resulting in the unmanned fighter aircraft being at a disadvantage in some cases. To solve these problems, this paper proposes predictive air combat decision model with segmented reward allocation for air combat tracking and attacking. On the basis of the air combat environment, we propose the prediction soft actor-critic (Pre-SAC) algorithm, which combines the prediction of enemy states with the states of UCAV for model training. This enables the UCAV to predict the next move of the enemy UCAV in advance and establish a greater air combat advantage for us. Furthermore, by adopting a segmented reward allocation model and combining it with the Pre-SAC algorithm, we propose the segmented reward allocation soft actor-critic (Sra-SAC) algorithm, which solves the problem of UCAVs being unable to adapt to different situations. The results show that the prediction-based segmented reward allocation the Sra-SAC algorithm outperforms the traditional soft actor-critic (SAC) algorithm in terms of overall reward, travel distance, and relative advantage.

A Redundancy Allocation Model for Uncertainty Random Water Supply System in Water Saving Management Contract Projects

Water saving management contract (WSMC) projects provide advanced technology and management for a water supply system to achieve water conservation and set redundant components to ensure water supply reliability. Project managers focus on the reliability optimization problem and require redundancy allocation strategies of the above system. This paper presents an optimization method by dealing with the lifetime of the whole water supply system. Assuming the lifetimes of advanced components are uncertain variables and the old ones are random variables, a reliability optimization model of water supply systems is established based on chance theory, and the redundancy allocation solutions are obtained by an optimization toolkit. A WSMC case in Shenzhen, China is studied and the results show that the reliability of the water supply system has been in a high state based on the allocation strategy. This study provides theoretical support for improving water-saving safety and popularizing the WSMC service mechanism.

Is the Norwegian stepped care model for allocation of patients with mental health problems working as intended? A cross-sectional study

ABSTRACT Objective Stepped care models are frameworks for mental health care systems in several countries. According to Norwegian guidelines, individuals with mental health problems of mild severity should be treated in community mental health services, moderate severity in specialist mental health services, while complex/severe problems are often a shared responsibility. This study investigated whether patients are allocated as intended. Methods In a cross-sectional study, 4061 outpatients recruited from community- and specialist mental health services reported demographic variables, symptoms of anxiety/depression, functional impairment, health status, and sick leave status. The community sample consisted of two subsamples: mild/moderate problems and complex problems. Results There was substantial overlap (80%–99%) of symptoms, impairment, and health between community- and specialist mental health services. More impairment, worse health, lower age, and being male were associated with treatment at specialist level compared to community mild/moderate. Better health, being in a relationship, and lower age were associated with specialized treatment compared to community complex group. Conclusion The limited association between treatment level and symptoms and functional impairment reveals inconsistencies between treatment guidelines and clinical practice. How the existing organization affects patient outcomes and satisfaction should be investigated further.

Static Task Allocation Method for Multi-Machines in Cooperative Operations Combining OGFR-GA and MLW-Prim

Aiming at the operational efficiency of small farm machinery groups in cooperative operations in hilly areas, this study proposes a static task allocation model. This method combines the optimal gene fragment retention method based on a genetic algorithm (OGFR-GA) and the method for generating multi-loop weighted connected graphs based on Prim’s algorithm (MLW-Prim). The collaborative objective function for the harvester group was established by considering factors such as operation time, fuel consumption, and distance. The OGFR-GA was designed and applied multiple times to obtain several optimal gene fragments corresponding to the number of farm machines. These fragments were used as critical paths in the weighted connected graph generated based on farm machinery performance parameters and task parameters. The MLW-Prim method was proposed to construct this weighted connected graph and realize the static task allocation model for multi-machine cooperative operations. Simulation experiments showed that the model combining OGFR-GA and MLW-Prim achieved optimal values with fewer iterations, and reduced both group cost and cost variance compared to traditional algorithms. This method meets the static task allocation needs for multi-machine cooperative operations in agricultural production and can be integrated with intelligent operations in mountainous and hilly regions, laying a theoretical foundation for improving efficiency.

Body Size Remains the Major Source of Sex Disparity Despite Updated Liver Transplant Allocation Policies.

Efforts to address US liver transplant (LT) access inequities continue, yet disparities linked to candidate traits persist. Analyzing national registry data pre- and post-Acuity Circle (AC) policy, our study assessed the impact of low body surface area (BSA) on LT waitlist mortality. The outcomes of LT candidates listed in the pre-AC era (n = 39 227) and post-AC (n = 38 443) were compared for patients with low BSA (22.9% pre-AC and 23.3% post-AC). Fine-Gray competing risk models highlighted that candidates with low BSA had a lower likelihood of LT both pre-AC (hazard ratio [HR] 0.93; 95% confidence interval [CI], 0.92-0.95) and post-AC (HR 0.96; 95% CI, 0.94-0.98), with minimal improvement in waitlist mortality/dropout risk from pre-AC (HR 1.15; 95% CI, 1.09-1.21) to post-AC (HR 1.13; 95% CI, 1.06-1.19). Findings were mostly reaffirmed by Cox regression models incorporating the trajectory of Model for End-stage Liver Disease (MELD) scores as time-dependent covariates. Regions 3, 5, and 7 showed notable LT waitlist disparities among low BSA patients post-AC policy. Causal mediation analysis revealed that low BSA and the difference between MELD-sodium and MELD 3.0 (MELD_D, as a proxy for the potential impact of the introduction of MELD 3.0) largely explained the sex disparity in AC allocation (percent mediated 90.4). LT waitlist disparities for female candidates persist, largely mediated by small body size. Although MELD 3.0 may reduce some disparities, further body size adjustments for in allocation models are justified.

GEMA-Na and MELD 3.0 severity scores to address sex disparities for accessing liver transplantation: a nationwide retrospective cohort study

The Gender-Equity Model for liver Allocation corrected by serum sodium (GEMA-Na) and the Model for End-stage Liver Disease 3.0 (MELD 3.0) could amend sex disparities for accessing liver transplantation (LT). We aimed to assess these inequities in Spain and to compare the performance of GEMA-Na and MELD 3.0. Nationwide cohort study including adult patients listed for a first elective LT (January 2016-December 2021). The primary outcome was mortality or delisting for sickness within the first 90 days. Independent predictors of the primary outcome were evaluated using multivariate Cox's regression with adjusted relative risks (RR) and 95% confidence intervals (95% CI). The discrimination of GEMA-Na and MELD 3.0was assessed using Harrell c-statistics (Hc). The study included 6071 patients (4697 men and 1374 women). Mortality or delisting for clinical deterioration occurred in 286 patients at 90 days (4.7%). Women had reduced access to LT (83.7% vs. 85.9%; p=0.037) and increased risk of mortality or delisting for sickness at 90 days (adjusted RR=1.57 [95% CI 1.09-2.28]; p=0.017). Female sex remained as an independent risk factor when using MELD or MELD-Na but lost its significance in the presence of GEMA-Na or MELD 3.0. Among patients included for reasons other than tumours (n=3606; 59.4%), GEMA-Na had Hc=0.753 (95% CI 0.715-0.792), which was higher than MELD 3.0 (Hc=0.726 [95% CI 0.686-0.767; p=0.001), showing both models adequate calibration. GEMA-Na and MELD 3.0 might correct sex disparities for accessing LT, but GEMA-Na provides more accurate predictions of waiting list outcomes and could be considered the standard of care for waiting list prioritization. Instituto de Salud Carlos III, Agencia Estatal de Investigación (Spain), and European Union.

Including glutamine in a resource allocation model of energy metabolism in cancer and yeast cells

Energy metabolism is crucial for all living cells, especially during fast growth or stress scenarios. Many cancer and activated immune cells (Warburg effect) or yeasts (Crabtree effect) mostly rely on aerobic glucose fermentation leading to lactate or ethanol, respectively, to generate ATP. In recent years, several mathematical models have been proposed to explain the Warburg effect on theoretical grounds. Besides glucose, glutamine is a very important substrate for eukaryotic cells—not only for biosynthesis, but also for energy metabolism. Here, we present a minimal constraint-based stoichiometric model for explaining both the classical Warburg effect and the experimentally observed respirofermentation of glutamine (WarburQ effect). We consider glucose and glutamine respiration as well as the respective fermentation pathways. Our resource allocation model calculates the ATP production rate, taking into account enzyme masses and, therefore, pathway costs. While our calculation predicts glucose fermentation to be a superior energy-generating pathway in human cells, different enzyme characteristics in yeasts reduce this advantage, in some cases to such an extent that glucose respiration is preferred. The latter is observed for the fungal pathogen Candida albicans, which is a known Crabtree-negative yeast. Further, optimization results show that glutamine is a valuable energy source and important substrate under glucose limitation, in addition to its role as a carbon and nitrogen source of biomass in eukaryotic cells. In conclusion, our model provides insights that glutamine is an underestimated fuel for eukaryotic cells during fast growth and infection scenarios and explains well the observed parallel respirofermentation of glucose and glutamine in several cell types.

Unreliability tracing of power systems with reservoir hydropower based on a temporal recursive model

AbstractPower system unreliability tracing model allocates the system's reliability index to individual components, identifying potential weaknesses. This study expands its scope by considering the impact of storage resources. Unreliable factors leading to load shedding are categorized into two groups: objective factors inherent to the component and insufficient storage resources. The latter requires a retrospective analysis of other components that caused unreliability previously. When allocating responsibility for load shedding at a certain time, it begins by allocating it among components based on differences between fixed expected output and actual supply. Expected output insufficiency is considered as the unreliable factor. This insufficiency due to insufficient storage resources is then decomposed into segments, each caused by excessive output in earlier instances of the same component. The expected output excess is attributed to the expected output insufficiency of other components in previous times, for which responsibility has been allocated to each component. Consequently, the expected output insufficiency at a particular time can be traced back based on a temporal recursive model, with the load shedding further allocated to components before that time. Case studies based on several systems demonstrate that the proposed model's allocation results are reasonable and more accurate than the traditional model.

On a fair and risk‐averse urban air mobility resource allocation problem under demand and capacity uncertainties

AbstractUrban air mobility (UAM) is an emerging air transportation mode to alleviate the ground traffic burden and achieve zero direct aviation emissions. Due to the potential economic scaling effects, the UAM traffic flow is expected to increase dramatically once implemented, and its market can be substantially large. To be prepared for the era of UAM, we study the fair and risk‐averse urban air mobility resource allocation model (FairUAM) under passenger demand and airspace capacity uncertainties for fair, safe, and efficient aircraft operations. FairUAM is a two‐stage model, where the first stage is the aircraft resource allocation, and the second stage is to fairly and efficiently assign the ground and airspace delays to each aircraft provided the realization of random airspace capacities and passenger demand. We show that FairUAM is NP‐hard even when there is no delay assignment decision or no aircraft allocation decision. Thus, we recast FairUAM as a mixed‐integer linear program (MILP) and explore model properties and strengthen the model formulation by developing multiple families of valid inequalities. The stronger formulation allows us to develop a customized exact decomposition algorithm with both benders and L‐shaped cuts, which significantly outperforms the off‐the‐shelf solvers. Finally, we numerically demonstrate the effectiveness of the proposed method and draw managerial insights when applying FairUAM to a real‐world network.

Energy Management Strategy for Wind Solar Storage Microgrid Based on Improved Ant Lion Optimizer

The energy management of microgrids involves optimizing the capacity configuration, which significantly impacts the economic and stable operation of microgrids. This paper presents a control strategy for microgrid operation that effectively manages distributed power sources and energy storage to optimize capacity configuration. A mathematical optimization model for microgrid energy management is established considering minimum annual cost and optimal scale constraints. The traditional Ant Lion Optimizer (ALO) is improved by using dynamic weight coefficients and chaotic mapping to enhance the diversity of the population and improve the convergence of the algorithm. This can effectively avoid local optimal solutions and premature problems, and improve the convergence speed and search ability of the ALO algorithm. Based on this control strategy and improved ALO algorithm, simulations and tests were conducted using actual data from an independent microgrid, resulting in the optimal solution for the microgrid capacity allocation model. The case study results validate the practicality of the proposed microgrid operation control strategy as well as the superiority of the improved ant colony optimization algorithm.

Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study

Applying artificial intelligence (AI) and machine learning (ML) techniques to optimize waste management strategies, focusing on enhancing economic efficiency and reducing environmental impact, is vital. The study utilized ML models to analyze and forecast waste generation trends, assess the viability of various waste management methods, and develop optimization models for resource allocation and operational efficiency. The research employs the World Bank’s comprehensive waste management dataset. After rigorous data preprocessing, including cleaning and feature selection, a variety of ML techniques, such as regression models, classification algorithms like Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and optimization algorithms, including linear programming, are applied. Unlike other research, this study achieved 85 % accuracy on predictive analytics models for forecasting waste generation trends, primarily attributed to integrating more diverse data sets, including socio-economic factors. Also, the optimization resource allocation achieved a 15 % increase in operational efficiency. These findings provide significant insights for policymakers and urban planners, suggesting that integrating ML in waste management can lead to more sustainable and cost-effective practices. This paper demonstrates the transformative potential of ML in optimizing waste management strategies, offering a pathway towards more sustainable and economically viable waste management solutions globally.

Investment allocation in an adjustment-cost production technology framework for two-stage network structures

Effective resource allocation can assist organizations in utilizing infinite resources, thereby improving efficiency and productivity. For an organization, investment is an essential production resource, and how to appropriately allocate investment is one of the most important decisions they need to make. This study aims to rationally allocate investment across each stage of two-stage network structure under two scenarios. We first propose general investment allocation model with adjustment costs to efficiently allocate investments across each stage. Then, given a fixed total investment, we use the DEA-based fixed cost allocation method to achieve the optimal allocation across stages, and we also incorporate two cooperative and noncooperative relationships between two stages. Additionally, capital stock efficiency is defined to analyze the relationship between optimal and actual capital stocks, clarifying the degree to which the optimal capital stock is reached. Finally, the proposed approach is applied to 14 A-share listed companies in China, and suggestions on the optimal amount of investment allocation and capital stock required are provided to facilitate organizational production.