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Seasonal effects can significantly impact the robustness of socio-technical systems (STS) to demand fluctuations. There is an increasing need to develop novel design approaches that can support capacity planning decisions for enhancing the robustness of STS against seasonal effects. This paper proposes a new network motif-based approach to supporting capacity planning in STS for an improved seasonal robustness. Network motifs are underlying nonrandom subgraphs within a complex network. In this approach, we introduce three motif-based metrics for system performance evaluation and capacity planning decision- making. The first one is the imbalance score of a motif (e.g., a local service network), the second one is the measurement of a motif's seasonal robustness, and the third one is a capacity planning decision criterion. Based on these three metrics, we validate that the sensitivity of STS performance against seasonal effects is highly correlated with the imbalanced capacity between service nodes in an STS. Correspondingly, we formulate a design optimisation problem to improve the robustness of STS by rebalancing the resources at critical service nodes. To demonstrate the utility of the approach, a case study on Divvy bike-sharing system in Chicago is conducted. With a focus on the size-3 motifs (a subgraph consisting three docked stations), we find that there is a significant correlation between the difference of the number of docks among the stations in a motif and the return/rental performance of such a motif against seasonal changes. Guided by this finding, our design approach can successfully balance out the number of docks between those stations that have caused the most severe seasonal perturbations. The results also imply that the network motifs can be an effective local structural representation in support of STS robust design. Our approach can be generally applied in other STS where the system performances are significantly impacted by seasonal changes, for example, supply chain networks, transportation systems and power grids. [ABSTRACT FROM AUTHOR]

The joint optimization problem of multiresource capacity planning and multitype patient scheduling under uncertain demands and random capacity consumption poses a significant computational challenge. The common practice in solving this problem is to first identify capacity levels and then determine patient scheduling decisions separately, which typically leads to suboptimal decisions that often result in ineffective outcomes of care. In order to overcome these inefficiencies, in this paper, we propose a novel two-stage stochastic optimization model that integrates these two decisions, which can lower costs by exploring the coupling relationship between patient scheduling and capacity configuration. The patient scheduling problem is modeled as a Markov decision process. We first analyze the properties for the multitype patient case under specific assumptions and then establish structural properties of the optimal scheduling policy for the one-type patient case. Based on these findings, we propose optimal solution algorithms to solve the joint optimization problem for this special case. Because it is intractable to solve the original two-stage problem for a general multitype system with large state space, we propose a heuristic policy and a two-stage stochastic mixed-integer programming model solved by the Benders decomposition algorithm, which is further improved by combining an approximate linear program and the look-ahead strategy. To illustrate the efficiency of our approaches and draw managerial insights, we apply our solutions to a data set from the day surgery center of a large public hospital in Shanghai, China. The results show that the joint optimization of capacity planning and patient scheduling could significantly improve the performance. Furthermore, our model can be applied to a rolling-horizon framework to optimize dynamic patient scheduling decisions. Through extensive numerical analyses, we demonstrate that our approaches yield good performances, as measured by the gap against an upper bound, and that these approaches outperform several benchmark policies. Summary of Contribution: First, this paper investigates the joint optimization problem of multiresource capacity planning and multitype patient scheduling under uncertain demands and random capacity consumption, which poses a significant computational challenge. It belongs to the scope of computing and operations research. Second, this paper formulates a mathematical model, establishes optimality properties, proposes solution algorithms, and performs extensive numerical experiments using real-world data. This work includes aspects of dynamic stochastic control, computing algorithms, and experiments. Moreover, this paper is motivated by a practical problem (joint management of capacity planning and patient scheduling in the day surgery center) in our cooperative hospital, which is also key to numerous other applications, for example, the make-to-order manufacturing systems and computing facility systems. By using the optimality properties, solution algorithms, and management insights derived in this paper, the practitioners can be equipped with a decision support tool for efficient and effective operation decisions. [ABSTRACT FROM AUTHOR]

As more variable renewable energy (VRE) and energy storage (ES) facilities are installed, accurate quantification of their contributions to system adequacy becomes crucial. We propose a definition of capacity credit (CC) for valuing adequacy contributions of these resources based on their marginal capability to reduce expected unserved energy. We show that such marginal credits can incentivize system-optimal investments in markets with installed capacity requirements and energy price caps. We simulated such markets using a LP-based capacity expansion planning model with convexified unit commitment (UC) constraints and ES. The impacts of UC and ES on capacity credits are investigated. Furthermore, we analyze technology and system cost distortions resulting from implementing inaccurate CCs in the capacity market. The results show that ignoring UC constraints can overestimate the CCs for VRE and ES. Building ES increases the CCs of VRE resources with higher capacity factors and a negative correlation with load. Assigning the wrong credit to VRE can significantly distort resource mixes and system cost. Implementing the proposed CCs can, in theory, eliminate those distortions and achieve the same overall optimum as a theoretical market without energy price caps. [ABSTRACT FROM AUTHOR]

Objectives. Systematize and analyze the response actions related to human resources for health during the pandemic, reported by 20 countries of the Region of the Americas in the mid-term evaluation of the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (Pan American Health Organization, 2018), and assess the importance of the policies on human resources for health (HRH) and on HRH management expressed in the Plan of Action and in the Strategy on Human Resources for Universal Access to Health and Universal Health Coverage during health emergencies and in normal times. Methods. Reports on actions taken in 20 countries of the Region against COVID-19 and for HRH were selected and systematized. These were classified as immediate contingency actions, actions related to installed capacities, and emerging actions. Results. The capacity to plan and manage HRH in countries depends on their installed, functional structures and competencies. The pandemic highlighted the need to have new job profiles, improve precarious working and contractual conditions, emphasize the gender perspective, and address numerical gaps in certain areas and levels of care. Conclusions. Linking the monitoring of the Plan of Action with the COVID-19 response demonstrated the importance of HRH governance, management, and installed capacities when responding to health emergencies and in normal times. The analysis suggests a need to review existing public policies, models of care that can guide current and future needs in HRH, the profiles required, working conditions, and ways to close numerical gaps, among other issues. The pandemic enabled countries to innovate in response to demands. The Strategy and the Plan of Action remain in place to guide and strengthen the performance of human resources for health. [ABSTRACT FROM AUTHOR]

Cloud datacenters provide a backbone to our digital society. Inaccurate capacity procurement for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist for mid-tier operators, leaving many planners with merely rule-of-thumb judgement. We derive requirements from a unique survey of experts in charge of diverse datacenters in several countries. We propose Capelin, a data-driven, scenario-based capacity planning system for mid-tier cloud datacenters. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. Capelin compares alternative topologies and for each gives detailed quantitative operational information, which could facilitate human decisions of capacity planning. We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. The results give evidence that reasonable choices can be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption. [ABSTRACT FROM AUTHOR]

The article aims to characterize the current strategic planning capacity of the Romanian Ministry of Energy, based on the ability to develop and implement sustainable strategic policies. Thus, the paper will start by summary analyzing the main concepts of strategic planning, further characterizing the current institutional capacity of the Romanian Ministry of Energy based on three relevant levels (the macro, micro, and strategy development environments). The methodology will focus on the study case of the Offshore Law legislative process from the last four years (2018-2022), the new version being recently adopted. The results will emphasize the public strategic planning issues and their effects, pointing out the overall business environment perception, an essential actor for the successful capitalization of energy resources. [ABSTRACT FROM AUTHOR]

This paper is devoted to the substantiation of methodological aspects of strategic planning of the capital-forming base of enterprises in accordance with the objective features of the knowledge economy. Regarding the basic researched category — «capital» — it is proposed to interpret it according to financial approach. At the same time in the context of business management and its development it is important to consider the peculiarities of capital-forming base, in particular, taking into account theories of intellectual (human) and information capital. In this case, the capital-forming base of the enterprise is proposed to be understood as a set of assets (financial, tangible and intangible) in its ownership and / or disposal, and theirs usage forms the potential of the entity’s development over time. At the same time, the formation of the capital-forming base of the enterprise in the conditions of knowledge economy formation should be based on the principles of complexity, systemicity, strategicity and innovation. The main priorities of the formation of the enterprises’ capital-forming base include: 1) ensuring stable operation over time by creating a sufficient margin of safety; 2) accumulation of potential for sustainable development; 3) the formation of the company’s ability to innovative transformation. Strategic planning of the capital-forming base of enterprises is proposed on the basis of a foresight approach. In this paper the critical analysis of its advantages and disadvantages for solving the scientific problem and the semantic distinction between ‘forecasting’, ‘foresight’, ‘prediction’ has been held. It is proved that the effectiveness of the foresight approach for strategic planning of the enterprise’s capital-forming base is ensured by a logical combination of five factors: 1) clarity of formulation of foresight targets and variation of their implementation; 2) validity and depth of understanding of the grounds for decision-making by all participants of the foresight; 3) reflection of target priorities in specific research, investment and other projects; 4) independence of foresight participants’ identification of their positions, except for the order of interaction of participants (determined by foresight organizers); 5) possible change of positions of participants depending on initially accepted determinants. Also, the implementation capacity of strategic planning of enterprise’s activity directly depends on the availability and quality of industry strategy’s development made by the relevant government institutions. Methodological features of strategic planning of the capital-forming base are considered on the enterprises of the construction industry. It is formed the contour determinants of innovative transformation drivers of the construction industry (as the fundamental basis of strategic planning of the enterprises’ capital-forming base) in the following semantic directions: technological, ecological, personnel, economic, managerial. In turn, for strategic planning of the capital-forming base of construction industry enterprises technological, social, personnel, financial and managerial components have been identified and for each unit the operational measures have been formulated. [ABSTRACT FROM AUTHOR]

Post‐demolition autoclaved aerated concrete in Germany until 2050 – a forecast Autoclaved aerated concrete (AAC) is a widely used building material for masonry blocks as well as prefabricated reinforced components and mineral insulation boards with growing popularity due to its exceptional insulating properties. Waste AAC from the demolition and dismantling of buildings is currently mainly landfilled. However, landfill capacities are decreasing, and the legal framework requires fixed recycling rates. Information is needed on recyclable quantities, temporal occurrence, and regional distribution to establish a high‐quality recycling network for post‐demolition AAC. Since this information is not yet available, a new model was developed to quantify post‐demolition AAC volumes using historical AAC production, construction activity, regional market shares of AAC and building lifetimes. The model was applied for Germany from 1950 to 2050 (year‐by‐year) with a geographical subdivision into 401 regions. According to the results, the post‐demolition AAC volume is expected to rise sharply in the coming decades. The volume in Germany could increase from 160,000 m3 (2000) to 1,200,000 m3 (2020) to more than 4,000,000 m3 (2050). Significant volumes are expected, especially in large German cities such as Berlin, Hamburg, Munich, Bremen, Hanover, Cologne, Frankfurt and Stuttgart. These results offer a decision‐making aid for the recycling of post‐demolition AAC concerning location and capacity planning as well as logistics. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have received considerable attention in dealing with severe environmental and energy crises. The capacity planning of public charging stations has been a major factor in facilitating the wide market penetration of EVs. In this paper, we present an optimization model for charging station capacity planning to maximize the fuzzy quality of service (FQoS) considering queuing behavior, blocking reliability, and multiple charging options classified by battery technical specifications. The uncertainty of the EV arrival and service time are taken into account and described as fuzzy numbers characterized by triangular membership functions. Meanwhile, an $\alpha$ -cuts-based algorithm is proposed to defuzzify the FQoS. Finally, the numerical results illustrate that a more robust plan can be obtained by accounting for FQoS. The contribution of the proposed model allows decision-makers and operators to plan the capacity of charging stations with fuzzy EV arrival rate and service rate and provide a better service for customers with different charging options. [ABSTRACT FROM AUTHOR]

The research work represents the development of a new decision-making model intended for the resource capacity planning depending on the production system constraints. A mathematical decision model for medium and short-term (dynamic) workers allocation was developed. A simulation study of the dynamic events and the response of the decision-making algorithm to achieve the optimal workers allocation was conducted. The results show that the resource capacity planning has a high importance as the constraints faced by workers in production systems become more severe. The results demonstrate the high ability to terminate production capacity while ensuring smooth, efficient operation of the production system. Presented method can be used in everyday workers allocation in different types of production systems. [ABSTRACT FROM AUTHOR]

We focus on capacity planning and admission control of people accessing a system consisting of paths, where the flow varies along spatial, temporal, or event‐based dimensions. People arrive during a time window, are admitted, travel through specified paths, and leave the system within a provided admission time guarantee. The decisions include choosing capacity for disruption plans and reactive capacity, adaptive path adjustment through redirection, and admission control in response to realized demand scenarios. We first develop a continuous‐time model to choose optimal capacity for disruption plans corresponding to an expected admission time guarantee. The next model determines the optimal upfront versus reactive capacity decisions given demand scenarios. For each disruption plan, a mathematical programming model chooses optimal capacity by path segment, redirection, and admission control over time to manage realized demand under an admission time guarantee. We quantify the value of flexible capacity deployment and: (a) provide the optimal capacity by disruption plan to ensure an admission time guarantee, (b) provide the optimal long term capacity and reactive capacity, and (c) provide the optimal adaptive routing and admission control decisions to minimize capacity costs to ensure an admission time guarantee. We apply the model to data from the Kumbh Mela at Ujjain, a bathing festival in India that attracts close to 85 million people over a 25‐day period, to provide insights for managers. The models can assist managers of food festivals, music events, religious walks, and other events involving managing people flow across paths during specific time windows. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the resulting utilization and cycle times in manufacturing settings that are subject to significant capacity losses due to setups when switching between different product or part types. In particular, we develop queuing approximations for a multi-item server with sequence-dependent setups operating under four distinct setup rules that we have determined to be common in such settings: first-in-first-out, setup avoidance, setup minimization and type priority. We first derive expressions for the setup utilization and overall utilization, and use Kingman's well-known approximation to estimate the average cycle time at the station under each setup rule. We test the accuracy of the approximations using a simulation experiment, and provide insights on the use of different setup rules under various conditions. [ABSTRACT FROM AUTHOR]

Continuous flow scheduling problems have their place in many industries such as gas, oil, chemicals, glass and fluids production as well as production of granular goods and steel details. The disruptions in processing capacities may result in schedule performance decrease. In this paper, we develop a new method for robustness analysis of those schedules that are formulated in continuous time in the state-space domain. The developed method is based on attainable sets (ASs) that allow computing a form to represent the states and performance of schedules in regard to different capacity degradation levels. Having such a form, it becomes possible to estimate the schedule robustness. The technical development and approximation of ASs are presented. A robustness index is developed on the basis of the minimax regret approach, and it can be used for decision-makers regarding the trade-off ‘performance vs. robustness’. As such, it becomes possible to compare maximal possible profits in situations without disruptions and realistic profits subject to some robustness investments and costs of protection against disruptions. With the presented results, it becomes possible to obtain ASs for interval data with no a priori information about perturbation impacts, i.e. for non-stationary perturbations. ASs permit to consider perturbations and schedule performances astime functions. Perturbation functions may be set up for different uncertainty scenarios, including interval perturbations. [ABSTRACT FROM AUTHOR]

We consider a significant problem that arises in the planning of many projects. Project companies often use outsourced providers that require capacity reservations that must be contracted before task durations are realized. We model these decisions for a company that, given partially characterized distributional information, assumes the worst-case distribution for task durations. Once task durations are realized, the project company makes decisions about fast tracking and outsourced crashing, to minimize the total capacity reservation, fast tracking, crashing, and makespan penalty costs. We model the company's objective using the target-based measure of minimizing an underperformance riskiness index. We allow for correlation in task performance, and for piecewise linear costs of crashing and makespan penalties. An optimal solution of the discrete, nonlinear model is possible for small to medium size projects. We compare the performance of our model against the best available benchmarks from the robust optimization literature, and show that it provides lower risk and greater robustness to distributional information. Our work thus enables more effective risk minimization in projects, and provides insights about how to make more robust capacity reservation decisions. Summary of Contribution: This work studies a financially significant planning problem that arises in project management. Companies that face uncertainties in project execution may need to reserve capacity with outsourced providers. Given that decision, they further need to plan their operational decisions to protect against a bad outcome. We model and solve this problem via adjustable distributionally robust optimization. While this problem involves two-stage decision making, which is computationally challenging in general, we develop a computationally efficient algorithm to find the exact optimal solution for instances of practical size. [ABSTRACT FROM AUTHOR]

Featured Application: This study examines the capacity requirement planning for resources utilized in multiterminal operations over time to understand the effect of levels of multiterminal cooperation on resource capacity requirement. Capacity sharing among neighboring terminals offer a means to meet increasing or unexpected demand for cargo-handling without additional capital investment. This study proposes a model for capacity requirement planning of major resources, such as quay cranes (QCs), storage space, and gate, in multiterminal port operations where demand is time dependent. A resource profile simulation is run to generate random events across the terminals and estimate the capacity requirement in the form of workload distributions on port resources over time-shifts. The effects on workload requirement, arising from multiterminal cooperation, are subsequently evaluated in consideration of different container flows among terminals. Experimental results suggest that higher transferring rate between terminals will reduce the QC intensity and storage space requirements but increase gate congestion. Variabilities in the QC intensity and storage space requirements also increase due to shorter stays and more movements in container inventory at the yard. The interaction effect between transferring and trans-shipment rates further shows that the average resource requirements for a terminal can be greatly reduced when the interterminal transferring of containers contributes positively to a more even workload redistribution across terminals. The most significant improvements occur when trans-shipment rate is 85% and transferring rate is 75% for QC intensity; trans-shipment rate is 90% and transferring rate is 60% for storage capacity; and trans-shipment rate is 80% and transferring rate is 75% for gate congestion. [ABSTRACT FROM AUTHOR]

The article presents the application of the original methodology to support tactical capacity planning in a medium-sized manufacturing company. Its essence is to support medium-term decisions regarding the development of the production system through economic assessment of potential change scenarios. It has been assumed that the developed methodology should be adapted to small and medium-sized enterprises (SMEs). Due to their flexibility, they usually have limited time for decision-making, and due to limited financial resources, they rely on internal competencies. The proposed approach that does not require mastery of mathematical modelling but allows streamlining capacity planning decisions. It uses the reasoning of throughput accounting (TA) supported by data obtained based on discrete event simulation (DES). Using these related tools in the design and analysis of change scenarios, make it possible for SME managers to make a rational decision regarding the development of the production system. Case studies conducted in a roof window manufacturing company showed the methodology. The application example presented in the article includes seven change scenarios analyzed based on computer simulations by the software Tecnomatix Plant Simulation. The implementation of the approach under real conditions has shown that a rational decision-making process is possible over time scale and with the resources available to SMEs for this type of decision. [ABSTRACT FROM AUTHOR]

A model that anticipates the passenger flow on trains will help passengers to avoid overcrowded trains in their journey planning. Such a model will also help rail industry to understand the current use of train capacity and plan the distribution of rolling stock, personnel and facilities. However, the existing studies only developed the models for forecasting the passenger flow in stations, which cannot reflect the true passenger number on trains. In this paper, a hierarchical modelling framework for passenger flow prediction is proposed. It includes two layers of fuzzy models, where a global model is used to predict for ordinary circumstances and a number of local models are used to predict the variations in passenger number due to specific factors, such as events and weather. A new data sifting method is proposed to obtain the most informative and representative data for model training, which greatly improves the modelling efficiency. The proposed method is then validated using a case study of forecasting the passenger flow of London Underground trains. [ABSTRACT FROM AUTHOR]

The electricity supply chain, especially electricity demand, is very sensitive to climate. Developing a clear and quantitative understanding of the impacts of climate change on monthly electricity demand is critical for long-term generation capacity planning to maintain a reliable electricity supply system. A methodological framework applicable to city-level study is proposed and applied in Tianjin to investigate the impacts of climate change on monthly electricity consumption. By combining the empirical results with an ensemble of climate predictions under three Representative Concentration Pathways (RCPs), the study simulates the changes in monthly electricity demand caused by climate change in Tianjin by the end-of-century. The simulation results showed that climate change is projected to have severe impacts on the frequency and intensity of monthly peak electricity demand. Specifically, electricity demand in July would be 56% higher than that in November in 2099 under the RCP8.5 scenario. Climate change may drive substantial changes to the electricity supply chain, and our study is indicative of a need for adaptive strategies. [ABSTRACT FROM AUTHOR]

Looming budget cuts will necessitate adept management to retain a military capable of competing and winning by avoiding the mistakes made in prior drawdowns. This article presents a framework for government and defense leaders to prepare for the coming drawdown and plan for the necessary capacity of tomorrow across the diplomatic, information, military, and economic framework. [ABSTRACT FROM AUTHOR]

Multi‐service networks are broadly used in different service industries. Effective design of these networks has a considerable impact on customer satisfaction. Controlling congestion created at service facilities (SFs) is an important function for improving service quality. There are different studies on modeling multi‐service network design, but they mostly neglect incorporating service‐quality aspects such as congestion into their models. This paper considers the problem of simultaneous service portfolio selection, SF location, and capacity planning in a multi‐service network with M/M/1 servers where the total congestion at established facilities should be controlled. The problem is formulated as a fractional 0‐1 programming model, which is linearized in several different ways. The linear reformulations are enhanced by exploiting the special structure of the original model. A comprehensive numerical study is carried out to compare the linear models. Finally, a real case on a community healthcare network is studied to provide a set of managerial insights. [ABSTRACT FROM AUTHOR]

The aim of this article is to understand how socially innovative spatial planning is fostered or impeded within disadvantaged rural areas and to identify the isomorphic dynamics that perpetuate governance failures and curb innovative capacities. This article draws from sociological institutionalist accounts within spatial planning to develop an integrated epistemological tool that traces the institutional qualities that affect the capacity for innovation. Mixed methods research was conducted in the NUTS3 region of Baixo Alentejo; the innovative spatial planning capacities were investigated at both the level of broad governance and the micro-environment of Local Action Groups (LAGs). The findings revealed that despite the enhanced institutional resources possessed by some LAGs, the weak interconnections with the broader governance framework restrict socially innovative spatial planning. In other words, in the absence of a strong governance chain of innovation, even the most empowered links might prove trivial. [ABSTRACT FROM AUTHOR]

India has been urbanizing at a tremendous speed. Its health, well-being, and infrastructure capabilities are changing. Disasters are growing more common, catastrophic, and costly. The steady increase in disasters and the increased vulnerability of people, facilities, built environment, and assets are alarming indicators that disaster preparedness must be strengthened and capacity planning for efficient response and recovery at the emergency level must be in place. In crises, ad hoc and unplanned capacity planning might result in additional losses or intensify the disaster-stricken population. This article examines factors of health capacity in controlling disaster risks from the perspective of a rapidly urbanizing nation. [ABSTRACT FROM AUTHOR]

By guiding where, how, and when development occurs, comprehensive plans are an important vehicle to implement green infrastructure. To examine how green infrastructure is incorporated into local comprehensive plans, we evaluate 38 city and county plans in coastal Texas. Like many coastal regions, coastal Texas is experiencing rapid population growth, environmental degradation, and growing natural hazard threats. We found that local comprehensive plans in this region do not effectively incorporate green infrastructure. Few plans include goals related to green infrastructure or discuss the benefits of green infrastructure. The plans lack information critical for planning a green infrastructure network and rely on a narrow set of policies. Based on the prevalence of strategies to protect open space, green infrastructure is valued by communities even if they do not use the term "green infrastructure." Ultimately, the results suggest that there is a critical need to build planning capacity to advance green infrastructure implementation. [ABSTRACT FROM AUTHOR]

This paper presents an approach to capacity planning and coordination for the regeneration of complex investment goods. In order to capture the specifics of the regeneration environment, Bayesian networks are utilised to improve the accuracy of the workload forecast and mathematical models are proposed for the long-, medium- and short-term capacity planning and coordination. The long- and medium-term models maximise the total profit through the optimum determination of the quantity of goods to be regenerated in-house or at the sites of external vendors, the number of regenerated goods to be stored and the extent to which penalties should be tolerated for delayed regeneration orders. The short-term model determines the optimal allocation of human and machine resources to different orders. The models are validated through the use of real-world data supplied by an industrial partner. Sensitivity analyses are conducted in order to gain insights into the model. The application of the models leads to significant improvements for the industry partner due to the reduction in regeneration costs as a result of implementing varying plant capacities over the course of a year. [ABSTRACT FROM AUTHOR]

An outbreak of a novel coronaviruswas first reported in China on 31 December 2019.As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales. We used 2011 census data to inform population sizes and movements, together with parameter estimates from the outbreak in China. We predict that the epidemic will peak 126 to 147 days (approx. 4 months) after the start of person-to-person transmission in the absence of controls. Assuming biological parameters remain unchanged and transmission persists from February, we expect the peak to occur in June. Starting location and model stochasticity have a minimal impact on peak timing. However, realistic parameter uncertainty leads to peak time estimates ranging from 78 to 241 days following sustained transmission. Seasonal changes in transmission rate can substantially impact the timing and size of the epidemic. We provide initial estimates of the epidemic potential of COVID-19. These results can be refined with more precise parameters. Seasonal changes in transmission could shift the timing of the peak into winter, with important implications for healthcare capacity planning. [ABSTRACT FROM AUTHOR]

What humans do when exposed to uncertainty, incomplete information, and a dynamic environment influenced by other agents remains an open scientific challenge with important implications in both science and engineering applications. In these contexts, humans handle social situations by employing elaborate cognitive mechanisms such as theory of mind and risk sensitivity. Here we resort to a novel theoretical model, showing that both mechanisms leverage coordinated behaviors among self-regarding individuals. Particularly, we resort to cumulative prospect theory and level-k recursions to show how biases towards optimism and the capacity of planning ahead significantly increase coordinated, cooperative action. These results suggest that the reason why humans are good at coordination may stem from the fact that we are cognitively biased to do so. Author summary: We propose a new computational model characterizing coordination among self-regarding individuals under theory of mind and risk sensitivity. Theory of mind enables decision-making based on the attribution of beliefs, knowledge, or goals to others, whereas different risk sensitivities allows one to assess the impact of different ways of valuing uncertain returns, as captured by descriptive theories from social-economic studies. Together they provide evidence that biases towards optimism, and the capacity for planning ahead, significantly increase coordinated, cooperative action. [ABSTRACT FROM AUTHOR]

Thelimousine service in luxury hotels is an integral component of the whole customer journey in the hospitality industry. One of the largest hotels in Singapore manages a fleet of both in-house and outsourced vehicles around the clock, serving 9,000 trips per month on average. The need for vehicles may scale up rapidly, especially during special events and festive periods in the country. The excess demand is met by having additional outsourced vehicles on standby, incurring millions of dollars of additional expenses per year for the hotel. Determining the required number of limousines by hour of the day is a challenging service capacity planning problem. In this paper, a recent transformational journey to manage this problem for the hotel is introduced, having driven up to S$3.2 million of savings per year while improving the service level. The approach builds on widely available open-source statistical and spreadsheet optimization tools, along with robotic process automation, to optimize the schedule of the hotel's fleet of limousines and drivers and to support decision making for planners and controllers to cultivate sustained business value. [ABSTRACT FROM AUTHOR]

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Resilient communities are less affected by, and recover faster from, natural disasters. To be resilient in rapidly changing contemporary environments subject to the effects of complex factors such as climate change and urbanization, communities must effectively and efficiently adapt to new conditions to minimize future risks. To develop resilience, the hazards to which the community is exposed and vulnerable (i.e., future hurricanes, subsidence, salt water intrusion) must be accurately assessed, the systems (i.e., natural, built, and social) must be well understood, and the community must be engaged in the proactive planning and priority setting process. An approach to building resilience that utilizes the adaptive capacity of planning highlights opportunities to work collaboratively across disciplines to incorporate models and data from different disciplines to reduce uncertainty. We present one interdisciplinary group's approach to addressing challenges to building resilience through proactive planning, including: (1) characterizing hazards more accurately; (2) improving understanding of the vulnerability of natural (e.g., climate and infrastructure) systems subject to hazards; and (3) capturing potential synergies from interactions between planning and policies that govern decisions about the design of human settlements in hazardous areas. [ABSTRACT FROM AUTHOR]

The problem of product and process planning analysed so far is how we can take advantage of our strategy in planning. Among the principles of manufacturing and service management concepts is that after planning demand, planning transformation is one of the key steps of integrated efficiency; it makes it possible to save costs that are not value adding and are not necessary from the customer's point of view. Currently, the methods of material requirements and capacity planning can be seen as classic solutions that are based on dependency relations between different resources, which can be dynamic in space and time. Measuring and recording capacities raise several problems in addition to the fact that our planning methods are not always satisfying. In the literature, the methods of material requirements planning or manufacturing resource planning (MRP) are not typically optimization methods, so they do not guarantee the best solution, and even if our planning methods were satisfying, several manufacturing restrictions (the time allowed, the complexity of the planning process, the lack of testing opportunities, etc.) could prevent us from reaching satisfying application. It is necessary to create a simple planning algorithm that can give the planner a greater degree of freedom and that would be simple and algorithmic in order to maintain continuous conscious control, putting an end to planning uncertainty and leading us to the best solution under the given conditions. The aim of our research is to introduce a novel, simple planning algorithm, similar to heuristic methods that eliminates the problem of defining the order quantity when applying traditional methods, which prevents us from determining in advance which method is desirable (causing unnecessary planning steps); computer-based solutions hide the causal relations of the methodology from the planner (causing unreliability uncertainty). [ABSTRACT FROM AUTHOR]

Carbon dioxide removal technologies, such as bioenergy with carbon capture and direct air capture, are valuable for stringent climate targets. Previous work has examined implications of carbon removal, primarily bioenergy-based technologies using integrated assessment models, but not investigated the effects of a portfolio of removal options on power systems in detail. Here, we explore impacts of carbon removal technologies on electric sector investments, costs, and emissions using a detailed capacity planning and dispatch model with hourly resolution. We show that adding carbon removal to a mix of low-carbon generation technologies lowers the costs of deep decarbonization. Changes to system costs and investments from including carbon removal are larger as policy ambition increases, reducing the dependence on technologies like advanced nuclear and long-duration storage. Bioenergy with carbon capture is selected for net-zero electric sector emissions targets, but direct air capture deployment increases as biomass supply costs rise. Carbon dioxide removal technologies such as bioenergy with carbon capture and direct air can influence power sector planning and operations. Here the authors show how carbon removal options lower costs of deep decarbonization and alter electric sector investments. [ABSTRACT FROM AUTHOR]

Decentralized regional multienergy system is one of the important development directions of energy and power systems, and researching on the optimization method of multienergy microgrid configuration could provide important support for the investment income guarantee and orderly development of regional multienergy systems. Based on a park-level multienergy microgrid, this paper proposed a multiobjective optimization model for a multienergy microgrid configuration based on the typical scenario set which was constructed by HMM. Besides, based on the actual historical data, the capacity configuration-oriented planning model and component configuration-oriented planning model were analysed and compared under different external environments. The results show that HMM has a good effect on the reduction and extraction of historical scenarios of the system. Compared with the traditional microgrid, the multienergy microgrid has better economic and emission reduction advantages. In addition, the capacity configuration-oriented planning model could reduce the investment cost by up to 62.4% compared with the component configuration-oriented planning model. [ABSTRACT FROM AUTHOR]

Advances in optical data transmission technology have allowed the current expansion of bandwidth-demanding services over the Internet. Also, the emergence of orthogonal frequency-division multiplexing (OFDM) has opened the possibility of increasing the network spectral efficiency by solving the routing, modulation and spectrum assignment (RMSA) problem. Recently, investigators have examined the effects of multiple demands or multiple virtual topologies when they are requested at different time periods over a single physical substrate. That makes the RMSA harder and with many more instances. Such analysis is required because network traffic does not remain static along time, and the demand can increase considerably as new user services arise. Therefore, planning the network considering a multi-period study becomes essential, since it can prevent a case where demands may exceed the bandwidth capacity and cause request blocking in future periods. In this work, we provide a novel mixed integer linear programming (MILP) formulation to solve the RMSA problem for several t periods of demands. This model can be used not only to find the solutions to minimize the used capacity, but also as an efficient method of network planning, since it can estimate with a single formulation and a single iteration the point of resource exhaustion in each period t. The results are found for a small network, and they show the efficiency of the proposed MILP formulation. We also propose an alternative version of this formulation with predefined paths, which is less computationally demanding. The results of this study are compared to a step-by-step planning, where the strategy is a decomposition method that breaks the previous formulation into t steps. Comparing the results of the two strategies, it can be seen that the single multi-period formulation is a good strategy to solve the problem. By contrast, the step-by-step strategy may require reconfigurations and eventual interruptions in the network, from a step to another one. [ABSTRACT FROM AUTHOR]

For an assembly manufacturing system, its capacity planning problem and production control problem are usually considered separately. However, production capacity cannot exert its maximum productivity without employing optimal production control; and optimal production control may not satisfy the demand without sufficient production capacity. Therefore, it is important to jointly optimize capacity planning and production control. First, the mathematical formulation of integrated production control and capacity planning problem for an assembly manufacturing system with multiple upstream production lines and one downstream assembly line is proposed in this paper. Second, we develop a method to obtain the marginal probability distribution of the inventory level in the assembly system, which can be employed to calculate the average production cost under an approximate optimal production control policy. Since optimal control policy is subject to production capacity (i.e., machine number), we employ the integer programming method to optimize the machine number (i.e., production capacity), which consequently influences the control policy. Numerical experiments are conducted to verify the correctness of the method of calculating steady-state probability distribution, and the effectiveness of the algorithm of integrated optimization of production control and production capacity. [ABSTRACT FROM AUTHOR]

This research examines a uniform-price auction mechanism in managing staffing for self-scheduling business such as task sourcing and work-from-home call centers. We consider two types of service providers: Type-1 agents who require advanced notice before a shift starts and Type-2 agents who are flexible enough to be scheduled on-demand. We develop an integrated framework that can jointly analyze demand forecast, short-term scheduling, and long-term planning of staff capacity. We discuss the adoption of a blended workforce in scheduling and the implication of attrition costs in the long-term staffing. In addition, we compare the auction model with a popular fixed-wage model, in order to examine under what conditions the auction model is preferred. These results provide insights to staff managers on the choice of staffing and wage models. [ABSTRACT FROM AUTHOR]

The paper proposes a theoretical framework that integrates the harder aspects of enterprise-wide diagnostic methodology, that is tools and techniques, with softer issues to understand and analyze enterprise-wide issues in the UK manufacturing SME. Enterprise-wide diagnostics application that considers hard and soft practices for managing change in an SME context is less evident in the literature and practice. A case study was conducted in the selected SME and data triangulation was achieved through on-site observations, interviews, and company reports. The use of the integrated framework, influenced by Watson, led to the identification of several gaps between the three levels- enterprise, business, and operations, that were impacting on demand management and capacity planning. The paper demonstrates the benefits of conducting enterprise-wide diagnostic as a first step to enhance better integration between organizational levels and departments when embarking on continuous improvement journey. [ABSTRACT FROM AUTHOR]

The creative cities discourse has long-overlooked the impact of the new creative economy regime on rural areas, often legitimazing arguable urban-biased policies. This paper illustrates how two small towns, in Asia and in Europe, have attempted to build creative settlements, setting up agendas for sustainability transition. This has implied a strategy to reposition the local economy around notions of culture and creativity, deconstructing mainstream pro-growth discourses. It has been also accompanied by the experimentation of forms of engagement of local communities. The aim is to explain the challenges encountered during this process, and to distil, from this experience, the potential factors that might hinder a real process of transition towards sustainability in the long run. It will conclude that employing effective creative-led strategies, to overcome 'smallness' and 'marginality' in a sustainable way, should be based on the strengthening of local planning capacities, and the development of effective network governance arrangements. [ABSTRACT FROM AUTHOR]

Purpose: COVID-19 patients requiring mechanical ventilation can overwhelm existing bed capacity. We aimed to better understand the factors that influence the trajectory of tracheostomy care in this population to facilitate capacity planning and improve outcomes. Methods: We conducted an observational cohort study of patients in a high-volume centre in the worst-affected region of the UK including all patients that underwent tracheostomy for COVID-19 pneumonitis ventilatory wean from 1st March 2020 to 10th May 2020. The primary outcome was time from insertion to decannulation. The analysis utilised Cox regression to account for patients that are still progressing through their tracheostomy pathway. Results: At the point of analysis, a median 21 days (IQR 15–28) post-tracheostomy and 39 days (IQR 32–45) post-intubation, 35/69 (57.4%) patients had been decannulated a median of 17 days (IQR 12-20.5) post-insertion. The overall median age was 55 (IQR 48–61) with a male-to-female ratio of 2:1. In Cox regression analysis, FiO2 at tracheostomy ≥ 0.4 (HR 1.80; 95% CI 0.89–3.60; p = 0.048) and last pre-tracheostomy peak cough flow (HR 2.27; 95% CI 1.78–4.45; p = 0.001) were independent variables associated with prolonged time to decannulation. Conclusion: Higher FiO2 at tracheostomy and higher pre-tracheostomy peak cough flow are associated with increased delay in COVID-19 tracheostomy patient decannulation. These finding comprise the most comprehensive report of COVID-19 tracheostomy decannulation to date and will assist service planning for future peaks of this pandemic. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach to quantify the impact of forward propagated demand and weather uncertainty on power system planning and operation models. Recent studies indicate that such sampling uncertainty, originating from demand and weather time series inputs, should not be ignored. However, established uncertainty quantification approaches fail in this context due to the data and computing resources required for standard Monte Carlo analysis with disjoint samples. The method introduced here uses an m out of n bootstrap with shorter time series than the original, enhancing computational efficiency and avoiding the need for any additional data. It both quantifies output uncertainty and determines the sample length required for desired confidence levels. Simulations and validation exercises are performed on two capacity expansion planning models and one unit commitment and economic dispatch model. A diagnostic for the validity of estimated uncertainty bounds is discussed. The models, data and code are made available. [ABSTRACT FROM AUTHOR]

Pumped storage technology plays a crucial role in achieving balance in power systems and enhancing the stability of energy systems. Scientific planning can help optimize the operation of power systems, promote the development of renewable energy, and conserve energy. This paper addresses the capacity planning problem of pumped storage stations in hybrid operation systems considering wind power uncertainty. A comprehensive decision-making method is proposed, based on the Delphi method, interval intuitionistic fuzzy theory, grey relational theory, entropy weight method, and prospect theory. This method comprehensively evaluates decision alternatives by considering various factors such as the uncertainty of qualitative indicators, the interrelationships between indicators, and the impacts of different risk environments. The test system in this paper is conducted on a hybrid wind-thermal-pump storage output model based on the IEEE 30-bus system. The optimal capacity planning scheme determined by the proposed method is 190 MW when decision-makers are risk-neutral or risk-averse, and 200 MW when they are risk-appetite. Subsequently, the case study verifies the impact of different load levels and proportions of wind power access on the optimal decision-making scheme, and compares it with decision-making method such as principal component analysis, technique for order preference by similarity to an ideal solution, and a combined method of Pythagorean fuzzy theory and cumulative prospect theory, validating feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

• We consider both the direct and traditional canvassing channels of the carrier. • We study empty container planning and channel coordination in the DCCTSC. • We introduce the option contract into maritime supply chain. • We design a revenue-sharing option contract to resolve conflicts in the DCCTSC. • We broaden the application scenarios of supply chain contracts. This paper examines empty container capacity planning and channel coordination in a dual-channel container transportation service chain (DCCTSC) in the context of random market demand. We first develop a benchmark model that does not take into account the option contract. Then, we introduce the option contract between the forwarder and the carrier and establish empty container leasing models in the decentralised and centralised modes. Next, we introduce an improved revenue-sharing contract based on the model constructed above to investigate the coordination of the revenue-sharing option contract in the DCCTSC. We obtain the relational equation for the contract parameters that is satisfied when the system is coordinated and analyse the Pareto improvement interval for the contract parameters that enable the carrier and the forwarder to attain a mutually beneficial outcome. Finally, the conclusion is confirmed by numerical simulation. The outcomes indicate that the option contract is effective in reducing the risk to the forwarder caused by market uncertainty, leading to an approximately 16-fold increase in its profit. The revenue-sharing option contract can reasonably distribute the system's profits, resulting in a win–win outcome for both the carrier and the forwarder, increasing the total system profit by up to 6.45%. [ABSTRACT FROM AUTHOR]

The widespread deployment of residential distributed photovoltaic (RDPV) remains complex and challenging due to photovoltaic output intermittency, fluctuating electricity demand, and rising electric vehicle (EV) adoption. Simultaneously, the energy storage capabilities of EVs and residential demand response (DR) offer solutions for optimizing RDPV applications. This study proposes an integrated RDPV capacity planning model by encompassing EV charging, vehicle-to-home, and flexible load DR. Five scenarios are established to reveal the impact of various factors on the optimal photovoltaic installation capacity, electricity cost, self-consumption and self-sufficiency rate. A case study of three typical residential electricity demand patterns indicates that DR and vehicle-to-home significantly reduce the optimal photovoltaic installation capacity and total electricity cost. When the feed-in tariff during photovoltaic generation periods is higher than the off-peak pricing, DR results in a reduction in photovoltaic self-sufficiency rate and an increase in photovoltaic self-consumption rate. EV charging and vehicle-to-home have minimal impact on photovoltaic self-consumption rate, while EV charging significantly decreases self-sufficiency rate and vehicle-to-home exacerbates this effect. [Display omitted] • Proposing a residential distributed PV capacity planning model. • Assessing the impacts of electric vehicles and demand response on PV installations. • Demand response and vehicle-to-home reduce the optimal PV installation capacity. • EV charging and vehicle-to-home have a minimal impact on PV self-consumption rate. [ABSTRACT FROM AUTHOR]