Scheduling Period Research Articles

I'm coming home (to charge): The relation between commuting practices and peak energy demand in the United Kingdom

Decarbonisation plans largely rely on the electrification of energy-intensive sectors such as transport, which has raised both concerns and hopes about the implications for (peak) electricity demand. Particularly so when it comes to the potential impact that private EV charging might have on residential demand patterns. On the one hand, the more pessimistic view suggests that this could substantially increase the demand experienced during peak periods, exacerbating the problems associated with such peaks. On the other hand, the more optimistic view suggests that mass uptake of EVs could offer the opportunity to integrate them as distributed storage units. There is evidence of the fact that synchronisation of practices associated with the use of energy-intensive devices is largely to blame for the occurrence of large peaks in demand; the question of whether this is likely to be the case for EV charging as well remains. This paper adds to the literature on the analysis of the synchronisation of energy-related practices with an in-depth analysis commuting behaviour, using driver commuters as a case study. Cluster analysis is used to identify those commuters with distinctive commuting schedules, and socio-demographic profiling of clusters is carried out with a view to identifying any meaningful correlations that could help target policy interventions. Three characteristic commuting patterns were identified, with clearly distinguishable features in terms of the timing of commuting trips. The analysis of the energy-relevant activities shows that arrival times have a noticeable impact on the scheduling and distribution of periods of engagement in such activities.

Optimizing Operational-Level Forest Biomass Logistic Costs for Storage, Chipping and Transportation through Roadside Drying

Forest biomass (FB) could supply more of Australia’s energy needs, but delivered costs must be reduced for it to be a viable energy source. Operational planning is critical to reducing delivered costs as it determines actual activities, though few operational FB supply chain (FBSC) planning tools have been published. This paper presents a “proof-of-concept” operational FBSC decision support system (DSS) to schedule FB deliveries for eight weeks from roadside storage for the least cost, taking in account moisture content changes. Four mathematical models are compared, solving a linear formulation of the FB delivery problem in terms of solution speed and delivered cost, and the practicality of implementing the solutions. The best performing model was a Greedy algorithm as it produced solutions not significantly different from those of the tested linear programming solver and was readily modified to significantly improve solution implementation through the addition of a non-linear element. FBSC planning tools typically assume accurate knowledge of stored FB quantities and that little or no rainfall occurs during storage. In practice, stored FB quantity estimates can be inaccurate due to variation in the bulk density of the piles. Improving these estimates is a critical area for future research. This study found that simulated rainfall with <20 mm during the first week of the scheduled period did not significantly effect delivered costs.

Long-term Value of Flexibility from Flexible Assets in Building Operation

In this work, we investigate how flexible assets within a residential building influence the long-term impact of operation. We use a measured-peak grid tariff (MPGT) that puts a cost on the highest single-hour peak import over the month. We apply a mathematical model of a Home Energy Management System (HEMS) together with Stochastic Dynamic Programming (SDP), which calculates the long-term impact of operating as a non-linear expected future cost curve (EFCC) from the end of the scheduling period to the start. The proposed model is applied to a case study for a Norwegian building with smart control of a battery energy storage system (BESS), Electric vehicle (EV) charging and space heating (SH). Each of the flexible assets are investigated individually with MPGT and for an energy-based grid tariff. The results showed that EV charging has the highest peak-power impact in the system, decreasing the total electricity cost by 14.6% with MPGT when controllable compared to a reference case with passive charging. It is further shown how the EFCC helps achieve optimal timing and level of the peak demand, where it co-optimizes both real-time pricing and the MPGT.

Timetable merging for the Periodic Event Scheduling Problem

We propose a new mixed integer programming based heuristic for computing new benchmark primal solutions for instances of the PESPlib. The PESPlib is a collection of instances for the Periodic Event Scheduling Problem (PESP), comprising periodic timetabling problems inspired by real-world railway timetabling settings, and attracting several international research teams during the last years. We describe two strategies to merge a set of good periodic timetables. These make use of the instance structure and minimum weight cycle bases, finally leading to restricted mixed integer programming formulations with tighter variable bounds. Implementing this timetable merging approach in a concurrent solver, we improve the objective values of the best known solutions for the smallest and largest PESPlib instances by 1.7 and 4.3 percent, respectively.

Hybrid multi-objective metaheuristic algorithms for solving airline crew rostering problem with qualification and language.

In order to cope with the rapid growth of flights and limited crew members, the rational allocation of crew members is a strategy to greatly alleviate scarcity. However, if there is no appropriate allocation plan, some flights may be canceled because there is no pilot in the scheduling period. In this paper, we solved an airline crew rostering problem (CRP). We model the CRP as an integer programming model with multiple constraints and objectives. In this model, the schedule of pilots takes into account qualification restrictions and language restrictions, while maximizing the fairness and satisfaction of pilots. We propose the design of two hybrid metaheuristic algorithms based on a genetic algorithm, variable neighborhood search algorithm and the Aquila optimizer to face the trade-off between fairness and crew satisfaction. The simulation results show that our approach preserves the fairness of the system and maximizes the fairness at the cost of crew satisfaction.

Pre-Perturbation Operational Strategy Scheduling in Microgrids by Two-Stage Adjustable Robust Optimization

A two-stage adaptive robust optimization is developed for pre-disturbance scheduling in microgrids (MGs) for handling uncertainties associated with electricity market prices, renewable generation, demand forecasts, and islanding events. The objective is to produce a reliable and optimal solution for MG operation that minimizes operational costs and the risk/failure in islanding events. In the literature, the uncertainty sets associated with islanding events cover a full scheduling period which results in a sub-optimal solution. In this paper, uncertainty sets corresponding to islanding events are modeled based on reliability/resiliency-oriented indexes of the MG/grid to achieve a more accurate/reliable solution. Besides, the Benders decomposition algorithm which is used to handle uncertainties in solving the optimization problem is time-consuming. Therefore, the column-and-constraint generation (C&CG) decomposition strategy is adopted to make the problem computationally tractable. Further, the uncertainty budget parameters are clarified to balance the conservatism and optimality (cost minimization) of the robust solution in uncertainty sets. The effectiveness of the proposed framework is evaluated and discussed by using a set of numerical studies with different scenarios in an MG. The simulations show that the proposed framework reduces operational costs by using the precise analysis of uncertainty budgets and a change in scheduling periods.

An ideal postgraduate student in anaesthesia.

An ideal postgraduate student in anaesthesia.

Metaheuristics approaches to profit based unit commitment for GENCOs

The optimum scheduling with maximize the profit from those generating systems have to maintain lots of constraints which are uncertain by nature. There are also have stimulating responsibility of delivered power among the power units which are committed which matters to visit restrictions over a planned scheduled time period to accomplish minimal cost and maximum profit of power generations. In the proposed work, authors have effectively given the combination of chaotic maps with Harris Hawks Optimization with Sine Cosine Algorithm and Slime Mould Algorithm advancement strategy and assessed execution of the proposed improved technique, named as hCHHO-SCA and hCSMA-SCA for standard benchmark of PBUCP are checked for small to large scale test systems i.e. 10-, 40- and 100-generating unit systems are considered and profit of each are evaluated as outcomes are compared with the exiting optimization algorithms in this research work.

Care of the Immigrant Child.

Care of the Immigrant Child.

Distributed Clustering-Based Cooperative Vehicular Edge Computing for Real-Time Offloading Requests

Mobile vehicles have been considered as potential edge servers to provide computation resources for the emerging Intelligent Transportation System (ITS) applications. However, how to fully utilize the mobile computation resources to satisfy the real-time arrived computation requests has not been explored yet. This work will address the critical challenges of limited computation resources, stringent computation delay and unknown requirement statistics of real-time tasks in realistic vehicular edge computing scenarios. Specifically, we design a distributed clustering strategy to classify vehicles into multiple cooperative edge servers according to the available computation resources, effective connection time and the distribution of tasks’ expected deadlines. Then, a ‘Less than or Equal to’ Generalized Assignment Problem (i.e., LEGAP) is formulated to maximize the system service revenue, and on this basis, we propose an offline Bound-and-Bound based Optimal (BBO) algorithm to make periodical scheduling with a global view of tasks’ requirement statistics. The quick branching is conducted by following a greedy solution and the upper bound at each branch is derived by solving a multiple-choice knapsack problem. In addition, we present an online heuristic algorithm which makes real-time offloading decision with the guarantees that the resource capacities of all the computing servers are never exceeded with new tasks arriving. Through comparing with the other four online algorithms, the BBO algorithm achieves the highest service revenues by offloading tasks with shorter delays, and the online heuristic algorithm has the best performance in improving the service ratios.

DETERIORATING INVENTORY POLICY FOR ITEMS WITH POWER DEMAND AND VARIABLE HOLDING COST CONSIDERING SHORTAGES

In this paper, An EOQ model for a linear deteriorating inventory policy for items with power demand pattern is presented. Holding cost is assumed to be time-dependent. Shortages are allowed and partially backlogged. The paper aims at minimising the total inventory cost by optimising the scheduling period and optimal ordering quantity. A numerical example is presented to establish the applicability of the model and sensitivity analysis carried out based on the example to know the effect of changes in various parameters in the system.

Optimization Strategy of Configuration and Scheduling for User-Side Energy Storage

In order to reduce the impact of load power fluctuations on the power system and ensure the economic benefits of user-side energy storage operation, an optimization strategy of configuration and scheduling based on model predictive control for user-side energy storage is proposed in this study. Firstly, considering the cost and benefits of energy storage comprehensively, an energy storage configuration optimization model with the highest annualized net income as the goal is built to determine the parameters for configuring energy storage. Then, with the goal of maximizing the profit during the scheduling period, pre-month scheduling optimization model, day-ahead scheduling optimization model and intra-day scheduling optimization model are established. The goal of the pre-month scheduling optimization model is to determine the maximum monthly demand; part of the scheduling results in the day-ahead scheduling optimization model directly participate in the intra-day scheduling; the intra-day rolling optimization relies on the advantages of real-time feedback and closed-loop scheduling to smooth out power fluctuations caused by load forecast errors. Finally, the configuration and economic benefit of lithium iron phosphate batteries, lead-carbon batteries and sodium-sulfur batteries are analyzed and compared, and scheduling analysis is performed. The simulation results show that the proposed optimization method can cut peaks and fill valleys, ensure the economic benefits of users, and provide guidance for users to invest in energy storage.

Codesign of communication scheduling and controller of networked control systems

Networked control systems have received increasing attention from many researchers because of their vast potential. The insertion of communication networks in a controlled system brings network-induced defects, which are mainly caused by limited network resources. This paper proposes a codesign of periodic communication scheduling and a controller using sparsity for efficient use of the network while improving initial control performance. The effectiveness of the proposed method is verified through two numerical simulations.

USABILITY APLIKASI ANDROID HONDA e-Care DENGAN USE QUESTIONNAIRE DAN IMPORTANCE PERFORMANCE ANALYSIS (IPA)

Honda Pondok Pinang, which is located on Jl. Ciputat Raya No.80 Pondok Pinang, South Jakarta, in customer service using an android application called HONDA e-Care. HONDA e-Care is very helpful in making it easier for consumers to access Honda service services. Customers can book services at Honda dealers online, get notifications of periodic maintenance schedules, and search for the nearest dealer from a location that can be seen on a digital map by first accessing the Honda e-Care application. But consumers often also face problems, such as when booking a service, but the ongoing booking service does not appear. Until now, no one has conducted research on the quality of the Honda e-Care application. This study aims to measure the extent of the usability of the HONDA e-Care android application and provide suggestions for developing the HONDA e-Care application in the future. Based on the gap analysis, it was found that in general the HONDA e-Care android application on the Honda Pondok Pinang was not satisfied with the quality of the current applications. Based on the IPA analysis, the priority scale for improving the quality of the HONDA e-Care android application can be mapped where quadrant I is the highest priority scale according to the user's perspective, namely: [2], [5], [7], [8] and [9]. These four items must be upgraded immediately by the manager to meet user expectations.

Periodic Event Scheduling for Automated Production Systems

Consider optimizing a periodic schedule for an automated production plant as a last step of a more comprehensive design process. In our scenario, each robot’s cyclic sequence of operations and trajectories between potential waiting points have already been fully specified. Further given are those precedences that fix sequence requirements on operations between different robots. It remains to determine the starting time for each operation or movement of each robot within a common cyclic time period so as to avoid collisions of robots that operate in the same space simultaneously. So the task is to find a conflict-resolving schedule that minimizes this common periodic cycle time while observing all precedence relations and collision avoidance constraints. The proposed cycle time minimization problem for robot coordination has, to the best of our knowledge, not been studied before. We develop an approach for solving it by employing binary search for determining the smallest feasible period time of an iso-periodic event scheduling problem (IPESP). This is a variant of the periodic event scheduling problem in which the objects that have to be scheduled need to obey exactly the same period time. The possibility to wait arbitrarily long at waiting points turns out to be essential to justify the use of binary search for identifying the minimum cycle time, thereby avoiding bilinear mixed integer formulations. Special properties of the given scenario admit bounds on the periodic tension variables of an integer programming formulation. Although the IPESP subproblems remain NP-complete in general, these bounds allow solving real-world instances sufficiently fast for the approach to be applicable in practice. Numerical experiments on real-world and randomly generated data are supplied to illustrate the potential and limitations of this approach. Summary of Contribution: When designing automated production plants, a crucial step is to identify the smallest possible per unit period time for the production processes. Based on periodic event scheduling ideas, we develop and analyze mathematical methods for this purpose. We show that the algorithmic implementation of our approach provides an answer to current real-world designs in reasonable time.

An optimal energy aware aperiodic task server for autonomous IoT sensors

A real-time applicative software consists of both aperiodic and periodic tasks. The periodic tasks have regular arrival times and strict deadlines. The aperiodic tasks have irregular arrival times and no deadline. The objective of an optimal aperiodic task server is to guarantee minimal response times for the aperiodic tasks and no violation of hard deadlines for periodic tasks. We consider a real-time energy harvesting system composed of energy harvester, energy storage unit, uniprocessing unit and the real-time tasks. We introduce a novel periodic task scheduler, namely SSP which is an extension of the slack stealing server so as to cope with fluctuations in energy availability. Experimental results show that the proposed algorithm achieves better performance in terms of aperiodic responsiveness. Simulations have been conducted for various settings of workloads and harvested energy profiles.

Long-term safety and efficacy of anacetrapib in patients with atherosclerotic vascular disease.

AimsREVEAL was the first randomized controlled trial to demonstrate that adding cholesteryl ester transfer protein inhibitor therapy to intensive statin therapy reduced the risk of major coronary events. We now report results from extended follow-up beyond the scheduled study treatment period.Methods and resultsA total of 30 449 adults with prior atherosclerotic vascular disease were randomly allocated to anacetrapib 100 mg daily or matching placebo, in addition to open-label atorvastatin therapy. After stopping the randomly allocated treatment, 26 129 survivors entered a post-trial follow-up period, blind to their original treatment allocation. The primary outcome was first post-randomization major coronary event (i.e. coronary death, myocardial infarction, or coronary revascularization) during the in-trial and post-trial treatment periods, with analysis by intention-to-treat. Allocation to anacetrapib conferred a 9% [95% confidence interval (CI) 3–15%; P = 0.004] proportional reduction in the incidence of major coronary events during the study treatment period (median 4.1 years). During extended follow-up (median 2.2 years), there was a further 20% (95% CI 10–29%; P < 0.001) reduction. Overall, there was a 12% (95% CI 7–17%, P < 0.001) proportional reduction in major coronary events during the overall follow-up period (median 6.3 years), corresponding to a 1.8% (95% CI 1.0–2.6%) absolute reduction. There were no significant effects on non-vascular mortality, site-specific cancer, or other serious adverse events. Morbidity follow-up was obtained for 25 784 (99%) participants.ConclusionThe beneficial effects of anacetrapib on major coronary events increased with longer follow-up, and no adverse effects emerged on non-vascular mortality or morbidity. These findings illustrate the importance of sufficiently long treatment and follow-up duration in randomized trials of lipid-modifying agents to assess their full benefits and potential harms.Trial registrationInternational Standard Randomized Controlled Trial Number (ISRCTN) 48678192; ClinicalTrials.gov No. NCT01252953; EudraCT No. 2010-023467-18.

A multi-feature fusion transfer learning method for displacement prediction of rainfall reservoir-induced landslide with step-like deformation characteristics

Rainfall reservoir-induced landslides in the Zigui Basin, China Three Gorges Reservoir (CTGR) area, exhibit typical step-like deformation characteristics with mutation and creep states. Previous landslide displacement forecasting models yielded low prediction accuracy especially for mutational displacements. Coupled with the lack of monitoring sites and data limitations, it is extremely difficult to obtain accurate and reliable early warnings for landslides. The multi-feature fusion transfer learning (MFTL) method proposed in this paper applies the knowledge and skills obtained from the Baijiabao landslide scenario and sufficient monitoring data to improve the prediction capacity for other landslides, such as the Bazimen and Baishuihe landslides. The model barely relies on the long-time continuous monitoring process, and it can not only fill gaps in data when monitoring is interrupted, but also provide real-time displacement predictions based on accurate weather forecasting and periodic reservoir scheduling. In addition, the non-uniform weight error (NWE) evaluation method is proposed in this paper to focus more on the mutation state prediction accuracy because landslide instability is most likely to occur in this stage. Compared with other intelligent algorithms, the results indicate that the MFTL method owns low prediction error and high reliability, as well as the positive generalization ability in landslide prediction. This study paves the potential way for realizing the real-time, whole-process and accurate landslide forecasting.

Energy management of ultra-short-term optimal scheduling of integrated energy system considering the characteristics of heating network

In view of the differences in transmission delay and transmission loss between heating network and power-grid in the integrated energy system, an ultra-short-term optimal scheduling energy management considering the characteristics of heating network is proposed in this pape. Based on graph theory, a heating network model suitable for system optimal scheduling is established. Considering the transient characteristics of the thermal medium temperature, the theoretical basis for judging the transient and steady-state heat transfer characteristics of the pipeline according to the scheduling period and the length of the pipeline is derived. The optimal operating state and daily operating cost of the equipment under three scenarios (considering the transient heat transfer characteristics of the heating network, considering the steady-state heat transfer characteristics of the heating network, and not considering the heat transfer characteristics of the heating network) are respectively compared. The calculation example analysis and verification show that the heat storage characteristics caused by the complex topology and transmission delay of the heat network change the matching of the supply and demand of the thermal power of the system, and the thermal power of the supply and demand side is no longer matched in real time. The ultra-short-term optimal scheduling energy management considering the transient heat transfer characteristics of the heating network has the regulation potential of the IES electric-thermal economic optimal operation under the excitation of time-of-use electricity price, and effectively makes use of the complementary characteristics of the electric and thermal systems to reduce the operation cost of the integrated energy system.

On Periodic Scheduling and Control for Networked Systems Under Random Data Loss

This article deals with networked control systems (NCSs) whose shared networks have limited communication capacity and are prone to data losses. Our contributions are two-fold. First, we present necessary and sufficient conditions on the plant and controller dynamics and the network parameters such that there exist purely time-dependent periodic scheduling sequences under which the stability of each plant is preserved for all admissible data loss signals. Second, given a period for the scheduling sequence, we identify sufficient conditions on the plant dynamics and the network parameters such that the plants admit static state-feedback controllers that are favorable for the stability conditions presented. The main apparatus for our analysis is a switched system representation of the individual plants in an NCS whose switching signals are time-inhomogeneous Markov chains. Our stability conditions involve the existence of sets of symmetric and positive definite matrices that satisfy certain (in)equalities. We identify the existence of stabilizing periodic scheduling sequences and their corresponding favorable state-feedback controllers under ideal communication between the plants and their controllers as special cases of our results. A numerical experiment is presented to demonstrate the proposed techniques.