Capacity Planning Research Articles

Green economic planning for rapid decarbonisation

ABSTRACT Sustainable futures require deep social and economic transformations to address climate change adequately. The current landscape of intergovernmental and market-based coordination is not delivering this outcome. In response, political economic scholarship is congregating around the concept of the green state as a corrective to the status quo. In spite of this resurgence of interest in the green state, much research takes place in issue-specific silos without exploring synergies between them. Our contribution is to call for an integrative agenda focused on ‘green economic planning’, a form of state-led decarbonisation whereby the state designs and implements structural complementarities between macro-financial architectures, industrial policy, and private sector incentives. Our evidence for this approach is taken from historical cases of indicative planning in post-war democracies, contemporary cases of sectoral planning by states, and finally, planning by multinational corporations. We draw not only on political economy but also on scholarship in the fields of business, environment, energy and economic history. The upshot is a new research agenda focusing on state planning capacity in hierarchical coordination institutions and multinational corporations as research laboratories for the study of the organisational and technological infrastructure needed for state planning.

3D City Modeling for Flood Inundation Analysis in Mayjen Sungkono Area, Surabaya

Abstract Floods in urban areas are primarily caused by high rainfall combined with inadequate drainage systems, inappropriate land use changes, and insufficient flood mitigation measures. Surabaya, characterized as a seasonal zone with periodic rainfall, frequently experiences flood inundation due to the loss of water retention areas and an inadequate drainage infrastructure. Three-dimensional (3D) city models provide a detailed representation of urban environments, capturing the three-dimensional geometry of objects and general urban structures. This 3D modeling approach can enhance the understanding of flood depths impacting surrounding buildings and infrastructure and assist in estimating the effects on drainage capacity and catchment areas for long-term planning. In this research, 3D flood inundation modeling was conducted using LiDAR data and aerial photography, integrated with other GIS data. The flood inundation simulation was performed using HEC-RAS, while the 3D modeling was carried out using GIS-based software employing semi-automatic methods. The results of the 3D modeling and visualization indicate potential flood inundation depths reaching up to 5 meters, affecting 1,100 buildings and 30 roads. Further research could focus on improving the accuracy of terrain and channel data used in flood modeling to enhance the precision of the results.

Flexible and adjustable resource backup capacity planning method for port areas with a high percentage of new energy access

In view of the problem of insufficient flexibility in grid regulation caused by the large-scale integration of new energy sources, this paper proposes an interactive regulation method for flexible resources to participate in wind and solar energy consumption and establishes a two-layer optimization model of planning and operation. The planning layer considers the planned capacity of flexible adjustable resources, and the operation layer comprehensively considers economic costs. Flexible resources are coordinated and regulated to achieve multi-objective optimization using a hybrid swarm migration algorithm based on indicators such as the operating costs of each standby unit and the cost of standby resources. The optimization results are then fed back to the planning layer to obtain the optimal regulation scheme. Meanwhile, this paper expands the source of backup resources to include the source-load-storage. It proposes a strategy for coordinating the participation of diversified resources from the source-load-storage in the backup capacity planning. Finally, a simulation analysis is carried out based on the actual power grid of a port. The results show that the reasonable allocation of diversified flexible resources can improve the economy of the power grid, the wind and solar energy consumption capacity, the flexibility of regulation, and the reliable operation capacity, which verifies the rationality and effectiveness of the proposed method.

Analisis Kapasitas Produksi Menggunakan Metode Rough Cut Capacity Planning Pada CV Tahaki Multi Kreasi

CV Tahaki Multi Kreasi is a company engaged in interior, exterior and advertising. The number of requests for cabinet room products tends to fluctuate every month. In 2023, from January to December, CV Tahaki Multi Kreasi received 143 cabinet room order projects. To estimate the amount of demand, effective forecasting is needed. Forecasting uses methods namely Moving Average, Exponential Smoothing, and Trend Analysis. The most effective forecasting uses the Trend Analysis method which has the smallest forecasting error. The MAD value is 1.9005, MSE is 4.5343, and MAPE is 19.2362. The production schedule at CV Tahaki Multi Kreasi is carried out every two weeks for optimal results. Production capacity requirements are calculated using the Rough Cut Capacity Planning (RCCP) method. The RCCP method is used to determine the possibility of insufficient capacity or overload in determining production capacity.. The total capacity required in all work centers is 962 hours, while the total available capacity is 2016 hours. The analysis shows that there is no capacity shortage at each work station because the variance shows negative and the load percentage is 54.2%.

Capacity planning for wind, solar, thermal and energy storage in power generation systems considering coupled electricity‐carbon markets

Capacity planning for wind, solar, thermal and energy storage in power generation systems considering coupled electricity‐carbon markets

Study on two-stage robust optimal configuration of integrated energy system considering CCUS and electric hydrogen production

Under the background of complementary and cascade utilization of multiple types of energy, how to carry out the capacity planning of economic, efficient, and low-carbon integrated energy system to meet the diversified needs of cooling, heating, electricity, and hydrogen loads is a challenging problem. This study conducted a two-stage robust optimization (RO) configuration of integrated energy system considering Carbon Capture Utilization and Storage (CCUS) and electric hydrogen production. In order to adapt the configuration scheme to the potential extreme operational scenarios, the uncertainty sets of new energy and load are constructed based on Latin hypercubic sampling. The efficacy is validated through the design of several scenarios and a comparison with alternative methods. The results indicate: 1) Strengthening of carbon emission constraints facilitates the cleaner and electrified transformation of energy. In the shift from “low carbon” to “zero carbon” scenario, the proportion of new energy capacity in power generation equipment increases from 45.1% to 79.8%; the proportion of electric chillers in cooling equipment increases from 1.3% to 7.2%, and reaches 60% under “zero carbon” scenario. 2) With a 36% reduction in the investment cost of new energy, the spatiotemporal transfer value of energy is realized, which increases the investment capacity of energy storage by about 7 times. 3) The configuration of hydrogen production and storage equipment to meet hydrogen demand reduces costs by 19% compared to direct purchasing in “low carbon” scenario. 4) Compared to other methods, the proposed method in this study is able to reduce the solution time and configuration cost.

Advancing Data Center Operations Through AI and Machine Learning : A Comprehensive Analysis of Predictive Maintenance and Resource Optimization

This article systematically analyzes the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on modern data center operations. Through an extensive review of implemented cases and empirical data from multiple data centers, the article demonstrates how AI-driven solutions significantly enhance operational efficiency, reduce maintenance costs, and improve infrastructure reliability. The findings indicate that predictive maintenance algorithms achieve a 47% reduction in unexpected equipment failures, while ML-based resource optimization leads to a 31% improvement in resource utilization rates. The article examines integrating deep learning models for real-time energy management, resulting in an average 23% reduction in cooling costs and a 0.15 improvement in Power Usage Effectiveness (PUE). Additionally, the article analyzes the implementation of AI-powered security frameworks, which demonstrated a 92% accuracy rate in anomaly detection and reduced false positives by 76% compared to traditional rule-based systems. The article also presents a novel framework for capacity planning using neural networks, achieving an 89% accuracy in demand forecasting over 12 months. These findings provide valuable insights for data center operators and establish best practices for implementing AI/ML solutions in mission-critical infrastructure environments. The article concludes with recommendations for overcoming integration challenges and a roadmap for future technological adoption.

Data-based optimisation of intra-hospital patient transport capacity planning

Data-based optimisation of intra-hospital patient transport capacity planning

A Combined Capacity Planning and Simulation Approach for the Optimization of AGV Systems in Complex Production Logistics Environments

Background: The capacity planning of production systems is one of the most fundamental strategic problems in the creation of a production plant. However, the implementation of increasingly complex production systems combined with sophisticated automated material handling justifies the development of novel approaches to solve the combined capacity planning and material handling problem, which is also the objective of the current study. Methods: The presented approach combines the use of capacity planning formulas and discrete event simulation for optimizing extensive automated guided vehicle (AGV) systems from the aspect of the number of required vehicles. Extensive series of simulation experiments are applied in the case of each model variant for optimal results and to account for machine failures in the system. Results: The application of the proposed method is demonstrated through a realistic sample problem in a plastic industry setting with the use of the Siemens Tecnomatix Plant Simulation software (version 2302.0003, Educational license). Conclusions: The results from the sample problem demonstrate the usefulness of the approach, as a non-intuitive solution proved to be the most efficient. Additionally, the main advantage of the method is that it provides a standardized framework for the simulation-based optimization of AGV systems starting out from the comprehensive production capacity parameters.

A Network Calculus Model for SFC Realization and Traffic Bounds Estimation in Data Centers

Network Function Virtualization (NFV) is a promising technology that can transform how internet service providers deliver their services. However, recent studies have identified several challenges in adopting NFV. Two key challenges are central to the operation and capacity planning of NFV Data Centers (DCs): (i) Service Function Chain (SFC) realization —determining if a new request with a known profile can be accommodated—and (ii) Network Function Virtualization (NFV) traffic bounds estimation —estimating the total traffic that a data center can handle considering all service requests and their performance constraints. To address these challenges, we propose a model that leverages stochastic network calculus to effectively dimension an NFV DC while ensuring delay and availability bounds for all service requests. Our theoretical model provides a mathematical framework to assess the realization of a single SFC request without delving into the specifics of the realization process. We utilize established availability-aware Virtual Network Function (VNF) placement patterns to obtain traffic bounds essential to planning data center capacity. We analyze NFV data center traffic under various scenarios over a Fat-tree DC topology. The results demonstrate that data center capacity is significantly influenced by the VNF placement strategy. Additionally, for data centers hosting latency-sensitive services, Service Level Objective (SLO) constraints on availability and delay are crucial in determining the number of such requests that can be accommodated.

Capacity Planning in Paediatric Palliative and Hospice Care: An Italian Case

Although the growing demand for healthcare services has enhanced the occurrence of inefficiencies in care provision, healthcare systems should ensure safe, timely and equitable access to care. In particular, the long waiting lists, the lack of beds, and the shortage of healthcare workforce have highlighted the necessity to effectively manage and strategically plan the available resources. Capacity Planning (CP) contributes to determine what, when, where and how much product or service to deliver, with the goal of analysing and optimizing the use and allocation of human and material resources (e.g., physical spaces, equipment, beds, workforce) based on the demand. CP demonstrates to be extremely relevant for hospital units to schedule appointments, diagnostics, surgeries and admissions. Complex contexts and broader healthcare networks could benefit even more from this approach, helping to coordinate multiple care providers and aligning the available resources with the demand pace. The current research presents the implementation of CP in the context of Paediatric Palliative and Hospice Care (PPHC), delivered in an Italian facility. PPHC provides long-term, interdisciplinary and coordinated care between territorial, ambulatory and hospice services. It encompasses follow-up activities, parental training, end-of-life and respite care. In the Italian case analysed, the shortage of beds affects the quality of care delivered to these patients, in terms of accessibility to the service, effectiveness and timeliness of care. Hence, a CP analysis has been performed to pursue a twofold objective: a) to understand if the current capacity of the facility meets the demand for care; b) to improve productivity through an optimized allocation of resources, particularly the available beds and the healthcare workforce. The expected results involve a more effective use of resources, an increased bed occupancy rate and a reduction in rescheduled admissions due to inadequate planning. Optimizing capacity in this specific setting can serve as a pilot project for other similar contexts, aiming to improve patients’ access to healthcare services, their quality of life and supporting the workforce involved in PPHC. This research offers new insights from both the academic and managerial point of view, providing evidence about the implementation of CP in complex healthcare settings.

Global, Regional, and Country-Level Economic Impacts of Oral Conditions in 2019.

The recent World Health Organization (WHO) Oral Health Resolution and the subsequent WHO Global Oral Health Action Plan highlight the key relevance of providing information on the economic impacts of oral conditions. The purpose of this study was to provide updated estimates for the global, regional, and country-level economic impacts of oral conditions in 2019. Extending previously established methods, dental expenditures (costs for treatments) and productivity losses for 5 oral conditions (caries in deciduous and permanent teeth, periodontitis, edentulism, other oral diseases) were estimated for the year 2019. The estimated total worldwide economic impacts of oral conditions in 2019 were US $710B, of which US $387B (US $327B to US $404B) was due to direct costs and US $323B (US $186 to US $460) was due to productivity losses for the 5 main oral conditions. Low-income countries spent an average of US $0.52 (US $0.22 to US $0.96) per capita on dental care, while high-income countries spent an average of US $260 (US $257 to US $268) per capita-a 500-fold difference. These findings suggest that oral conditions continue to substantiate an enormous economic burden to individuals and society. The comprehensiveness of estimates supersedes that of previous work as the primary information on direct costs was identified for a larger number of countries. The need for more and better routine reporting and monitoring of the economic impact of oral conditions is emphasized. The relevance of such information is also highlighted by its inclusion in the first-ever WHO Global Oral Health Status Report and Global Strategy on Oral health 2023 to 2030. Given the persistently high economic burden of oral conditions, there is a key role for better prioritization of cost-efficient oral health programs as well as needs-responsive capacity planning.

Advanced obstacle recognition and trajectory prediction for autonomous vehicles using 3D CNN and GRU

Abstract. Recent years have seen a rapid advancement in new energy vehicle technology, which has increased consumer demand for autonomous driving systems. However, current self-driving cars still face many technical barriers. Obstacle detection and obstacle trajectory prediction in dynamic environments is one of the main difficulties of the technique. To improve self-driving cars' capacity for route planning and decision-making in dynamic contexts, this thesis proposes an obstacle identification and trajectory prediction model that combines a gated recurrent unit (GRU) network with a three-dimensional convolutional neural network (3D CNN). The integrated framework utilises the feature recognition capability of 3D CNN and the prediction capability of GRU to accurately predict dynamic obstacles, thereby improving the performance of the model in various types of environments (e.g., visual SLAM). Experimental results based on publicly available datasets (e.g., KITTI and Argoverse) show that the proposed method has significant advantages in dynamic obstacle identification and prediction as well as overall model operation efficiency. The findings show that the integrated framework is useful and successful in advancing the creation of self-driving automobiles.

How to ensure an appropriate oral health workforce? Modelling future scenarios for the Netherlands

BackgroundCurrent methods for oral health workforce planning lack responsiveness to dynamic needs, hampering efficiency, equity and sustainability. Effective workforce planning is vital for resilient health care systems and achieving universal health coverage. Given this context, we developed and operationalised a needs-adaptive oral health workforce planning model and explored the potential of various future scenarios.MethodsUsing publicly available data, including the Special Eurobarometer 330 Oral Health Survey, we applied the model in a hypothetical context focusing on the Dutch population’s dental needs from 2022 to 2050. We compared current and future provider supply and requirement and examined, in addition to a base case scenario, several alternative scenarios. These included epidemiological transition scenarios with different oral health morbidity trajectories, skill-mix scenarios with independent oral hygienists conducting check-ups and multiple dental student intake and training duration (5 instead of 6 years) scenarios.ResultsBased on the aforementioned historical data, our model projects that provider requirement will exceed supply for the planning period. If the percentage of people having all natural teeth increases by 10% or 20% in 2032, 34 or 68 additional full-time equivalent (FTE) dentists will be required, respectively, compared to the base case scenario. In the skill-mix scenario, the model indicates that prioritising oral hygienists for check-ups and shifting dentists’ focus to primarily complex care could address population needs more efficiently. Among the student intake and training duration scenarios, increasing intake to 375 and, to a lesser extent, reducing training to 5 years is projected to most effectively close the provider gap.ConclusionsThe study underscores the importance of understanding oral health morbidity trajectories for effective capacity planning. Due to limited dental epidemiological data, projections carry substantial uncertainty. Currently, demand for FTE dentists seems to exceed supply, though this may vary with epidemiological changes. Skill-mix strategies could offer efficiency gains by redistributing tasks, while adjustments in dental intake and training duration could also help address the requirement-supply gap. Resolving dentistry workforce challenges requires a multifaceted approach, including strengthening oral epidemiology projections, addressing the root causes of dental health issues and prioritising harmonious dental public health and general practice prevention measures.

Framework for Analysis of Queueing Systems with Correlated Arrival Processes and Simultaneous Service of a Restricted Number of Customers in Scenarios with an Infinite Buffer and Retrials

In this paper, we create a framework for the uniform algorithmic analysis of queueing systems with the Markov arrival process and the simultaneous service of a restricted number of customers, described by a multidimensional Markov chain. This chain behaves as the finite-state quasi-death process between successive service-beginning epochs, with jumps occurring at these epochs. Such a description of the service process generalizes many known mechanisms of restricted resource sharing and is well suited for describing various future mechanisms. Scenarios involving customers who cannot enter service upon arrival, access via waiting in an infinite buffer, and access via retrials are considered. We compare the generators of the multidimensional Markov chains describing the operation of queueing systems with a buffer and with retrials and show that the sufficient conditions for the ergodicity of these systems coincide. The computation of the stationary distributions of these chains is briefly discussed. The results can be used for performance evaluation and capacity planning of various queueing models with the Markov arrival process and a variety of different service mechanisms that provide simultaneous service to many customers.

KAJIAN RENCANA TATA RUANG WILAYAH BERDASARKAN RISIKO BENCANA GEMPA BUMI DI KOTA JAYAPURA

A study has been conducted on the typology of earthquake disaster-prone areas in the city of Jayapura and the suitability of Regional Spatial Planning (RTRW) for land use capacity in the city of Jayapura. The study onearthquake disaster-prone area typology aims to determine the distribution of stability levels of the area againstthe risk of earthquakes. This study employs a weighting matrix method for area stability based on the Minister ofPublic Works Regulation No. 21 of 2007 concerning Guidelines for Spatial Planning of Volcanic Eruption andEarthquake-Prone Areas (Department of Public Works, 2007). The research results indicate that the final score forthe city of Jayapura is between 30 - 45, which falls into all stability categories: stable and less stable. Thepercentage of area for each category is 97.81% for Stable Type A, 1.91% for Stable Type B, and 0.28% for LessStable Type C. The typology of earthquake disaster-prone areas in Jayapura based on stability scores falls intocategories A, B, and C. The study on the suitability of the Regional Spatial Planning (RTRW) for 2013-2033 with the land usecapacity in Jayapura was assessed by scoring data on land capacity and land use, followed by an overlay analysis.This resulted in a land capacity suitable for development of 57,039.39 hectares, or about 60.48% of the study area,while approximately 37,272.96 hectares, or around 39.52%, were deemed unsuitable.

A source and a destination country for migrant healthcare workers: the case of Ireland

Abstract Background Ireland has a long tradition of emigration, particularly to countries such as the United Kingdom and the USA. Since 2000, Ireland has become increasingly reliant upon migrant healthcare workers. At present, 43% of nurses and 45% of physicians registered in Ireland, trained internationally. Methods This study draws on literature and available data (migration and registration data) to illustrate recent patterns of healthcare worker migration into and out of Ireland. Results The data show that Ireland depends heavily on migrant healthcare workers to staff its health system. In 2021-22, internationally trained healthcare workers comprised 71% of new entrants to the medical register and 69% of new entrants to the nursing register. In terms of outward migration, the data also indicate a consistent pattern of outward migration of physicians to countries such as Australia, the United Kingdom, Canada, and New Zealand. Conclusions A heavy (and growing) reliance on migrant healthcare workers to staff the Irish health system implies a failure to train or retain sufficient healthcare workers locally and indicates weak healthcare workforce planning capacity. It also highlights a disconnect between Irish healthcare workforce planning and the WHO global code on the international recruitment of health personnel. This study will consider some of the risks associated with high rates of inward and outward healthcare worker migration in the Irish context.

Enhancing hosting capacity for electric vehicles in modern power networks using improved hybrid optimization approaches with environmental sustainability considerations

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model’s effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.

Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm

The study addresses the integration of hybrid hydrogen (H2) and battery (BT) energy storage systems into a renewable energy microgrid comprising solar photovoltaic (PV) and wind turbine (WT) systems. The research problem focuses on improving the effectiveness and computational efficiency of energy management systems (EMS) while ensuring high system reliability. Despite the existing optimization methods for hybrid microgrids, challenges remain in optimizing energy storage and capacity planning in grid-connected microgrids. To solve this, we propose the use of the Levy Flight Algorithm (LFA) to optimize the capacities of PV, WT, H2 tanks, electrolyzers (EL), fuel cells (FC), and BT, which presents a complex nonlinear optimization challenge. The novelty of this study lies in integrating the LFA with a rule-based EMS, enhancing system reliability and efficiency. The proposed approach significantly reduces the annualized system cost (ASC) and the levelized cost of energy (LCOE). The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. The LFA achieves the lowest LCOE at $0.275/kWh, compared to $0.278/kWh with SSA, $0.289/kWh with GA, $0.280/kWh with PSO and $0.283/kWh with GWO. This research contributes to the broader scientific community by providing a more efficient approach to optimizing renewable energy microgrids with hybrid storage systems, thus promoting eco-friendly and cost-effective energy solutions. The proposed system design offers a pathway to future energy systems with high renewable integration, especially as technology advances and costs continue to decrease.

Code and Data Repository for On the Value of Risk-Averse Multistage Stochastic Programming in Capacity Planning

This repository aims to demonstrate the value of risk-averse multistage stochastic programming models in capacity planning problems.