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Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation. [ABSTRACT FROM AUTHOR]

To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO. [ABSTRACT FROM AUTHOR]

In the present paper, we develop a non-homogeneous semi-Markov reward model, deriving expressions for a healthcare system's expected structure along with the expected costs generated by medical services and patients' holding times in the states. We provide a novel definition and investigation for states' availability, which is critical for capacity planning based on service demand in an environment of limited resources. The study is based on patients' mobility through hospital care, where each patient spends an amount of time in every state of the hospital (emergency room, short-term acute care, hospitalization, surgery room, and intensive care unit). Multiple outcomes, such as discharge or death, can also be taken into account. We envisage a situation where any discharges are immediately replaced by a number of new admissions that carry on the pathways of the patients who exit. By assuming an expanding system, the new idea of states' inflows is considered due to new patients who create pathways through hospital care, along with internal entrances. The theoretical results are illustrated numerically with simulated hospital data informed by aggregated public data of the Greek public health sector. The framework can be used for both strategic planning and cost evaluation purposes for hospital resources. [ABSTRACT FROM AUTHOR]

The quest for sustainable urban development requires innovative planning approaches more apt to cater for transformative action. Based on a case study approach, this study aims to explore the potential for local planning to develop practical approaches able to accelerate the transition towards sustainable urban development. To guide spatial development towards sustainability, the analysis of the results shows that local planners need to envision a process design as to advance the understanding of how a site or area can be developed. The act of process design involves pending between processes of contextualization (i.e. exploring five distinguishable domains of planning inquiry) and concretization (i.e. deciding upon a course of action by executing activities that form a process trajectory). Regarding the potential of local planning to develop transformative approaches, it is concluded that: (i) local planners possess capabilities that allow them to probe the future of places and produce insights about prospective change, (ii) the transformative capacity of local planning practices is bound to the development of the identified domains of planning inquiry and (iii) forthcoming evaluations organized around the act of process design can reveal insights regarding the possibilities to put the notion of sustainability into practice at the local level. [ABSTRACT FROM AUTHOR]

Fossil fuel combustion to produce power is responsible for a substantial amount of greenhouse gases and is essential to climate change and global warming. Therefore, the leading industrial countries are urged to invest in renewable energy and reduce reliance on traditional fossil fuel supplies. Photovoltaic (PV) solar systems are a promising renewable energy technology that has developed and expanded rapidly in recent years. However, capacity planning for the PV system is a strategic decision-making process that lasts about 20 years and is affected by climate and economic changes during this period. For this purpose, this research proposes a robust capacity planning approach by formulating a multiobjective mixed-integer linear programming (MO-MILP) optimization model. Climate and economic changes such as global irradiation, ambient temperature, and inflation rate are represented by a set of possible scenarios with a probability of occurrence. MO optimization helps study the trade-offs among economic, technical, and environmental objectives. A real-world case of capacitating a grid-connected PV system to supply power to the residential area at King Fahd University of Petroleum and Minerals is provided to demonstrate the practicability of the optimization model. In addition, a sensitivity analysis is conducted to get more technical and managerial insights into the planning under more scenarios. As a result, it is recommended to use 1252 PV arrays at an annual cost of M$ 1.18 and M$ 0.127 per year to satisfy the shortage and reduce CO2 emissions by 96.31%. [ABSTRACT FROM AUTHOR]

In recent years, environmental issues have gradually become one of the most concerning issues in the world. New energy vehicles have attracted wide attention by their good environmental and social benefits such as zero exhaust emissions and low noise pollution. This paper proposes a regional charging demand forecasting method for electric vehicles (EVs) based on hierarchical charging decision model to solve the problem of charging pile capacity planning, which affects the charging decision behavior of EVs users. The Monte Carlo sampling model is adopted to obtain the time distribution of electric vehicle arriving at each functional area, the distribution of battery state of charge when arriving, and the distribution of average daily mileage. The negative exponential distribution model is used to obtain the average mileage distribution. The fuzzy system theory is proposed to calculate the charging probability, and the fuzzy rule base is adjusted according to the actual data to ensure that the fuzzy rule base is real and effective. Finally, the charging demand distribution curve of electric vehicles in each functional area is obtained by sampling simulation. Through experimental analysis, optimizing the capacity configuration of charging facilities in the station can well describe how electric vehicle users make charging decisions in the actual situation. The research on electric vehicle charging stations can slow down the waiting time of electric vehicle charging, and then affect the process of replacing fuel vehicles with electric vehicles to achieve the development goal of popularizing electric vehicles. It is of great significance for the promotion of new energy vehicles to rationally plan the location and capacity of electric vehicle charging stations. [ABSTRACT FROM AUTHOR]

This article presents the findings of an interview and digital methods-based study of young Australian hospitality workers who lost work in the wake of the pandemic. In so doing it focuses on the relationship between one's present-day material conditions and their capacity to form plans for the future, and the role of planning in young people's future thinking. The article argues that the impact of the pandemic on the participants' plans can be best understood not through analysis of their orientations to planning, but through consideration of the degree to which their present-day circumstances brought them further away from the desired futures that they had been planning. Indeed, some of the participants who found themselves in states of deprivation identified that their plans remained unchanged, while others who were little affected in an immediate, material sense experienced it as a crisis or as a catalyst for re-evaluation. By identifying the relationship between present-day conditions and future plans for our participants, we ultimately contend that understanding planning as a form of practice through which one's present is connected to their desired future allows for insight into how young people prioritise and address goals in various spheres of life. [ABSTRACT FROM AUTHOR]

Capacity planning should be performed to balance investment costs and benefits of investing to meet the current and future demand in intensive care units. Having a high capacity to increase patient admission will lead to unutilized capacity in some periods, thereby increasing costs. On the other hand, patient admission requests from inborn and transported patients might be rejected due to lack of equipment. It should be considered in terms of cost-effectiveness and patient health; therefore, optimal equipment capacity must be determined. In this study, the optimal capacity planning problem has been considered for the neonatal intensive care unit of a hospital adopting the simulation-optimization approach. A discrete event simulation model is proposed for a neonatal intensive care unit in Adana, Turkey. Then, the optimization model identified the optimal numbers of incubators, ventilators, and nitric oxide devices to maximize equipment efficiency and minimize total inborn patient rejection and transport ratios. Three different resource allocations are presented, and the best is obtained from these three objectives as 72 incubators, 35 ventilators, and three nitric oxide devices. The application results obtained have revealed that the rejection and transport rate, which is found to be 1.12% in the current situation, can be reduced to 0.2% with different numbers of equipment and that equipment efficiency can be achieved with optimal quantities of each equipment. The results of the study can help the decision-makers when minimum transport and rejection ratios are critical which almost all intensive care units are required. Furthermore, the proposed simulation-optimization model can be adapted to different neonatal intensive care units having the same characteristics. [ABSTRACT FROM AUTHOR]

This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual delivery data from HepsiJet, a leading e-commerce logistics provider in Turkey. We first employ several machine learning algorithms and simulations to investigate the capacity of the courier. Subsequently, a dynamic route planning workflow is executed with a highly specialized and novel routing algorithm. Our proposed heuristic approach considers combined fleet operations for delivering regular packages originating from a central depot and dynamic e-grocery orders picked up at local supermarkets and delivered to the customers. The heuristic algorithm constitutes k-opt and node transfer operation variations customized for this integrated problem. We report the performance of our approach in problem instances from the literature and instances from HepsiJet's daily operations, which we also publicly share as new route optimization problem instances. Our results suggest that, despite the more complex nature of the integrated problem, our proposed algorithm and solution framework produce more efficient and cost-effective solutions that offer additional business opportunities for companies such as HepsiJet. The computational analyses reveal that implementing our proposed approach yields significant efficiency gains and cost reductions for the company, with a distance reduction of over 30%, underscoring our approach's effectiveness in achieving substantial cost savings and enhanced efficiency through integrating two distinct delivery operations. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative assessment of the value of hydroelectric power plants (HPPs) in power systems with a significant penetration of variable renewable energy sources (VRESs). Through a capacity expansion planning (CEP) model that incorporates a detailed representation of HPP operating principles, the study investigates the construction and application of HPP rule curves essential for seasonal operation. A comparative analysis is also conducted between the proposed rule curve formulation and alternative modeling techniques from the literature. The CEP model optimizes installed capacities per technology to achieve predefined VRES penetration targets, considering hourly granularity and separate rule curves for each HPP. A case study involving twelve reservoir hydropower stations and two open-loop pumped hydro stations is examined, accounting for standalone plants and cascaded hydro systems across six river basins. The study evaluates the additional generation and storage required to replace the hydropower fleet under high VRES penetration levels, assessing the resulting increases in total system cost emanating from introducing such new investments. Furthermore, the study approximates the storage capabilities of HPPs and investigates the impact of simplified HPP modeling on system operation and investment decisions. Overall, the findings underscore the importance of reevaluating hydro rule curves for future high VRES penetration conditions and highlight the significance of HPPs in the energy transition towards carbon neutrality. [ABSTRACT FROM AUTHOR]

This work presents an implementation of a Nested Decomposition Algorithm (NDA) applied to co-optimizing generation and transmission capacity expansion planning problems in power systems, including operational flexibility constraints. The proposed methodology has been gaining relevance in recent years, as it can efficiently solve large mixed-integer problems faster than the conventional extensive formulation (mixed-integer linear programming). Three case studies are conducted on two IEEE test power systems to evaluate the algorithm's performance and cut configuration. The first case study compares the performance between the NDA and the extensive formulation. The second case study compares the performance of each cut type, analyzing differences in simulation times and algorithm convergence. The third case study proposes a set of cut patterns based on the prior outcomes, whose performance and convergence are tested. Based on the simulation results, conclusions are drawn about the capability and performance of the NDA applied to the capacity expansion planning problem. The study shows that obtaining results with reasonable convergence in less simulation time is possible using a particular pattern. [ABSTRACT FROM AUTHOR]

Throughout his career, Michael Bratman has developed a detailed model of individual 'planning agency', and, more recently, models of joint action and aspects of social life that he argues such planning agency helps support. How might we empirically investigate whether these models capture what is going on in actual human lives? In this article, we critically engage with this broad question by focusing on what Bratman calls the 'core capacity thesis', which is at the center of his most recent discussions of how his work in the philosophy of action hangs together. In his own words, the core capacity thesis claims that 'our capacity for planning agency plays basic roles in multiple, interrelated systems of human, mind‐shaped practical organization, individual and social'. We explore how one might go about testing this thesis, focusing on Bratman's particular way of defending this thesis using the idea of 'nested Cummins functions'. In so doing, we hope to contribute to a broader discussion about what the relationship is (or, perhaps more importantly, should be) between the kind of philosophy of action Bratman does and empirical work in fields such as psychology, cognitive science, and biology. [ABSTRACT FROM AUTHOR]

Planning allows us to coordinate our actions over time, and the ability to plan is crucial in many areas of our lives. I argue that while planning is deeply embedded in contemporary societies, not all individuals have equal access to the structures that support such planning. This article explores how external planning‐support structures are essential to our capacity to plan and how inequality in access to these structures can impact an individual's ability to deliberate and pursue long‐term plans. I conclude by suggesting that we need to think more deeply about unequal access to the structures that support planning directly, not just inequality in resources. [ABSTRACT FROM AUTHOR]

Following Bratman, I distinguish between the Cummins or component‐function of the planning capacity (its role as a component of larger forms of practical organizations) and its Wright or existence‐function – the planning capacity's effect that explains its existence. I agree with Bratman that these functions are distinct. The planning capacity's role within larger practical organizations need not explain its origin. But I argue that the distinction is less stark for future‐oriented existence‐functions, which concern the future persistence and stability of the capacity. In mature practical organizations, component‐functions usually give rise to matching future‐oriented existence‐functions, given that pressures for the persistence of practical organizations transfer to their components (such as the planning capacity) because of their component‐functions. But in the special case of individual temporally extended agency, there is an even stronger alignment of functions. The planning capacity and individual temporally extended agency are part of an inextricable package. The capacity is not just a distinct component. This entails that, within individual agency, the two functions merge and the planning capacity is the primary rather than the derivative target of pressures of persistence, care, and value. Or so I speculatively conjecture. [ABSTRACT FROM AUTHOR]

Our capacity for planning agency is a core capacity that underlies interrelated forms of mind‐shaped practical organization: cross‐temporal organization of individual agency, shared agency, social rules, and rule‐guided organized institutions. A function of our capacity for planning agency is the support of these forms of practical organization. I highlight Peter Godfrey‐Smith's contrast between the 'Wright function' of something as 'the effect it has which explains why it is there' and 'Cummins functions' that 'are capacities or effects of components of systems, which are salient in the explanation of capacities of the larger system'. Drawing on Paul Grice's strategy of 'creature construction', I articulate a sequence of nested constructions: from temporally extended planning agency to shared agency to shared policies to social rules to rule‐guided organized institutions. We see our capacity for planning agency as part of an explanation of how we achieve such practical organization, and as having nested Cummins functions of supporting those forms of organization. This sheds light on related ideas in H.L.A. Hart's theory of law, a challenge from J. David Velleman, the centrality of such forms of organization to the philosophy of action, and the moral and political significance of our capacity for planning agency. [ABSTRACT FROM AUTHOR]

In recent years, global warming and environmental problems have become more serious due to greenhouse gas (GHG) emissions. Harvesting solar energy for production and logistic activities in supply chains, including factories and distribution centers, has been promoted as an effective means to reduce GHG emissions. However, it is difficult to balance the supply and demand of solar energy, owing to its intermittent nature, i.e., the output depends on the daylight and season. Moreover, the use of large-capacity solar power generation systems and batteries incurs higher installation costs. In order to maintain low costs, demand-to-supply management of solar energy, based on appropriate seasonal analysis of power generation and consumption and the capacity planning for power generation and the storage battery, is necessary. In this study, the on-demand cumulative control method is applied to actual power consumption data and solar power generation data estimated at a distribution center. Moreover, the monthly, seasonal, and temporal characteristics of power generation and consumption at the distribution center are analyzed. Additionally, the total amount of power purchased is investigated for solar energy demand-to-supply management. [ABSTRACT FROM AUTHOR]

Reliable forecasts are key to decisions in areas ranging from supply chain management to capacity planning in service industries. It is encouraging then that recent decades have seen dramatic advances in forecasting methods which have the potential to significantly increase forecast accuracy and improve operational and financial performance. However, despite their benefits, we have evidence that many organizations have failed to take up systematic forecasting methods. In this paper, we provide an overview of recent advances in forecasting and then use a combination of survey data and in-depth semi-structured interviews with forecasters to investigate reasons for the low rate of adoption. Finally, we identify pathways that could lead to the greater and more widespread use of systematic forecasting methods. [ABSTRACT FROM AUTHOR]

Assessing the postdisaster seismic responses of the as-built bridges based on their real-time status is critical to maintaining their seismic capacity and planning retrofits. This task requires a fast and accurate identification and response estimation method, whereas existing methods either adopt model updating requiring detailed design archives or request sophisticated site testing, both of which are time-consuming and labor-intensive. In this study, approaches combining system identification methods with response spectrum methods for bridges are investigated. A general state-space representation for bridges considering multisupport excitation is developed for model identification. The assumption of the boundary damping matrix in the multisupport response spectrum (MSRS) method is investigated and proved to eliminate pseudo-damping for general viscous-damping structures. A hybrid frequency fitting method (HFF) and subspace system identification method (SSI) in the time and frequency domains, respectively, are examined, and approaches to extracting equivalent boundary stiffness and damping from the identified structural models are studied. The MSRS method is adapted to both identification methods. Numerical simulations and experiments are used to validate the identification approaches in the estimation of boundary conditions. The models identified from the shake table experiments are further used in evaluating its seismic responses. The results of the MSRS method are compared with those from the spectral analysis method showing that the HFF method performs well both in identifying the bridge model and estimating the structural responses. Whereas, the SSI-based methods fail to give a good estimation of boundary damping, and thus cannot directly apply to the MSRS method. [ABSTRACT FROM AUTHOR]

For achieving the carbon peaking and carbon neutrality goals, the renewable energy and the battery energy storage will be developed more rapidly. The participation of the solar-wind-battery renewable energy system (SWBS) in black-start can improve the resilience of the power grid. Aiming at the balance between the capacity demand of SWBS participating in black-start and the benefit of SWBS, this paper proposes a positive-sum game-based SWBS capacity configuration planning method. First, a levelized cost of energy- (LCOE-) based full life cycle index is presented and constructs an economic benefit model of SWBS considering the participation of wind and solar power plants in carbon trading. Second, a black-start capacity demand model is constructed considering the continuous power demand on the auxiliary devices of the operated thermal power generator. Taking the economic benefit of SWBS and the black-start capacity requirement into account, this paper establishes a positive-sum game-based SWBS capacity optimal configuration model, in which maximizing the economic benefits of SWBS as an objective. Third, the simulation analysis is carried out by using a real power system, and results verify that the proposed method can achieve the optimal balance between the feasibility of black-start and the economic benefit of the SWBS. [ABSTRACT FROM AUTHOR]

We study the capacity planning and allocation decisions for multiple heterogeneous resources, considering potential demand scenarios, where each demand requests a subset of the available resource types simultaneously at a specified time, location, and duration (smRmD). We model this problem as a two-stage stochastic integer program and consider two variants for the objective function: (a) maximize the expected reward of demands met over all scenarios, subject to a budget B for resources, and (b) maximize the expected reward of demands met over all scenarios minus the cost of resources. Contributions of this work include (i) a thorough complexity analysis of smRmD and its variants, (ii) analysis of structural properties, (iii) development of various approximation algorithms using the unique structural properties of smRmD and its variants, and (iv) an extensive computational study to explore the ease with which exact and approximate solutions may be found. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research has been supported in part by National Science Foundation (NSF) Graduate Research Fellowship [DGE-1650044], NSF [Grants CMMI-1538860, NSF-AF:1910423, and NSF-AF:1717947], and the following Georgia Tech benefactors: William W. George, Andrea Laliberte, Richard "Rick" E. & Charlene Zalesky, and Claudia & Paul Raines. [ABSTRACT FROM AUTHOR]

The continuous growth of renewable energy sources (RESs) has increased the demand for flexibility in managing uncertainties of RES generation. Energy storage systems (ESSs) are recognized as one of the promising methods to address this challenge. For multi‐area power system planning problems, capacity allocations of RESs can vary considerably among areas accounting for the geographic diversities in RES generation and load patterns. This paper presents a hierarchical coordinated planning model considering the interaction of local‐area (LA) planning entities and the system‐wide (SW) planner. A novel multi‐objective LA planning model is proposed to compute optimal capacity configuration ratios of RESs and ESSs based on regional resource characteristics. The SW planner acts as a coordinator and further optimizes specific capacities of generation assets. Optimal configuration ratios produced by LA planning entities are important indices reflecting LA planning preferences and serve as a link between the two planning layers. Numerical results on a modified NREL‐118 test system show that the proposed planning model can reduce carbon emission by an average of 10.5% and improve the RES utilization rate by an average of 1.8% compared with planning models without considering configuration ratios and ESS installations. [ABSTRACT FROM AUTHOR]

The protection of a person facing difficulties with legal decision-making can be challenging; however, estate planning documents (EPDs) can offer safeguards. Little is known about the community awareness of EPDs and their perceived importance, including for people with dementia. Two-hundred and thirty adults read a mild dementia vignette, proposed a diagnosis for the vignette character (VC), and rated the VC's legal decision-making capacity (DMC). The effect on ratings about DMC under different conditions was explored (e.g., variation of VC characteristics and the decision). Awareness, and the personal importance, of EPDs were assessed. DMC was affected by the variations in the VC's age, diagnosis, and the decision, as high- or low-stakes, but not VC sex. Awareness of EPDs was low; however, planning importance was increased for dementia. Although the public understand that dementia affects DMC, they require further information about EPDs to validly enact them, thereby reducing potential vulnerability. [ABSTRACT FROM AUTHOR]

The Public Organization Review is inviting article proposals for a Special Issue Symposium on "Public-Private Partnerships Essential for Mitigating against, Preparing for, Responding to, and Recovering from Natural Disasters." The proposals should address experiences and lessons learned from partnerships among governments, nonprofit organizations, and private businesses in planning and building capacity to deal with natural disasters. Scholarly papers on various sub-topics using quantitative and qualitative analysis approaches will be considered. The deadline for proposal submission is November 30, 2023, and accepted manuscripts must be submitted by March 15, 2024. The Special Issue will be published online first and in print in September 2024. [Extracted from the article]

Intensive care units (ICUs) are one of the most important elements of hospitals. ICUs play a central role in the healthcare system and in providing care for critical patients, so capacity planning in these units is critical. A shortage of ICU beds and staff can have irreparable consequences, including patient death. As a result, hospital managers make efforts to determine the appropriate number of beds. However, the interarrival time (IAT) of patients to ICUs and the service time (ST) of patients in ICUs are stochastic in nature. Consequently, capacity planning is a dynamic operations management problem. For this research, we used mixture distributions to approximate the interarrival time (IAT) and service time (ST) of patients in ICUs. We then incorporated these distributions into a simulation model that helps us to determine the number of beds needed to accommodate all incoming patients without any waiting in the queue. The results show that the mixture distributions provide a better estimate than empirical statistical distributions [ABSTRACT FROM AUTHOR]

Reasonable production capacity is related to the economic benefits of an open-pit coal mine. This study analyzes the relationship between the working face length, the annual advancing speed and the production capacity. It constructs a production capacity function relationship model. Take the Baorixile open-pit coal mine as an example. The remaining unmined parts are divided into four regions, and the range of production capacity in each region is analyzed by the established model and the determined respectively. On this basis, three mining district division plans are proposed. By analyzing and comparing the stripping ratio, mining life of the district, fault influence, difficulty of transition connection in the mining districts, the convenience of transportation system layout and other indexes of each plan, Plan 3 is determined to be the optimal plan. The production capacity planning results of each mining district in this plan are as follows: the production capacity of the 3rd mining district is 30–35 Mt/a; the production capacity of the 4th mining district in Region 1 is 20–31 Mt/a, and the production capacity in Region 2 is 24–33 Mt/a; the production capacity of the 5th mining district is 20–27 Mt/a. [ABSTRACT FROM AUTHOR]

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery's power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility. [ABSTRACT FROM AUTHOR]

Humans experience small fluctuations in their gait when walking on uneven terrain. The fluctuations deviate from the steady, energy-minimizing pattern for level walking and have no obvious organization. But humans often look ahead when they walk, and could potentially plan anticipatory fluctuations for the terrain. Such planning is only sensible if it serves some an objective purpose, such as maintaining constant speed or reducing energy expenditure, that is also attainable within finite planning capacity. Here, we show that humans do plan and perform optimal control strategies on uneven terrain. Rather than maintaining constant speed, they make purposeful, anticipatory speed adjustments that are consistent with minimizing energy expenditure. A simple optimal control model predicts economical speed fluctuations that agree well with experiments with humans (N = 12) walking on seven different terrain profiles (correlated with model p = 0.55 ± 0.11, P < 0.05 all terrains). Participants made repeatable speed fluctuations starting about six to eight steps ahead of each terrain feature (up to ±7.5 cm height difference each step, up to 16 consecutive features). Nearer features matter more, because energy is dissipated with each succeeding step's collision with ground, preventing momentum from persisting indefinitely. A finite horizon of continuous look-ahead and motor working space thus suffice to practically optimize for any length of terrain. Humans reason about walking in the near future to plan complex optimal control sequences. [ABSTRACT FROM AUTHOR]

With the development of multi-energy systems and the urgent demand for low-carbon energy provision, the regional integrated energy system (IES) is considered an efficient paradigm to improve energy efficiency and carbon emission reduction. The optimal capacity planning of different IES components is considered a non-trivial task due to the uncertainties of renewable power generation and various demands (e.g., electricity, heating and cooling) as well as the carbon emission constraints. This paper addresses this challenge and presents a scenario-based robust optimal planning solution for the regional low-carbon IES fully considering the economic cost, carbon emission and energy supply reliability. The operational uncertainties of different forms of energy sources and demands are characterized by a controllable generative adversarial network (GAN). The proposed method is extensively assessed based on an IES case study in China through a comparative analysis, the numerical results show that compared with the traditional planning method, the proposed capacity planning solution can reduce the total cost by 4.24% and the carbon emissions by 42.61%, the effectiveness and benefits of the planning solution have been effectively confirmed. • A scenario-based robust capacity planning solution of IES with carbon emission consideration is proposed. • A controllable GAN-based scenario generation is adopted for operational uncertainty characterization. • The proposed solution can reduce both the total cost and carbon emissions. [ABSTRACT FROM AUTHOR]

Investment decisions in competitive power markets are based upon thorough profitability assessments. Thereby, investors typically show a high degree of risk aversion, which is the main argument for capacity mechanisms being implemented around the world. In order to investigate the interdependencies between investors’ risk aversion and market design, we extend the agent-based electricity market model PowerACE to account for long-term uncertainties. This allows us to model capacity expansion planning from an agent perspective and with different risk preferences. The enhanced model is then applied in a multi-country case study of the European electricity market. Our results show that assuming risk-averse rather than risk-neutral investors leads to slightly reduced investments in dispatchable capacity, higher wholesale electricity prices, and reduced levels of resource adequacy. These effects are more pronounced in an energy-only market than under a capacity mechanism. Moreover, uncoordinated changes in market design may also lead to negative cross-border effects. [ABSTRACT FROM AUTHOR]

Obstetric surgical suites differ from most inpatient surgical suites, serving one specialty, and often small. We evaluated long-term capacity planning for these operating rooms. The retrospective cohort study included all caesarean births in three operating rooms over 28 years, 1994 through 2021, plus all other obstetric procedures over the latter 19 years. We calculated the obstetric anaesthesia activity index, 0.5 × neuraxial labour analgesia placement + 1.0 × caesarean births. Annual caesarean births from one year to the next had a Pearson linear correlation coefficient of 0.993. Therefore, linear regression can be used for long-term capacity planning. However, the difference between 0.9 and 0.1 quantiles in weekly caseloads was greater than tenfold larger than the annual rate of growth in births per week. Therefore, clinicians likely would be unable to distinguish, by experience, between growth versus being busy due to variability, suggesting value of the modelling. Over 19 years, the fraction of the obstetric workload from caesarean births was unchanging, Pearson correlation coefficient of 0.04. Therefore, use of the obstetric anaesthesia activity index to judge changes in workload was appropriate. The annual total for the index increased linearly, Pearson correlation coefficient of 0.98, supporting validity of the finding that long-term capacity can be planned with linear regression. The difference between 0.9 and 0.1 quantiles in weekly totals of the index exceeded annual rate of growth, supporting validity of the finding that variability week to week is very large relative to growth. These results help decision-makers ensure that operating rooms and staff meet referring hospitals' needs. [ABSTRACT FROM AUTHOR]

Health systems are human based systems comprising a high number of stakeholders who interact with each other in complex ways. Due to the stochastic nature of health systems and the complex dynamics and interactions of their inputs, processes and outputs; providers of health services require tools tat allow them to comprehend the said complexity and improve system performance. Intensive care units (ICUs) may fall short in treating patients due to insufficient capacity and patients may be transferred to other hospitals under certain circumstances, and waiting times can get longer. Efficient use and management of a scarce resource such as the ICU is of critical importance for trouble-free operation of a hospital. Capacity planning, in other words, determination of optical bed configuration based on current knowledge of future demand and capacity, is very difficult due to high uncertainty in both bed capacity and demand. The paper at hand proposes a simulation model to allow an easier decision-making process in the planning of bed capacity in ICU. The study focuses on improving the capacity planning of a level III university hospital in Turkey that specializes in education and research. The objective of the study is to determine optimal bed requirement by matching the resources of the ICU with demand. We provide forecasts of ICU capacity by evaluating the simulation models based on patient waiting times and bed quantity. Considered in the context of the vital importance of intensive care units and the uncertainties in their management, it is thought that the use of simulation modeling in the making of capacity decisions can prove useful for decision makers in assessing their resource allocation strategies under different regional and hospital conditions. [ABSTRACT FROM AUTHOR]

Precise route planning needs huge amounts of trajectory data recorded in multimedia devices. The data, including each user's location privacy, are stored as cipher text. The ability to plan routes on an encrypted trajectory database is an urgent necessity. In this paper, in order to plan a public route while protecting privacy, we design a hybrid encrypted random bloom filter (RBF) tree on encrypted databases, named the encrypted random bloom filter (eRBF) tree, which supports pruning and a secure, fast k nearest neighbor search. Based on the encrypted random bloom filter tree and secure computation of distance, we first propose a reverse k nearest neighbor trajectory search on encrypted databases (RkNNToE). It returns all transitions, in which each takes the query trajectory as one of its k nearest neighbor trajectories on the encrypted database. The results can be the indicator of a new route's capacity in route planning. The security of the trajectory and query is proven via the simulation proof technique. When the number of points in the trajectory database and transition database are 1174 and 18,670, respectively, the time cost of an R2NNToE is about 1200 s. [ABSTRACT FROM AUTHOR]

In this paper, a multi-server queueing system providing service to two correlated flows of requests was considered. Non-preemptive priority was granted to one flow via the preliminary delay of requests in the intermediate buffers with different rates of extracting from the buffers. Customers' impatience during waiting in the intermediate and main buffers was taken into account. The possibility of the use of the results of the mathematical analysis for managerial goals is numerically illustrated. [ABSTRACT FROM AUTHOR]

Background: The size and cost of outpatient capacity directly affect the operational efficiency of a whole hospital. Many scholars have faced the study of outpatient capacity planning from an operations management perspective. Objective: The outpatient service is refined, and the quantity allocation problem of each type of outpatient service is modeled as an integer linear programming problem. Thus, doctors' work efficiency can be improved, patients' waiting time can be effectively reduced, and patients can be provided with more satisfactory medical services. Methods: Outpatient service is divided into examination and diagnosis service according to lean thinking. CPLEX is used to solve the integer linear programming problem of outpatient service allocation, and the maximum working time is minimized by constraint solution. Results: A variety of values are taken for the relevant parameters of the outpatient service, using CPLEX to obtain the minimum and maximum working time corresponding to each situation. Compared with no refinement stratification, the work efficiency of senior doctors has increased by an average of 25%. In comparison, the patient flow of associate senior doctors has increased by an average of 50%. Conclusion: In this paper, the method of outpatient capacity planning improves the work efficiency of senior doctors and provides outpatient services for more patients in need; At the same time, it indirectly reduces the waiting time of patients receiving outpatient services from senior doctors. And the patient flow of the associate senior doctors is improved, which helps to improve doctors' technical level and solve the problem of shortage of medical resources. [ABSTRACT FROM AUTHOR]

Hospital services constitute a fundamental pillar in the development of society. The objective of this article is to provide medium-term capacity planning improvements in a General Surgery service. A procedure was designed for capacity planning based on process analysis and design as well as the patients' clinical characteristics. Some of the main results were: the demand of the Major Diagnostic Categories was predicted, the rooms were identified as the limiting resource in this kind of service, where an 82.82 % of them were used, the necessary wards reservations were calculated for the year 2021, with a rate of 163, 96 and 59 times for incisional, inguinal and umbilical hernia surgeries respectively, and a system of capacity improvement actions which were projected. The analysis of capacity using the proposed heuristic method makes possible to identify limiting resources, enhance performance and devise strategies to meet the demand. [ABSTRACT FROM AUTHOR]

C arbon-free microgrids (CFMs) composed of renewable energy and hydrogen energy storage systems (HESS) are a promising solution for low-carbon energy systems. However, when CFMs are off-grid, their reliability is frequently questioned, especially when renewable energy (RE) outputs are extremely low. To reduce the total cost and improve the off-grid reliability, this paper proposes a two-stage robust planning model for CFM with renewable energy and HESS that considers both grid-connected (GC) and short-term (several days) off-grid (OG) statuses. In the GC status, the target is to minimize the system expenses; while in the OG status, the optimization goal is not to lose the important loads in a short period of time considering the worst realization of days. The Nested-C&CG algorithm is employed to solve the robust planning model of CFM. Additionally, a new uncertainty set that takes terrible days into account for RE and loads is presented to make the decision robust. Finally, the numerical analysis based on the case studies shows that the HESS in CFM has the ability to support loads in OG status for a week. And the total cost of the CFM using HESS and batteries can be reduced by 11.83% compared with that using only batteries. In addition, the planning results with robust optimization can guarantee that all the important loads are satisfied. [ABSTRACT FROM AUTHOR]

Multi-access edge computing (MEC) offers cloud computing capabilities and IT services situated at the Radio Access Network (RAN) in the mobile users' proximity. Applications could offload their computation-intensive tasks to the MEC servers. Consequently, MEC significantly diminishes the mean response time and job rejection probability compared to conventional Mobile Cloud Computing (MCC). Cost-performance trade-off is one of the major concerns of the system designers. Low performance leads to the Service Level Agreement (SLA) violation and disappoints the service consumers. On the other hand, reaching high performance by augmenting the number of servers in the MEC and Cloud sides incur more infrastructure and other operational costs. In this paper, we formulate the mentioned cost-performance trade-off into an optimization problem. We demonstrate that the optimization problem is integer and non-linear. Moreover, we propose a capacity planning framework to determine the optimal number of servers in the MEC and Cloud sides, minimizing the Total Cost of Ownership (TCO) with SLA satisfaction. The proposed capacity planning framework gains from the simulated annealing algorithm to obtain a globally optimum solution. Furthermore, we deploy the stochastic performance model to measure mean response time and job rejection probability at each iteration. Numerical results reveal that the proposed framework determines the optimal solution within a reasonable time. [ABSTRACT FROM AUTHOR]

The first approaches to conceptualizing software ecosystems (SECO) appeared more than 15 years ago, and few studies have addressed proprietary SECO. In a proprietary SECO, organizations are concerned with keeping their assets protected by intellectual property. We first report on a longitudinal literature study to understand how the governance mechanisms work and organizations handle incidents in SECOs, updating a previous systematic literature review covering from 2006 to 2015 with new studies from 2016 to 2020. Next, we investigated the governance mechanisms in a proprietary SECO of a large international organization through a participative case study. Moreover, we gathered information on governance in practice and defined strategies to implement governance mechanisms measured by health metrics, as follows: 1) observation: analyzing the behavior of the participants in the face of problematic situations; 2) interviews: collecting participants’ information on the adoption of governance mechanisms; and 3) opinion survey: verifying the level of participants’ perception about the new strategies related to proprietary SECO governance mechanisms. Based on the results, we derived practical implications and contributions to provide a research agenda for academics and practitioners, such as workforce capacity planning in order to gain a competitive advantage in the market. [ABSTRACT FROM AUTHOR]