Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

基于NPV Scheduler软件的露天转地下矿山开采境界研究

基础的L-G图论法未考虑地下开采可能的成本优势,在露天转地下矿山开采境界圈定时存在一定的局限性。本文通过对价格法与储量盈利比较法计算的经济合理剥采比进行分析,结合NPV Scheduler软件研究了露天转地下开采境界圈定方法,并通过工程实例验证了该方法的合理性。研究成果对露天转地下矿山开采境界的确定具有一定的指导意义。

Influence of two-stage harvesting on the properties of cold-pressed rapeseed(Brassica napus L.)oils

Rapeseed(Brassica napus L.)harvesting method is critical since it significantly determines the seed yield,oil quality,and industrial efficiency.This study investigated the influences of harvesting methods on the quality of cold-pressed rapeseed oil of two varieties.Oil color,peroxide value(POV),tocopherol content,fatty acid composition,and polarity of total polyphenols(PTP)contents of two rapeseed varieties in Huanggang and Xiangyang were compared through artificially simulated combined harvesting and two-stage harvesting.Results showed significant differences in the quality of rapeseed oil between the two harvesting methods.The red value(R-value),POV,total tocopherol contents,linoleic and linolenic acid content,and PTP content of the pressed rapeseed oil prepared by the combined harvesting method were about 27.6,5.7,15.8,2.0,0.5,and 28.6%lower than those of the oil produced from the two-stage harvesting method,respectively.Xiangyang and Huayouza62 performed better in the two regions and two varieties,respectively.To sum up,the rapeseed oil obtained 41–44 days after final flowering of combined harvesting,35 days after final flowering,and six days of post-ripening of the two-stage harvesting had the best quality.

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.

Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN

The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation jobs.This paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator(YARN)scheduler,called the Adaptive Node and Container Aware Scheduler(ANACRAC),that aligns cluster resources to the demands of the applications in the real world.The approach performs to leverage the user-provided configurations as a unique design to apportion nodes,or containers within the nodes,to application thresholds.Additionally,it provides the flexibility to the applications for selecting and choosing which node’s resources they want to manage and adds limits to prevent threshold breaches by adding additional jobs as needed.Node or container awareness can be utilized individually or in combination to increase efficiency.On top of this,the resource availability within the node and containers can also be investigated.This paper also focuses on the elasticity of the containers and self-adaptiveness depending on the job type.The results proved that 15%–20%performance improvement was achieved compared with the node and container awareness feature of the ANACRAC.It has been validated that this ANACRAC scheduler demonstrates a 70%–90%performance improvement compared with the default Fair scheduler.Experimental results also demonstrated the success of the enhancement and a performance improvement in the range of 60%to 200%when applications were connected with external interfaces and high workloads.

A hybrid two-stage fexible flowshop scheduling problem with m identical parallel machines and a burn-in processor separately

A hybrid two-stage flowshop scheduling problem was considered which involves m identical parallel machines at Stage 1 and a burn-in processor M at Stage 2, and the makespan was taken as the minimization objective. This scheduling problem is NP-hard in general. We divide it into eight subcases. Except for the following two subcases： （1） b≥ an, max{m, B} 〈 n; （2） a1 ≤ b ≤ an, m ≤ B 〈 n, for all other subcases, their NP-hardness was proved or pointed out, corresponding approximation algorithms were conducted and their worst-case performances were estimated. In all these approximation algorithms, the Multifit and PTAS algorithms were respectively used, as the jobs were scheduled in m identical parallel machines.

A two-stage but time-saving reconstructive scheme to treat severe lower limb length discrepancy:Two case reports

Limb length discrepancy(LLD)is a common orthopedic condition that can result in significant functional impairment,pain,and cosmetic deformities.Current reconstructive techniques for severe LLD are primarily based on callus distraction,which is a time-consuming process that can lead to complications,such as significant infection,joint stiffness,and stress fractures.To reduce the therapeutic time and minimize the risk of complications,we investigated the use of vascularized bone flaps as a technical supplement to callus distraction in the reconstruction of short limbs.We present two cases of severe LLD in the upper and lower legs,in which a twostage reconstruction approach was used.In the first stage,external fixation was applied to the affected limb to correct the soft tissue length and convert the short deformity into a bone defect.In the second stage,the bone defect was reconstructed using bilateral(patient A)or unilateral(patient B)free vascularized fibula bone grafts.Both patients had complete survival of the fibular grafts without stress fractures,and bone consolidation took 8 months(patient A)and 4 months(patient B).Compared to the traditional callus distraction,the two-stage approach was found to be more time-saving and reliable.The entire reconstructive scheme required 18 and 4 months for patients A and B,respectively,whereas the traditional callus distraction required 41 and 17 months,respectively.These findings suggest that the use of vascularized bone flaps as a technical supplement for callus distraction may provide an effective and efficient alternative for the treatment of severe LLD.Further studies are needed to validate these results and assess the long-term outcomes of this approach.

A New Two-Stage Tunable Space-Time Adaptive Detector

In order to improve the rejection capability of mismatched interferer signals,a new two-stage detector is proposed under homogeneous scenarios with unknown covariance matrix,which is obtained by cascading the adaptive matched filter(AMF)detector and the enhanced RAO(EnRAO)detector.The new detector has constant false alarm performance,and the closed-form expression of probability of false alarm and probability of detection is derived.The performance of the new detector is assessed,and analyzed in comparison with other detectors.The results show that,the proposed detector can provide enhanced rejection capability in the case of mismatch,but the performance of the detector is slightly lost under the condition of matching.

Two-stage revision in periprosthetic knee joint infections

Periprosthetic joint infection(PJI)following total knee arthroplasty is one of the most catastrophic and costly complications that carries significant patient wellness as well as economic burdens.The road to efficiently diagnosing and treating PJI is challenging,as there is still no gold standard method to reach the diagnosis as early as desired.There are also international controversies with respect to the best approach to manage PJI cases.In this review,we highlight recent advances in managing PJI following knee arthroplasty surgery and discuss in depth the two-stage revision method.

采用NPV Scheduler软件对某大型露天矿山进行境界优化

某大型矿床随着开采的进行以及补充勘探的深入,加上市场的变化,使得原来的境界设计条件发生了一些变化,因此需要根据最新的边界条件对境界进行优化设计。采用NPV Scheduler软件用L-G图论法对该铜矿的最终开采境界进行了优化圈定,使得最终境界更符合当前采选成本和市场条件。

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

A Layered Energy-Efficient Multi-Node Scheduling Mechanism for Large-Scale WSN

In recent years, target tracking has been considered one of the most important applications of wireless sensornetwork (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally criticalobjectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. Theproposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)election, pre-selection, and task set selectionmechanisms, where the latter two kinds of selections forma two-layerselection mechanism. The CH election innovatively introduces the movement trend of the target and establishesa scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energyconsumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to applyfor tracking tasks, which can reduce the application consumption and the overhead of the following task setselection. Finally, the task node selection is mathematically transformed into an optimization problem and thegenetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results showthat HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.

A Novel Scheduling Framework for Multi-Programming Quantum Computing in Cloud Environment

As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity,which in turn hampers users from achieving optimal satisfaction.Therefore,cloud quantum computing service providers require a unified analysis and scheduling framework for their quantumresources and user jobs to meet the ever-growing usage demands.This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment.The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience.It introduces three innovative designs:1)Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks.2)Multi-programming mechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud.In comparison to conventional task schedulers,our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud.3)The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters.

A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

Performance Prediction Based Workload Scheduling in Co-Located Cluster

Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster,where the resources can be pooled in order to maximize data center resource utilization.Due to resource competition between batch jobs and online services,co-location frequently impairs the performance of online services.This study presents a quality of service(QoS)prediction-based schedulingmodel(QPSM)for co-locatedworkloads.The performance prediction of QPSM consists of two parts:the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on randomforest.On-line service QoS anomaly prediction is used to evaluate the influence of batch jobmix on on-line service performance,and batch job completion time prediction is utilized to reduce the total waiting time of batch jobs.When the same number of batch jobs are scheduled in experiments using typical test sets such as CloudSuite,the scheduling time required by QPSM is reduced by about 6 h on average compared with the first-come,first-served strategy and by about 11 h compared with the random scheduling strategy.Compared with the non-co-located situation,QPSM can improve CPU resource utilization by 12.15% and memory resource utilization by 5.7% on average.Experiments show that the QPSM scheduling strategy proposed in this study can effectively guarantee the quality of online services and further improve cluster resource utilization.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.