Flexible Capacity Research Articles

Cloud-edge collaboration composition and scheduling for flexible manufacturing service with a multi-population co-evolutionary algorithm

The Cloud Manufacturing Service Composition and Scheduling (CMfg-SCS) are essential processes in cloud manufacturing. Flexible Manufacturing Services (FMS), such as those provided by industrial robots, are widely used in cloud manufacturing to improve service quality and efficiency. Traditional CMfg-SCS methodologies, however, fall short in effectively managing the inherent temporal-dynamic QoS and flexible capability of FMS. To overcome these challenges, we propose a novel Cloud Manufacturing Service Cloud-edge Collaboration Composition and Scheduling (CMfg-SCCCS) method for FMS. Firstly, the service-task matching hypernetwork is constructed, and the temporal-dynamic QoS and flexible capacity of FMS are modeled. Subsequently, we develop a CMfg-SCCCS optimization model aimed at three objectives, along with a cloud-edge collaboration scheduling mechanism to harmonize cloud and edge-local tasks. Finally, a multi-population co-evolution algorithm with adaptive meta-knowledge transfer mechanism is proposed to solve the complex optimization model. The computational experiments serve to validate the effectiveness of the CMfg-SCCCS method and further reveal the superiority of the co-evolution algorithm in enhancing both the convergence and diversity of the population.

Extending ConWIP by flexible capacity and WIP-Cap adjustment for a make-to-order multi-item multi-stage production system

Extending ConWIP by flexible capacity and WIP-Cap adjustment for a make-to-order multi-item multi-stage production system

Aggregation of building predictive energy flexibility in smart microgrid

Numerous demand-side resources possess significant potential to offer substantial flexible reserves and ancillary services, thereby enhancing the economic and technical performance of power systems within the demand response domain. A novel approach to aggregating the energy flexibility of buildings based on the virtual battery model is proposed in the smart microgrid, employing predictive control techniques while ensuring consumer privacy. The individual approximation process is implemented to extract the flexible capacity of local resources while protecting information privacy. Subsequently, the aggregator collects the flexibility information to develop an optimization strategy for maximum flexibility aggregation at the upper level, enabling the broader exploitation of global flexibility. The feasibility of power disaggregation is guaranteed according to the aggregation procedure. Furthermore, the comprehensive framework accommodates temporal preferences for flexibility requests from the aggregator. Numerical simulations are conducted to perform a comparative analysis with existing state-of-the-art methods. The results demonstrate that the proposed approach exhibits improved adaptability in characterizing the aggregate flexibility from buildings, effectively managing the heterogeneity of load characteristics and the variability in the collected flexibility information. Microgrids can realize cost savings and achieve dynamic flexibility utilization. This methodology can be extended and applied to diverse types of flexible resources.

Determining capacity flexibility of the urban transit network under different transit operation conditions

This paper proposes the capacity flexibility models of transit networks to describe the ability of urban transit networks to accommodate the changes in passenger demands. In order to keep the variations of the average passenger travel time in a reasonable way, the concept of coefficient of variation (CoV) is introduced to measure and limit the deviation from a baseline time of the average passenger travel time. The CoV could transform the capacity flexibility values to non-dimensional form in order to compare the capacity flexibility characteristics of different scale transit networks on an equal basis. The genetic algorithm with deep search (GA-DS) for this problem is proposed to find the approximating optimal solution of the capacity flexibility models more effectively. The algorithm has been tested with benchmark problems reported in the existing literature. The optimal solution of the flexibility models shows that the flexibility values are further improved by reducing the number of stops for transit routes under different transit operation conditions. At last, the influence factors of the capacity flexibility of transit systems are discussed to address reliable transit services.

The Cost Reduction Potential of Demand Response in Balancing Markets from a System Perspective

Demand response (DR) can potentially provide a cost-efficient alternative for balancing the electricity grid by replacing fossil-fuelled power plants for the provision of flexible capacity. This paper aims to quantify the cost reduction potential of DR from a system perspective. Historical data of balancing markets are studied using regression and average bid price analysis to quantify the effect of the participation of DR resources on the price of flexible capacity for the provision of balancing reserves by focusing on two case studies in Great Britain and the Netherlands. It is estimated that DR bids are, on average, 35% lower than the market average. The regression analysis concluded that 1% higher participation of DR in balancing markets leads, on average, to a 2.7% lower prices for flexible capacity. The results verify the hypothesis that flexible DR capacity is offered at a lower price on balancing markets compared to conventional generation resources, resulting in lower costs for grid operators to balance the grid, thus reducing societal costs for electricity provision and overall emissions through the integration of low-carbon balancing resources.

Research on foldable two-matrix 3D braided composites: Manufacturing and bending progressive damage

Research on foldable two-matrix 3D braided composites: Manufacturing and bending progressive damage

Extended flexible capacity planning methodology with flight network contribution to the airline fleet assignment problem

Flexible capacity planning is crucial for the airline industry due to the high fixed cost of assets, notably aircraft ownership, and the fluctuating demand, particularly in response to external shocks like pandemics and political crises. Following the negative consequences of such events on the financial positions of almost all airline companies, there is growing interest in developing more responsive capacity planning structures for cost efficiency in the airline industry. In this study, we present a sequential two-phase methodology for flexible capacity planning and airline fleet assignment considering the factors related to fleeting strategies, passenger demand behavior, and contribution of flights in the network. The first phase of the methodology generates potential scenarios to estimate the number of flights to the destinations, based on the data and the reports provided by two prominent associations in the aviation industry, which incorporate various country-related and airline-related factors, including population, income, routes, airports, and connectivity. The second phase identifies the required number of aircraft for each type within the fleet inventory, taking into account the aircraft utilization rates associated with each scenario. The fleet assignment problem is also reformulated to employ the objective of maximizing total profit of the entire flight network by introducing network contribution and nation/region-specific limitations in this phase. A real case study from Turkey is conducted to illustrate efficiency of the proposed methodology. We also present a comparative study to highlight the impact of network contribution and how fleet size affects the outcomes. We have observed that the inclusion of the network contribution aspect and country constraints has led to a significant improvement in financial indicators and connectivity. The results show that the methodology can effectively tackle challenges in airline transportation problems.

The Value of Flexible Vaccine Manufacturing Capacity: Value Drivers, Estimation Methods, and Approaches to Value Recognition in Health Technology Assessment.

Expanding flexible vaccine manufacturing capacity (FVMC) for routine vaccines could facilitate more timely access to novel vaccines during future pandemics. Vaccine manufacturing capacity is 'flexible' if it is built on a technology platform that allows rapid adaption to new infectious agents. The added value of routine vaccines produced using a flexible platform for pandemic preparedness is not currently recognised in conventional health technology assessment (HTA) methods. We start by examining the current state of play of incentives for FVMC and exploring the relation between flexible and spare capacity. We then establish the key factors for estimating FVMC and draw from established frameworks to identify relevant value drivers. The role of FVMC as a countermeasure against pandemic risks is deemed an additional value attribute that should be recognised. Next, we address the gap in the vaccine-valuation literature between the conceptual understanding of the value of additional FVMC and the availability of accurate and reliable tools for its estimation to facilitate integration into HTA. Three practical approaches for estimating the value of additional FVMC are discussed: stated and revealed preference studies, macroeconomic modelling, and benefit-cost analysis. Lastly, we review how value recognition of additional FVMC can be realised within the HTA process for routine vaccines manufactured on flexible platforms. We argue that, while the value of additional FVMC is uncertain and further research is needed to help to better estimate it, the value of increased pandemic preparedness is likely to be too large to be ignored.

A Low-Cost 3D SLAM System Integration of Autonomous Exploration Based on Fast-ICP Enhanced LiDAR-Inertial Odometry

Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system’s capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration.

Optimal bus service frequency setting and limited-stop operation with varying vehicle capacity limit

With the advancement of Vehicle to Infrastructure (V2I) technology, it becomes feasible to perceive the diverse needs of various passengers with sensory equipment, offering potential for further enhancing the quality of bus services. Vehicle capacity limit is a pivotal factor for both bus operators and passengers, wherein higher capacities are associated with increased profitability but potentially compromise passenger comfort. The significance of establishing optimal vehicle capacity limits cannot be overstated. Nevertheless, existing researches usually use a fixed vehicle capacity, neglecting the varied needs of passengers at different stations. Notably, the strong demand of elderly and long-distance travelling passengers for seating are frequently ignored. This study introduces a novel framework for determining flexible vehicle capacity limits, which considers the percentage of elderly and long-distance passengers, as informed by station-specific sensor data and historical origin-destination data. Furthermore, it optimizes bus service frequencies to minimize operational costs while enhancing the level of service offered by buses. The quality of public transportation services is improved, addressing the diverse demands of passengers.

Incorporate robust optimization and demand defense for optimal planning of shared rental energy storage in multi-user industrial park

The increasing uncertainty and volatility of net load caused by the high penetration of renewable energy leads to higher demand tariffs for industrial park and potentially impacts their economic benefits. To tackle these issues, this paper develops a novel business mode to enable rental energy storage sharing among multiple users within an industrial park, and propose a robust optimization and demand defense-based iterative bi-layer planning framework. The upper layer focuses on the maximization of the investment profitability of shared rental energy storage by developing a robust information gap decision theory optimization. Meanwhile, the lower layer is dedicated to enhancing the demand defense ability of shared rental energy storage in real-time operation through the formulation of a distributed model predictive control. After that, the synchronous alternating direction multiplier method with consistency theory is derived for solving the distributed optimization. Numerical results demonstrate that the proposed shared rental energy storage is 6.391% and 7.714% more economical than shared and self-built energy storage, respectively. Moreover, the iterative bi-layer planning enables flexible energy storage capacity configuration, reduces the impact of net load uncertainty, improves the ability of demand defense, and enhances the system’s overall economy.

Towards a Greener Future: Scaling Up Agroforestry for Global Sustainability

The current world faces several challenges, including climate change, an exponentially increasing population, the loss of biodiversity and environmental concerns which necessities the adoption of the sustainable nature-based solutions such as agroforestry. Agroforestry is the intricate integration of trees, crops, and livestock on the same piece of land management unit that helps to restore the ecosystem, improve soil health, thus combating the environmental degradation. Moreover, agroforestry has also emerged as a potential tool that bridges the ecological preservation, while increasing or at least maintaining the productivity of the whole agroecosystem. Agroforestry is a contemporary food system that relies primarily on natural inputs, leading to the creation of more sustainable and resilient landscapes. Simultaneously, agroforestry systems reduce the soil erosion, and increases the accumulation of organic matter, hence established a closed nutrient cycling which sustain the agricultural production for longer duration. Simultaneously, agroforestry systems are effective in enhancing food security, diversifying farm income and strengthening the resilience of community to climate change thus empower the farmers and provide an opportunity to combat poverty. However, there are numerous constraints including clear land tenure rights, market infrastructure and linkage, and lack of the traditional knowledge which influenced the adoption of agroforestry systems at global level. This immediately need to be addressed by implementing flexible legislation (such as National Agroforestry Policy 2014 adopted by India), capacity building and community involvement, value chain development and payment for ecosystem services. Overall, it is crucial to adopt low-carbon perennial agroecological practices like agroforestry in order to increase the CO2 abatement rate, improve the food, nutritional and livelihood security of smallholder farmers and contribute to a more environmental friendly and sustainable future.

Design and construction of small-scaled variable modular installation (SVMI) in the context of Chinese street renewal (CSR) : A case study of the "Jingzhang Box" (JZB) service installation in Beijing

China's urban areas are entering an era of stock renewal, where street renewal is receiving policy attention and practical project implementation. However, the challenges posed by spatial constraints and insufficient functional activities on the streets have led to street renewal failing to meet public demands. Small Variable Modular Installations (SVMI) possess a high degree of adaptability to constrained spaces and a flexible capacity to shape diverse functions, making them a targeted strategy in theory to address the challenges of street renewal. Nevertheless, there is a research gap in the context of Chinese street renewal (CSR) concerning the design, and construction of SVMI: an insufficient exploration of combined variable and modular technology solutions and practical construction techniques and methods. In response to this research gap, this study focuses on the "Jing-Zhang Box" (JZB) service installation in Beijing, providing specific analysis of its design concept, design technical points, application functions, spatial structure and connection methods, construction methods, and construction outcomes. This study aims to provide specific supplementation to the research on SVMI design and construction in the context of CSR and to offer theoretical and engineering foundations for the future design and practical construction of SVMI in various scenarios.

Adaptive governance of disaster preparedness? The case of regional networks in Finland

Adaptive governance (AG) has emerged as a theoretical alternative to traditional forms of governance, which often fail to address complexity and rapid changes in socioecological systems. Frequently recognized attributes of AG include learning and experimentation, collaboration and networks, polycentric institutions and multilevel governance at multiple appropriate scales, as well as reflexivity, flexibility and adaptive capacity. To provide more examples of the practical implementation of AG, we examine whether a Finnish regional preparedness network model could be an example of a tangible way to implement AG. According to our results, the regional networks embody many features of AG: they facilitate person- and organization-level networking and peer learning, and thus help their members to grasp the overall picture of preparedness and safety in the region, and reveal ambiguities concerning the division of responsibilities. The networks also support nestedness and polycentricity through cross-sector and multilevel cooperation. At the same time, the voluntary nature of the networks is perceived as problematic considering the overall preparedness of society. Notably, although networks often include non-state actors, citizens remain outside of them, which may reduce the effectiveness of a network.

Enhancing Make-to-Order Manufacturing Agility: When Flexible Capacity Meets Dynamic Pricing

The rise of online marketplaces has raised customer expectations regarding customization and lead time. It poses significant challenges to manufacturing firms and prompts a move from make-to-stock to a more flexible make-to-order system. Compared to make-to-stock settings, make-to-order systems cannot smooth fluctuations in demand using available stock. While viewing dynamic pricing as a useful strategy to balance supply with demand, many manufacturing firms can also create capacity flexibility. In that scenario, system costs could be cut by managing capacity and demand simultaneously. In this paper, we consider a make-to-order production environment with base and surge capacity as well as the ability to adjust product pricing. Our main focus is on operational decision-making, assuming that the base capacity and surge capacity are fixed, but activating the surge capacity incurs a setup cost. Initially, we propose a stochastic control model to reflect this complex decision problem. However, our initial model leads to an intractable dynamic programming problem. To overcome this, we convert the problem to a more tractable diffusion control problem. This approach helps to reveal the conditions under which utilizing flexible capacity is more advantageous than relying solely on fixed capacity. When flexible capacity is advantageous, we provide a solution to the diffusion control problem that can guide optimal capacity and price adjustments. We discover an interesting interplay between capacity adjustment and dynamic pricing. In particular, we find that the price, which aims at reducing congestion, may not monotonically increase with the congestion level when capacity adjustments incur a fixed cost.

Assessing The Supply Chain Risks of an Indonesian Electricity Company

Energy is a crucial necessity for human societies, and access to electrical energy plays a significant role in economic development. A state-owned enterprise (SOE) is entrusted with the provision of electrical energy across Indonesia. The objective of this study is to identify and assess risk factors within the supply chain of primary distribution materials at SOE. The identification of risk factors was based on a comprehensive review of literature across various industrial sectors, further validated through consultations with senior managers at the company. For the assessment of these risks, the study employed the modified Decision Matrix Risk Assessment methodology, which involves evaluating risks based on their likelihood and impact. This evaluation was conducted through surveys distributed to 66 respondents, including planning managers, distribution managers, and commercial managers across all regional and distribution main units in Indonesia. Two risk categories with five sub-categories and a total of 27 risk factors were identified. Supply-related risks are the most significant compared to the other four sub-categories. Scarcity of raw materials, lack of capacity flexibility, and failure to make delivery requirements are in the top three rankings. This research can enrich the supply chain risk literature and provide valuable insights for utility managers in understanding supply chain risks.

Exploring the influence of health system factors on adaptive capacity in diverse hospital teams in Norway: a multiple case study approach

ObjectivesUnderstanding flexibility and adaptive capacities in complex healthcare systems is a cornerstone of resilient healthcare. Health systems provide structures in the form of standards, rules and regulation to healthcare providers...

Cognitive flexibility is associated with sickness resilience.

Psychological constructs related to health outcomes and well-being, such as metacognitive beliefs, have been linked to executive functions in general, and cognitive flexibility more specifically. However, such effects have previously only been discussed on a theoretical level and behavioral flexibility has most often been measured through self-report, only approximating information processing capacities. Objectively measured executive functions may be a more potent predictor of health outcomes. We set out to test whether cognitive flexibility capacity was associated with sick leave in a medium sized company. We included 111 subjects of widely different occupations and assessed their executive functions using Delis-Kaplan Executive Function System test battery (D-KEFS). To assess cognitive flexibility capacity, we included Design Fluency (DF) and Verbal Fluency (VF) and computed these into an index of cognitive flexibility (DFVF). Detailed information on sick leave for the last 5 years was gathered from the company. Our results showed that there was a significant negative correlation between DFVF and sick leave [rs(109) = -0.23, p = 0.015] in the full group as well as in the group that had at least 1 day of sick leave [rs(72) = -0.25, p = 0.03]. The results withstood adjustment for sex, age, occupation, and several core executive functions as well as autistic and ADHD-traits. The results remained for separate analyses using DF or VF. Our main findings were conceptually replicated in a group of bipolar disorder patients. This study shows that objectively measured capacity of cognitive flexibility is associated with key health outcomes such as sick leave.

Jump-Starting Global Grid Decarbonization: US-Dutch Partnerships Energize Utility-Scale Battery Deployment

The United States and the Netherlands heavily invest in renewable energy as part of their individual commitments to net zero emissions by 2050. Weather-dependent fluctuations in wind and solar power, along with limitations in legacy infrastructure and systems, restrict the on-demand use of these renewables and pose substantial bottlenecks to the energy transition. Integration of dynamic energy storage technologies into modernized electrical systems will provide crucial solutions to overcome these challenges. Herein, we profile stakeholder perspectives on the energy transition and present our recommendations for US-Dutch collaboration on utility-scale electrochemical battery solutions to update power grids with robust, accessible, and sustainable carbon-free flexible energy capacity.

Comparative techno-economic evaluation of energy storage technology: A multi-time scales scenario-based study in China

Energy storage technology is a crucial means of addressing the increasing demand for flexibility and renewable energy consumption capacity in power systems. This article evaluates the economic performance of China's energy storage technology in the present and near future by analyzing technical and economic data using the levelized cost method. Through a comparative analysis of different energy storage technologies in various time scale scenarios, we identify diverse economically viable options. Sensitivity analysis reveals the possible impact on economic performance under conditions of near-future technological progress. The application analysis reveals that battery energy storage is the most cost-effective choice for durations of <2 h, while thermal energy storage is competitive for durations of 2.3–8 h. Pumped hydro storage and compressed-air energy storage emerges as the superior options for durations exceeding 8 h. This article provides insights into suitable energy storage technologies for China's energy structure development in the present and near future.