Flexible Service Research Articles

Dynamic Resource Allocation for Virtual Network Function Placement in Satellite Edge Clouds

Satellite edge computing has become a promising way to provide computing services for Internet of Things (IoT) users in remote areas, which are out of the coverage of terrestrial networks. Nevertheless, it is not suitable for large-scale IoT users due to the resource limitation of satellites. Cloud computing can provide sufficient available resources for IoT users, but it does not meet delay-sensitive services as high network latency. Satellite edge clouds can facilitate flexible service provisioning for numerous IoT users by incorporating the advantages of edge computing and cloud computing. In this paper, we investigate the dynamic resource allocation problem for virtual network function (VNF) placement in satellite edge clouds. The aim is to minimize the network bandwidth cost and the service end-to-end delay jointly. We formulate the VNF placement problem as an integer non-linear programming problem and then propose a distributed VNF placement (D-VNFP) algorithm to address it. The experiments are conducted to evaluate the performance of the proposed D-VNFP algorithm, where Viterbi and Game theory are considered as the baseline algorithms. The results show that the proposed D-VNFP algorithm is effective and efficient for solving the VNF placement problem in satellite edge clouds.

STOCKPILE MANAGEMENT THROUGH THE EVERYDAY OPERATION OF A PHARMACEUTICAL COMPANY

In this article, the stock management activities and stock levels of a pharmaceutical wholesaler will be analysed. Stocks play a significant role in both material flow processes and the supply chain. If the quantity of stocks is not adequate, stockout or overstocking may occur. Companies can take various measures to ensure uninterrupted supply, and also prevent shortages and stockouts. Therefore, the objective is to reach an optimal level of stock. The aim of the present study is to find an answer to how stockouts may be reduced or minimized in the future, identify which aspects of stock management activity play a major role in minimizing shortages, as well as analyse strategically important decisions pertaining to stockpiling. Another issue is to find out why it is crucial for us to know our strategic product groups, why it is important to conduct a more pronounced measuring regarding stocks of these products, and reduce the shortages to zero. The competitive situation of the market allows for a company to retain its customers for the long term, and to this end, providing quick and flexible service and ensuring the availability of products are of key importance. Today, immediate availability is particularly important. Consequently, time is a crucial factor as people have high expectations and do not tolerate long waiting times, thus inventory and inventory management are especially important where deficiencies are not allowed. As the central topic of the current paper stock levels at various sites of a wholesaler have been examined focusing on the product scopes with low stockpiles on the basis of the calculated turnover rate. Generally, stock analysis methods that would result in the successful decrease of shortages were sought after. The analysis pertained to the context of turnovers and stock levels, seeking parallels between stock levels as well as the size and spatial distribution of the customer base . Corporate inventory management and demand forecasting are two interrelated areas of management that can directly and significantly affect the efficiency and economy of operations What is more, an adequate stock level can signal a competitive supply chain.

The Strategies for Increasing Grid-Integrated Share of Renewable Energy with Energy Storage and Existing Coal Fired Power Generation in China

The growing share of renewable energies needs more flexible services to balance their intermittency and variance. The existing coal fired units and electrical energy storage (EES) systems may play an important role in delivering flexible services. The value of their flexibility services, along with the value of renewable energies, has to be analyzed from the perspective of the power system, in which the capacity costs and operation costs of renewable energy power units, EES systems, and thermal power generation units have to be taken into consideration. An optimal model is built to analyze the renewable energy integration and the flexibility services delivered by the EES systems and thermal power units in a power system. Taking the existing thermal power units and EES systems in North China Power Grid as an instance, the overall cost of the grid is examined for the penetration of renewable energies and flexible service provision. The results show that the growing shares of renewable energies are affected by their capacity credits and flexibility sources in the grid, and that the potential of thermal power units to provide flexible services will be reduced due to the replacement of renewable energies for thermal power generation. The results also indicate that the thermal units may be dispatched to have priority to delivering flexible services for the renewable energy integration, and that the curtailment of renewable energies may be regarded as one type of flexible service. According to these results, policy and strategy recommendations are put forward to weigh the role of existing coal-fired units and EES systems in providing flexible services, and to improve their compensation mechanism and their coordination.

Minimal impact of anticoagulant on in vitro whole blood quality throughout a 35-day cold-storage regardless of leukoreduction timing.

There is increasing interest in leukoreduced whole blood (WB) as a transfusion product for trauma patients. In some jurisdictions, few leukoreduced filters are approved or appropriate for WB leukoreduction and quality information is therefore limited. This study assessed the impact of filtration timing of WB collected in CPDA-1 versus CPD on in vitro quality. WB was collected in CPDA-1 or CPD and leukoreduction filtered either after 3-8h (early) or 18-24 h (late) from stop bleed time. In vitro quality was assessed after filtration and throughout 5 weeks of storage at 4°C. Cell count and hemoglobin levels were determined by hematology analyzer, platelet activation and responsiveness to ADP by surface expression of P-selectin by flow cytometry, hemolysis by HemoCue, and metabolic parameters by blood gas analyzer. Hemostatic properties were assessed by rotational thromboelastometry. Plasma protein activities and clotting times were determined by automated coagulation. Although there were some data points which showed statistically significant differences associated with anticoagulant choices or the filtration timing, no general trend in inferiority/performance could be discerned. After 35 days' storage, only clotting time, alpha angle and factor II in the early filtration arm comparing anticoagulants and prothrombin time and factor II in the CPDA-1 study arm comparing filtration timing showed a significant difference. In vitro WB quality seems to be independent on the choice of anticoagulant and filtration timing supporting WB hold-times to up to 24 h, increasing operational flexibility for transfusion services.

Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers

Price guidance in the flexible energy electricity market is not mature. The imbalance between supply and demand in current electricity markets is mainly caused by uncontrolled flexible energy generation; the electricity supply with load changes in the traditional electricity market is no longer applicable to the current flexible energy electricity market. The work proposes neural network dynamic differential control as the long-term price guidance mechanism for flexible energy service providers. This mechanism alleviates the imbalance between supply and demand in the future electricity market. The neural network dynamic differential control method combines the long-short term memory network and the stochastic dynamic differential method. The proposed method integrates the effects of environmental temperature, holidays, and government regulation on demand. This mechanism enables the demand of flexible energy customers to equal the generation capacity of flexible energy, customers to minimize costs in a long-term electricity market and flexible energy to be completely absorbed. This mechanism directly controls the electrical energy demand and indirectly controls the indoor temperature of the building cooling model through price guidance signals. The experimental results show that the building cooling model can save 4.84% of the cost and 65.03% of the potential cost savings.

Retrofitting Buildings into Thermal Batteries for Demand-Side Flexibility and Thermal Safety during Power Outages in Winter

Decarbonizing heating in buildings is a key part of climate change mitigation policies, but deep retrofit is progressing slowly, e.g., at a pace of 0.2%/y of the building stock in Europe. By means of tests in two flats of a multiapartment housing complex recently renovated to very low values of energy needs, this paper explores the role of deep retrofitted buildings in providing energy flexibility services for the occupants/owners/managers and for the energy system. Key to this flexibility increase and capacity savings is the large reduction of energy needs for heating via a high level of external insulation, which allows the thermal capacity of the building mass to act as an energy storage, without the large energy losses presently affecting a large part of the building stock. Due to the limited number of case studies reporting experimental applications in real buildings, this research aims to offer an analysis based on a series of tests and detailed monitoring which show a significant increase in the time interval during which the low-energy-needs building remains in the comfort range, compared to a high-energy-needs building, when active delivery of energy is deactivated during the heating season. Intermittent renewable energy might hence be stored when available, thus enhancing the ability of the energy system to manage inherent variability of some renewable energy sources and/or increasing the share of the self-consumption of locally generated RES energy. Besides, two unplanned heating power outages which have involved the entire building complex allowed us to verify that deep retrofitted buildings are able to maintain thermally safe indoor conditions under extreme events, such as a power outage, for at least 5 days.

Toward flexible and persistent UAV service: Multi-period and multi-objective system design with task assignment for disaster management

Usability of lightweight unmanned aerial vehicles (UAVs) limited in a variety of activities in disaster management due to two fundamental drawbacks: limited flight duration, and limited loading capacity. With the proper system design and task assignment methodologies, these limitations can be overcome to enable long-duration UAV missions over large areas. The present study set out to determine the optimal unmanned aerial system (UAS) system design and UAV task assignment for such applications. Based on a proposed multi-objective mathematical model, exact Pareto optimal solutions were derived using the ε–constraint method. To find approximate Pareto solutions, an approximate two-phase (ATP) approach was developed, which is computationally inexpensive compare to the optimal solution approach. Through numerical experiments based on a realistic case study, it was shown that the ATP approach results in high accuracy and a two-orders-of-magnitude reduction in computation time. Additionally, for prompt application of our study to continuously changing situations, a rolling-horizon UAS approach herein is suggested.

A dynamic scheduling process and methodology using route deviations and synchronized passenger transfers for flexible feeder transit services

The general lack of first/last mile connectivity is one of the main challenges encountered by intercity transport systems. New transportation alternatives called flexible transit systems, which combine on-demand service adjustment capabilities to the regular route and master schedule characteristics of the conventional transit, are therefore increasingly proposed. Feeder transit services can run as one type of flexible transit services, connecting a service area to a major intercity transport network through a transfer hub. Considering the unstable and constantly changing characteristics of daily passenger demand, a dynamic scheduling methodology for feeder transit services is proposed to minimize passenger crowding in vehicles. Deviations in the base route are allowed for accommodating optional requests or visiting potential transfer locations, which further offer a new type of flexible scheduling operation called synchronized passenger transfer. Operators can dynamically expand the spatial coverage of vehicles out of their regular routes to meet each other at appointed locations and times. Then, passengers can transfer between vehicles to minimize or even avoid passenger crowding. In this study, a multi-commodity network flow optimization model with side constraints in a three-dimensional space–time–state network is proposed for this type of dynamic scheduling problem. Additionally, a coordinate descent-based solution framework consisting of three steps (augmentation, decomposition, and linearization) is developed. The proposed model and solution approach are investigated using an eight-node simple network and the Sioux-Falls network, and the Chicago sketch network was adopted to demonstrate the practicability of the proposed model.

Day-ahead self-scheduling from risk-averse microgrid operators to provide reserves and flexible ramping ancillary services

In this paper, a new decision-making framework is proposed for the day-ahead self-scheduling problem of microgrids, in which the microgrid operator (MGO) can provide ancillary services for both the independent system operator (ISO) and the distribution system operator (DSO). The MGO provides the reserve and flexible ramping product (FRP) services for the ISO through participating in the corresponding markets. Also, the MGO reschedules its resources to provide the requested services for the DSO. To model the uncertain behavior of the renewable energy resource, demand, and real-time energy price, the MGO problem is modeled as a two-stage stochastic model. Then, the uncertainties of the reserve and the FRP deployment in real-time operation are modeled using the information gap decision theory approach. The results show the effects of the different strategies in scheduling the local resources adopted by the MGO to participate in the energy and ancillary service markets. In the risk-based model proposed for the MGO, increasing the risk parameter decreases the capacity of the provided reserve and ramp-up FRP while it increases the energy sold to the day-ahead energy market.

Optimization-based modelling and game-theoretic framework for techno-economic analysis of demand-side flexibility: A real case study

This paper proposes a two-step framework for techno-economic analysis of a demand-side flexibility service in distribution networks. Step one applies optimization-based modelling to propose a generic problem formulation which determines the offer curve, in terms of available flexible capacity and its marginal cost, for flexible distribution-connected assets. These offer curves form an input to the second step, which uses a multi-agent iterative game framework to determine the benefits of demand-side flexibility for the Distribution System Operator (DSO) and the service providers. The combined two-step framework simultaneously accounts for the objectives of each flexibility provider, technical constraints of flexible assets, customer preferences, market clearing mechanisms, and strategic bidding by service providers, omission of any of which can lead to erroneous results. The proposed two-step framework has been applied to a real case study in the North East of England to examine four market mechanisms and three bidding strategies. The results showed that among all considered market mechanisms, flexibility markets that operate under discriminatory pricing, such as pay-as-bid and Dutch reverse auctions, are prone to manipulations, especially in the lack of competition. In contrast, uniform pricing pay-as-cleared auction provides limited opportunities for manipulation even when competition is low.

Shippers’ willingness to use flexible transportation services

Factors driving the choice of shipper firms for services of logistics service providers have long been recognized in the freight transportation literature. However, the willingness among shippers to choose flexible transportation services, where the service package can be adapted during planning and execution, has received less attention. In particular, little is known about the contextual circumstances under which shippers would be inclined to select such flexible transportation service. In this study, experimental scenarios and discrete choice modeling are used to investigate the willingness among shippers to use flexible transportation services. We estimate multinomial logit, mixed logit, and latent class models for a sample of nearly 200 global shipper firms and calculate willingness-to-pay measures for flexibility. The findings indicate that flexible services are essential in demand-volatile markets. Since logistics services may provide external flexibility for shipper firms, we also study which related internal flexibilities in supply chains drive these choices. In particular, our findings show that it is mainly the volume flexibility of shippers that mediates the choice of flexible transportation services.

Cloud–Edge Collaborative SFC Mapping for Industrial IoT Using Deep Reinforcement Learning

The industrial Internet of Things (IIoT) and 5G have been served as the key elements to support the reliable and efficient operation of Industry 4.0. By integrating burgeoning network function virtualization (NFV) technology with cloud computing and mobile edge computing, an NFV-enabled cloud–edge collaborative IIoT architecture can efficiently provide flexible service for the massive IIoT traffic in the form of a service function chain (SFC). However, the efficient cloud–edge collaboration, the reasonable comprehensive resource consumption, and different quality of services are still key problems to be solved. Thus, to balance the quality of IIoT services, as well as computational and communicational resource consumption, a multiobjective SFC deployment model is designed to characterize the diverse service requirements and specific network environment for the IIoT. Then, a deep- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning-based online SFC deployment algorithm is presented, which can efficiently learn the relationship between the SFC deployment scheme and its performance through the iterative training. Simulation results demonstrate that our proposed approach outperforms others in balancing the resource consumption, accepting more SFC requests, as well as providing differentiated services for delay-sensitive IIoT traffic and resource-intensive IIoT traffic.

Cognitive intelligence based 6G distributed network architecture

5G is envisioned to guarantee high transmission rate, ultra-low latency, high reliability and massive connections. To satisfy the above requirements, the 5G architecture is designed with the properties of using service-based architecture, cloud-native oriented, adopting IT-based API interfaces and introduction of the Network Repository Function. However, with the wide commercialization of 5G network and the exploration towards 6G, the 5G architecture exposes the disadvantages of high architecture complexity, difficult inter-interface communication, low cognitive capability, bad instantaneity, and deficient intelligence. To overcome these limitations, this paper investigates 6G network architecture, and proposes a cognitive intelligence based distributed 6G network architecture. This architecture consists of a physical network layer and an intelligent decision layer. The two layers coordinate through flexible service interfaces, supporting function decoupling and joint evolution of intelligence services and network services. With the above design, the proposed 6G architecture can be updated autonomously to deal with the future unpredicted complex services.

An interactive two-stage retail electricity market for microgrids with peer-to-peer flexibility trading

To accommodate the proliferation of microgrids (MGs) that manage various distributed energy resources (DERs) to ensure secure internal operations, an interactive two-stage retail electricity market based on the transactive energy is proposed to provide an effective platform for these prosumers to participate in distribution-side retail transactions. Firstly, given the unpredictability of uncertainty, the centralized day-ahead stochastic energy trading between the distribution system operator (DSO) and MGs is performed, where the chance-constrained uncertainty distribution locational marginal price (CC-UDLMP) that quantifies the cost of uncertainty precautions is used to settle the transactions. Secondly, in the intra-day stage, to eliminate power imbalances from rolling estimates by exploiting the adjustability capacity of the DERs within the MGs, the peer-to-peer (P2P) based flexibility service sharing is implemented between MGs under the system-level regulation of DSO. Thirdly, a fully distributed iterative algorithm is adopted to find the Nash equilibrium solution of this two-stage sequential game under the market machanism, where the Lp-box alternating direction methods of multipliers algorithm (Lp-box ADMM) is adopted to efficiently resolve the stochastic market clearing with a mixed-integer programming structure to improve the versatility of this work. It is verified that the proposed market mechanism can effectively leverage the flexibility potentials of the MGs and improve the overall market efficiency under uncertain environments.

Harnessing Organizational Leadership and Cultural Competence to Attract and Retain Quality Teachers: The Case of Inner-City Schools

Strategic investments in organizational leadership within the education sector can reap significant dividends in attracting and retaining quality teachers. These investments include comprehensive cultural competency training for all school administrators, teachers, and staff as well as the inclusion of these groups in creating a collaborative organizational culture. Such investments are particularly important in inner-city schools, where research reveals that cultural incompetence and the absence of a collaboratively developed organizational plan contribute to high levels of teacher attrition and substantial difficulties in attracting new teachers. Because scholarship has already recognized and identified the problem of attracting talented teachers to low-income and urban districts, this article takes the next step to suggest novel evidence-based solutions to the problem. Central to this approach is the school administrator’s responsibility to build an organizational culture that ensures equity, educational rigor, and opportunities for meeting teachers’ and administrators’ work-related needs. Building this culture requires (a) delivery of empirically supported cultural competency training; (b) self-assessments of management style to create an approach designed to enhance employee satisfaction; (c) creation of a shared vision and organizational leadership plan designed to make the work environment more attractive and satisfying to current and prospective teachers; and (d) development of a flexible yet practical service delivery framework that enables employees’ workplace needs to be met. In creating this culture, we argue that inner-city teacher attraction and retention rates can be dramatically improved.

Synchromodal transport planning with flexible services: Mathematical model and heuristic algorithm

As a critical feature of synchromodal transport (ST), service flexibility plays an important role in improving the utilization of resources to reduce costs, emissions, congestions, and delays. However, none of the existing studies considered flexible services under the framework of synchromodality. This paper develops a Mixed Integer Linear Programming (MILP) model to formulate service flexibility in ST planning. In the MILP model, vehicles with flexible services as well as fixed services are both considered, and vehicle routes and request routes are planned simultaneously. Due to the computational complexity, an Adaptive Large Neighborhood Search heuristic is designed to solve the problem. Several customized operators are designed based on the characteristics of the studied problem. The proposed model is compared with the models developed in a highly-cited paper and a newly published paper that do not consider service flexibility. Case studies on small instances verified that the proposed model with flexibility performs better on all scenarios, including scenarios with different weights for the individual objectives, scenarios under congestion,and dynamic optimization scenarios. On large instances (up to 1600 shipment requests), the proposed model with flexibility reduces the cost by 14% on average compared with the existing models in the literature.

Day‐ahead flexibility market clearing mechanism for interactive collaboration of transmission and distribution system operators

This paper explores the procurement of flexibility services in the power system by the collaboration between transmission system operators (TSO) and distribution system operators (DSO) in the day-ahead market (DAM). With the coordination and interaction among the operators, the existing flexibility sources in transmission and distribution levels are exploited efficiently to procure flexibility services. For motivation of the distributed generation (DGs) sources and microgrids (MGs) to provide flexibility services, DSO aggregates the flexible sources of its grid and offers them to the upstream market. Doing so addresses the TSO uncertainties and improves the flexibility of the entire grid. The simulation results suggest the increased market efficiency and reduced operating costs for transmission and distribution levels using flexible sources. Furthermore, the results demonstrate that utilization of existing flexible sources in the distribution grid enhances the flexibility of the entire system and, consequently, increases the absorption from renewable energy sources (RES). Also, the flexibility procurement for DSO is associated with an increase in revenue and motivation of MGs. An examination of the impact of MG's ownership on the market clearing mechanism is also investigated in this regard. Finally, the reserve affects the system's flexibility. This effect is helpful for flexible systems while reducing the flexibility metrics for inflexible systems.

Case Study in Youth Flexible Assertive Community Treatment: An Illustration of the Need for Integrated Care.

Youth Flexible Assertive Community Treatment (Youth Flexible ACT) is a client- and family-centered service delivery model for young people up to 24 years of age who have interrelated psychiatric- and social problems across multiple life domains and do not readily engage with office-based mental health services. Youth Flexible ACT teams were set up to meet the multifaceted needs of this subgroup in an integrated manner. In this paper, we present a case study to (1) describe the core principles of Youth Flexible ACT and (2) illustrate the application of the mental healthcare model. Subsequently, we describe the contribution of model elements to a positive care process and discuss the challenges of the team in working with the model. The case study displays the importance of integrated flexible and personalized care services to enable adolescents to remain engaged in care.

Power System Stability Analysis of the Sicilian Network in the 2050 OSMOSE Project Scenario

This paper summarizes the results of a power system stability analysis realized for the EU project OSMOSE. The case study is the electrical network of Sicily, one of the two main islands of Italy, in a scenario forecasted for 2050, with a large penetration of renewable generation. The objective is to establish if angle and voltage stabilities can be guaranteed despite the loss of the inertia and the regulation services provided today by traditional thermal power plants. To replace these resources, new flexibility services, potentially provided by renewable energy power plants, battery energy storage systems, and flexible loads, are taken into account. A highly detailed dynamical model of the electrical grid, provided by the same transmission system operator who manages the system, is modified to fit with the 2050 scenario and integrated with the models of the mentioned flexibility services. Thanks to this dynamic model, an extensive simulation analysis on large and small perturbation angle stability and voltage stability is carried out. Results show that stability can be guaranteed, but the use of a suitable combination of the new flexibility services is mandatory.

The need for flexibility in forest harvesting services – a case study on contractors’ workflow variations

ABSTRACT In many parts of the world, contractors account for the main share of harvesting work. Harvesting is characterized by innate complexity and volatility, and this can affect contractors’ workflow and ultimately their profitability. Thus, there is certainly a need for flexibility in harvesting service provision and procedures, but our current knowledge about contractors’ workflow variations are limited. This study investigates workflow variations in harvesting services by comparing monthly variations between contractors’ workload in terms of harvested volumes and the time spent on operations. The data originates from 77 machines belonging to contractors and their harvesting of 6.6 million m3 of roundwood in Sweden during a two-year period. The results indicate differences between contractors’ workflow variations which can be attributed to the number of machines, machine sizes, and the workload in harvested volume and hours. These findings are relevant for guiding both the customer and contractor in this business relationship, and they could also serve as a basis for further research on the need for flexibility to effectively increase and decrease volume production in harvesting services.