Capacity Constraints Research Articles

A multi-objective scheduling model in medical tourism centers considering multi-task staff training

In the present era, hospitals are actively encouraging their International Patient Departments (IPD) to provide high-quality medical services at a cost-effective rate for international patients. To accomplish this objective, several crucial factors must be taken into account, including capacity constraints, hospital waiting lists, and the presence of a highly skilled workforce. The waiting time experienced by patients is a significant indicator of the quality of hospital services and is often used by patients to evaluate healthcare professionals, sometimes even surpassing their knowledge and expertise. This becomes particularly critical when dealing with international patients as longer waiting times indirectly lead to additional accommodation costs. One potential solution is to schedule international patients on different days within a specific timeframe to minimize patient flow time. However, this can only be realistically achieved if staff productivity is enhanced through in-service training. These challenges, coupled with the uncertainty surrounding treatment duration, create a complex problem for IPDs. This study proposes an optimization approach for scheduling medical tourists who visit destination medical centers with two primary objectives: 1) reducing patient waiting times and 2) decreasing training costs while enhancing the skill level of the staff serving patients. The proposed model is formulated as a bi-objective nonlinear integer programming problem, which is subsequently transformed into a linear model using theoretical techniques. Given that this problem is NP-hard, two efficient meta-heuristic methods are developed to solve it for real-world scenarios of varying sizes. The findings of this study present a comparative analysis between the outcomes generated by the proposed algorithms and an exact method (CPLEX), showcasing their effectiveness and superiority. Additionally, a sensitivity analysis highlights that despite the costs associated with in-service training, enhancing staff skills can substantially reduce patient waiting times, resulting in significant cost savings for medical services while ensuring the delivery of high-quality care. Moreover, the improved staff skills enable IPD managers to gain valuable insights into the financial well-being of the medical center, including its ability to attract medical tourists.

Trends in clinical workload in UK primary care 2005-2019.

Substantial increases in UK consulting rates, mean consultation duration and clinical workload were observed between 2007 and 2014. No analysis of more recent trends in clinical workload has been published to date. This study updates and builds on previous research, identifying underlying changes in population morbidity levels affecting demand for primary health care. To describe the changes in clinical workload in UK primary care since 2005. Retrospective cohort study. Over 500 million anonymised electronic health records were obtained from IQVIA Medical Research Data to examine consulting rates with GPs and practice nurses together with the duration of these consultations to determine total patient-level workload per person-year. Age-standardised mean GP direct (face-to-face and telephone) consulting rates fell steadily by 2.0% a year from 2014 to 2019. Between 2005 and 2019 mean GP direct consulting rates fell by 5.8% overall whereas mean workload per person-year increased by 25.8%, due in part to a 36.9% increase in mean consultation duration. Indirect GP workload almost tripled over the fifteen years, contributing to a 48.3% increase in overall clinical workload per person-year. The proportion of the study population with two or more serious chronic conditions increased from 22.5% to 31.6%, accounting for almost 55.0% of total clinical workload in 2019. Findings show sustained increases in consulting rates, consultation duration and clinical workload until 2014. From 2015, however, rising demand for healthcare and a larger administrative workload have led to capacity constraints as the system nears saturation.

Capacity Demand Analysis of Rural Biogas Power Generation System with Independent Operation Considering Source-Load Uncertainty

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene’s data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness.

Flexible optimal bus‐schedule bridging for metro operation‐interruption

AbstractUnder the sudden interruption of the metro, it is significant to dispatch emergency bridging buses to evacuate stranded passengers to improve linkage management and service reliability. Aiming at the problem of emergency bridging bus scheduling, considering the remaining capacity of conventional buses, passenger tolerance, and site convenience, a flexible combined emergency bridging bus scheduling model is constructed based on the constraints of vehicle capacity, dispatching capacity, and maximum evacuation times of emergency bridging bus, aiming at minimizing the maximum evacuation time and passenger delay. The improved Harris hawk algorithm is used to solve the model, and the evacuation plan of the demand‐responsive and station‐station bridging lines is obtained. The maximum evacuation time is 41 min, and the average delay of stranded passengers is 19 min. The results show that the maximum evacuation time is 10% and 27% less than the fixed combined and single scheduling. The average delay is 16% and 42% less than the fixed combination and traditional single scheduling. The sensitivity analysis of the influencing factors of emergency bridging bus scheduling is conducted. The results show that the flexible combined emergency bridging bus scheduling model constructed in this paper can improve evacuation efficiency and reduce passenger travel delays.

Hybrid Energy-Efficient Task Offloading Algorithm (HEETA): A Framework for Optimizing Edge Computing Offloading Decisions

In the evolving realm of Mobile Edge Computing (MEC), efficient task offloading remains pivotal. This paper introduces the Hybrid Energy-Efficient Task Offloading Algorithm (HEETA) to address the deficiencies of the current Joint Optimization Task Offloading Strategy based on Particle Swarm Optimization (JOBPSO). Drawing from a broad dataset encompassing diverse MEC operational variables, HEETA exhibits exemplary performance metrics, with a notable mean fitness of approximately 0.99984 and a minimal standard deviation of 0.000116. Such metrics not only reflect HEETA's robustness but also its adaptability across multifaceted MEC parameters. Furthermore, its dynamic nature facilitates adaptability to variables including task numbers, computational capacity, and latency constraints, resulting in marked improvements in energy efficiency. Quantitative evaluations, as evidenced by a performance matrix, position HEETA's global best fitness values between approximately 0.9998 and 0.9999. While HEETA signifies a monumental step in enhancing energy efficiency, prolonging device longevity, and optimizing overall MEC system performance, the research acknowledges potential limitations, emphasizing the imperatives of accurate modeling and subsequent validations within distinct MEC environments.

The effect of capacity constraints on the slope of the Phillips curve

Abstract How do capacity constraints affect the slope of the Phillips curve? In particular, do changes in output affect prices by more when firms face more strongly diminishing returns? Intuitively, one may conjecture that the answer is yes. With more strongly diminishing returns, marginal costs increase more when production increases, also raising prices by more. But this intuition is incomplete in models with sticky prices à la Calvo. In the standard New Keynesian model with Calvo pricing frictions, more strongly diminishing returns actually flatten the Phillips curve. The reason is the reluctance of firms who alter their prices to let their prices get too far out of line with the prices set by firms that do not adjust. We explain how the Phillips curve might be amended to avoid this implausible feature, for example, by using Rotemberg pricing or introducing wage stickiness.

Multi-objective optimization of production system with staggered production

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers.

ROBUST OPTIMIZATION MODEL OF CONTAINER LINER ROUTES IN FEEDER LINE NETWORK

The universal application of the hub-and-spoke maritime network makes feeder line network key to restricting the quality and efficiency of maritime transportation. However, container liner routes in feeder line network are susceptible to the changes in shipment demand and international fuel prices. Therefore, based on the hub-and-spoke maritime network, this paper constructs a robust optimization model of container liner routes in feeder line network. Under the capacity and time constraints, routes optimization and ship equipment under uncertain environment are analysed. An improved tabu search algorithm was designed based on the characteristics of the model. The example analysis proves that the model can still ensure the robustness of routes under uncertain environment, which is more applicable than the deterministic model.

Optimization of land use to accommodate nutritional transformation of food systems: a case study from the Beijing–Tianjin–Hebei region

The transformation and reconstruction of China’s food system not only faces many risks, such as the unceasing growth of food consumption on the demand side and the structural imbalance of dietary nutrition, but also must address serious challenges, such as constraints of resources, environment, and production capacity on the supply side. The optimal allocation of land use structure is an important method to realizing a transformation of sustainable food systems, achieving the goal of nutrition security, and guiding coordinated spatial development. This study takes the Beijing–Tianjin–Hebei region as an example, analyzing the development trends of the region’s dietary nutrition structure clarifies the objectives for improving dietary nutrition. This study uses comprehensive optimization model and dynamic land system model, exploring land use optimization schemes under different nutritional goals and development scenarios. The result show that the dietary structure in the Beijing–Tianjin–Hebei region is transitioning from “food based” to “intake balance” and gradually evolved to “intake diversity,” with the main objectives being to maintain stable calorie intake while moderately increasing protein intake and reducing fat intake. Achieving this goal will gradually increase demand for cultivated land and intensify spatial competition for land use. However, by optimizing land use allocation, it is possible to free up more spatial resources to balance economic development and ecological protection and reduce land use fragmentation, thereby significantly enhancing regional economic benefits and the value of ecosystem services based on improvements in dietary nutrition.

Optimizing School Bus Routes With Ant Colony Optimization: A Dynamic Approach To Sustainable Transportation Logistics

In response to the escalating demands of modern transportation and logistics within the educational sector, this paper presents a novel exploration of Ant Colony Optimization (ACO) applied to school bus routing, a complex subset of the Vehicle Routing Problem (VRP). Traditional methods of addressing VRPs have proven insufficient in navigating the intricate requirements and dynamic nature of routing challenges, necessitating the adoption of more sophisticated heuristic and metaheuristic strategies. By harnessing ACO, inspired by the path-finding capabilities of ants via pheromone trails, this study introduces an innovative approach to dynamically optimize school bus routes. This optimization not only aims for efficiency in routing but also emphasizes environmental considerations and cost reduction. This research extends beyond the theoretical framework of ACO, incorporating practical applications and simulations that reflect real-world conditions such as fluctuating student attendance and varying traffic patterns. Through a comprehensive analysis that includes the development of a Python-based ACO model, calculation of transition probabilities, and a simulation of routing strategies, we demonstrate the algorithm's robustness and versatility. Additionally, we address critical factors such as time windows and bus capacity constraints, underscoring the model's adaptability to the multifaceted dimensions of school bus routing. The findings from this study highlight the significant advantages of applying ACO to VRPs, showcasing notable improvements in route efficiency, fuel consumption, and overall logistical execution compared to conventional routing methods. This research not only contributes to the existing body of knowledge on VRPs and ACO but also sets the stage for future explorations into dynamic routing optimization. It opens avenues for integrating advanced predictive models and real-time data analysis, promising further enhancements in transportation logistics and the potential for broad application across various sectors.

Optimizing sustainable multimodal distribution networks in the context of carbon pricing, with a case study in the Thai sugar industry

Transportation is a major cause of energy consumption and emissions which can be reduced by optimizing routings and using alternative modes of transport. This paper relates to the strategic design of multimodal transportation networks. It presents a general model of green vehicle routing problems that supports strategic decision-making by identifying optimal solutions and provides data on costs and emissions. Three general linear programming models were developed that optimize multimodal distribution networks that could be applied in many industries. Model I evaluates carbon emissions; model II assesses carbon emissions and capacity constraints; and model III establishes total costs including transportation, handling, storage, fuel and carbon costs.Thailand is the third largest world sugar exporter in the world and is piloting carbon pricing, which will affect energy intensive industries, including the sugar industry. The models are applied using data obtained from a collaborating company. The research contributed to practice by informing managerial decisions relating to the export of sugar from the factory. This included evaluating the possible use of a dry port with rail connections, which could reduce transportation and carbon costs by 54.3% and facilitate the building of another factory to increase exports.

Remote screening accuracy of first-time hearing aid users.

Capacity constraints in Danish hearing healthcare may lead to diagnostic delays and repetitive pre-treatment audiological exams for hearing-impaired patients. This study investigated the effectiveness of remote ear-nose-throat (ENT) specialist assessments (RESA) for complicated hearing loss, comparing the accuracy of private ENT specialists and medical audiologists. RESA screening accuracy was determined for four ENT specialists, individually and as subspecialised groups. These assessments were benchmarked against "gold standard" in-person ENT assessments for 445 potential adult first-time hearing aid users. Medical audiologists initially recorded lower RESA screening specificity and positive predictive values than private ENT specialists. However, after making two adjustments to the dataset, these differences were neutralised. Screening sensitivity was consistent across individual and grouped subspecialities. RESA screening is a promising tool for timely diagnosis and treatment. The findings reveal that both private ENT specialists and medical audiologists may conduct RESA with high consistency and uniformity. This research was funded by the Danish Health Data Authority and the North Denmark Region, but does not reflect their views. The study received no commercial support. Not relevant.

Free-for-all: Does crowding impact outcomes because hospital emergency departments do not prioritise effectively?

Unexpected peaks in volumes of attendances at hospital emergency departments (EDs) have been found to affect waiting times, intensity of care and outcomes. We ask whether these effects of ED crowding on patients are caused by poor clinical prioritisation or a quality-quantity trade-off generated by a binding capacity constraint. We study the effects of crowding created by lower-severity patients on the outcomes of approximately 13 million higher-severity patients attending the 140 public EDs in England between April 2016 and March 2017. Our identification approach relies on high-dimensional fixed effects to account for planned capacity. Unexpected demand from low-severity patients has very limited effects on the care provided to higher-severity patients throughout their entire pathway in ED. Detrimental effects of crowding caused by low-severity patients materialise only at very high levels of unexpected demand, suggesting that binding resource constraints impact patient care only when demand greatly exceeds the ED's expectations. These effects are smaller than those caused by crowding induced by higher-severity patients, suggesting an efficient prioritisation of incoming patients in EDs.

A Mixed Integer Linear Programming for Exam-Invigilator Assignment Problem: A Case Study at Universiti Pertahanan Nasional Malaysia

The assignment of invigilators for examinations is a complex and challenging task, particularly when faced with numerous factors that must be carefully considered. Critical elements are essential in this process, including staff availability, room capacity, and time constraints, requiring thorough evaluation and coordination. This paper focuses on improving the allocation of invigilators for examinations at Universiti Pertahanan Nasional Malaysia (UPNM). The issue arises when academic staff members responsible for teaching the subject are also assigned as exam invigilators, which conflicts with their primary role of assisting students in addressing their queries during examinations. It is essential to reconsider the distribution of invigilator roles, ensuring that academic staff members can focus solely on providing educational support. In contrast, qualified non-academic staff handle invigilation duties effectively. A mixed-integer linear programming (MILP) model is formulated using the existing examination timetable to solve this problem. The model is solved using a simple algorithm implemented in the XPress MP programming language, resulting in an improved solution that requires less computational effort than the conventional method. This approach offers an alternative and better solution for scheduling examination invigilators at UPNM, ensuring the efficient and effective management of exam procedures while maximizing the utilization of available resources. It can serve as a starting point for future investigations into UPNM's scheduling procedures.

Design of a mathematical model for staff planning in an emergency department

The emergency department plays a fundamental role in hospitals and critically affects a hospital's overall efficiency. Inadequate staff planning in emergency departments generates high costs, overcrowding, and patient dissatisfaction due to long waiting times, possibly putting the patient's health and life at risk. This research designed two mixed integer linear programming mathematical models. The first determined the optimal number of physicians required per shift and weekday in an adult emergency department to minimize the deviation between available and required capacity. The results of the optimization model would reduce by 16.07 % the required medical office staff per week, from 56 physicians in the current situation to 47, reducing staffing costs without impacting waiting times. Moreover, the overall physician utilization would be 95.01 % compared to 77.79 % in the current situation, indicating an adequate distribution of physicians on each shift of each day according to patient demand. These results contribute to the problem of high medical staff costs and overcrowding without sacrificing timeliness and quality of care. In contrast, the second model that minimized the number of physicians considering capacity constraints would increase the staff by 7.14 % concerning the current situation. This research was based on a model presented in the literature, but the objective function included the deviation variables as unrestricted in sign and a constraint to ensure that they were positive. The first model designed is presented as a tool to support emergency department managers in the medium-term planning of medical staff, ensuring an optimal solution to this problem

Heterogeneous beliefs with information processing capacity constraints and asset pricing in a monetary economy

This paper proposes a monetary model to explore the influences of heterogeneous beliefs and information processing capacity constraints on the dynamics of asset prices. The capacity constraints not only influence the estimations of the capacity constrained investor, but also generate persistent disagreements among the investors. The model implies that reducing the levels of capacity constraints can alleviate the influences of heterogeneous beliefs and helps to stabilize financial markets. The model also reveals that introducing heterogeneous beliefs about both real and nominal sectors not only leads the stock with low monetary policy exposure to have significantly higher average return than the stock with high monetary policy exposure, but also can explain the mixed results about the relationship between the volatility and the risk premium of the aggregate stock market.

Solving a Multimodal Routing Problem with Pickup and Delivery Time Windows under LR Triangular Fuzzy Capacity Constraints

This study models a container routing problem using multimodal transportation to improve its economy, timeliness, and reliability. Pickup and delivery time windows are simultaneously formulated in optimization to provide the shipper and the receiver with time-efficient services, in which early pickup and delayed delivery can be avoided, and nonlinear storage periods at the origin and the destination can be minimized. Furthermore, the capacity uncertainty of the multimodal network is incorporated into the advanced routing to enhance its reliability in practical transportation. The LR triangular fuzzy number is adopted to model the capacity uncertainty, in which its spread ratio is defined to measure the uncertainty level of the fuzzy capacity. Due to the nonlinearity introduced by the time windows and the fuzziness from the network capacity, this study establishes a fuzzy nonlinear optimization model for optimization problem. A chance-constrained linear reformulation equivalent to the proposed model is then generated based on the credibility measure, which makes the global optimum solution attainable by using Lingo software. A numerical case verification demonstrates that the proposed model can effectively solve the problem. The case analysis points out that the formulation of pickup and delivery time windows can improve the timeliness of the entire transportation process and help to achieve on-time transportation. Furthermore, improving the confidence level and the uncertainty level increases the total costs of the optimal route. Therefore, the shipper and the receiver must prepare more transportation budget to improve reliability and address the increasing uncertainty level. Further analysis draws some insights to help the shipper, receiver, and multimodal transport operator to organize a reliable and cost-efficient multimodal transportation under capacity uncertainty through confidence level balance and transportation service and transfer service selection.

New algorithms for fair k-center problem with outliers and capacity constraints

The fair k-center problem has been paid lots of attention recently. In the fair k-center problem, we are given a set X of points in a metric space and a parameter k∈Z+, where the points in X are divided into several groups, and each point is assigned a color to denote which group it is in. The goal is to partition X into k clusters such that the number of cluster centers with each color is equal to a given value, and the k-center problem objective is minimized. In this paper, we consider the fair k-center problem with outliers and capacity constraints, denoted as the fair k-center with outliers (FkCO) problem and the capacitated fair k-center (CFkC) problem, respectively. The outliers constraints allow up to z outliers to be discarded when computing the objective function, while the capacity constraints require that each cluster has size no more than L. In this paper, we design an Fixed-Parameter Tractability (FPT) approximation algorithm and a polynomial approximation algorithm for the above two problems. In particular, our algorithms give (1+ϵ)-approximations with FPT time for the FkCO and CFkC problems in doubling metric space. Moreover, we also propose a 3-approximation algorithm in polynomial time for the FkCO problem with some reasonable assumptions.

Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

Dynamic Assignment Algorithms to Boost Refugee Outcomes Amid record-breaking forced displacement in recent years, researchers and policymakers alike have become increasingly interested in the idea of algorithmically matching refugees to geographic localities in order to optimize their employment or other integration outcomes. In “Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing,” Bansak and Paulson propose new dynamic assignment algorithms for this context designed to maximize a given outcome while meeting the operational needs of refugee resettlement and asylum agencies. Using resettlement data from both the United States and Switzerland, they demonstrate how one algorithm (currently being piloted in Switzerland) can achieve near-optimal results compared with a hindsight-optimal matching. They also show that, because of nonstationarities in the arrival process, outcome maximization (even when subject to location capacity constraints) can result in an imbalanced allocation to localities over time, putting periodic strains on limited local resettlement resources. They account for this problem in a second algorithm that achieves near-perfect balance over time with only a small loss in average outcomes compared with the first algorithm.

Development of a three-stage hierarchical model for quick calculating stair evacuation time of high-rise building coupled with simulation analysis

Calculating the evacuation time during staircase evacuation is essential to evaluate the security conditions of high-rise buildings under emergency scenarios. A three-stage hierarchical model for calculating emergency evacuation time is constructed by combining discrete state update rules and continuous forms in computation to investigate the evacuation process of pedestrians in multi-floor buildings in emergency circumstances. Combined with simulation analysis, the model considers the capacity constraint of stairwell and congestion effects and calculates the evacuation time hierarchically. The model's validity is verified by the existing evacuation simulator for a hypothetical building case study, coupled with a comprehensive computational comparison. Calculations on entire and separate evacuation times are explored considering different stairway capacity restrictions and geometric features. Computational results demonstrated that this model is effective and may be regarded as a valid tool for staircase evacuation time calculation in the context of high-rise buildings.