Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

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.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Maintenance scheduling is essential and crucial for wind turbines (WTs) to avoid breakdowns andreduce maintenance costs. Many maintenance models have been developed for WTs’ maintenance planning, suchas corrective, preventive, and predictive maintenance. Due to communities’ dependence on WTs for electricityneeds, preventive maintenance is the most widely used method for maintenance scheduling. The downside tousing this approach is that preventive maintenance (PM) is often done in fixed intervals, which is inefficient. In thispaper, a more detailed maintenance plan for a 2 MW WT has been developed. The paper’s focus is to minimize aWT’s maintenance cost based on a WT’s reliability model. This study uses a two-layer optimization framework:Fibonacci and genetic algorithm. The first layer in the optimization method (Fibonacci) finds the optimal numberof PM required for the system. In the second layer, the optimal times for preventative maintenance and optimalcomponents to maintain have been determined to minimize maintenance costs. The Monte Carlo simulationestimates WT component failure times using their lifetime distributions from the reliability model. The estimatedfailure times are then used to determine the overall corrective and PM costs during the system’s lifetime. Finally,an optimal PM schedule is proposed for a 2 MW WT using the presented method. The method used in this papercan be expanded to a wind farm or similar engineering systems.

Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Technological advancements in unmanned aerial vehicles(UAVs)have revolutionized various industries,enabling the widespread adoption of UAV-based solutions.In engineering management,UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments,surpassing traditional inspection techniques.Building on this foundation,this paper delves into the optimization of UAV inspection routing and scheduling,addressing the complexity introduced by factors such as no-fly zones,monitoring-interval time windows,and multiple monitoring rounds.To tackle this challenging problem,we propose a mixed-integer linear programming(MILP)model that optimizes inspection task assignments,monitoring sequence schedules,and charging decisions.The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem(VRP),leading to a mathematically intractable model for commercial solvers in the case of large-scale instances.To overcome this limitation,we design a tailored variable neighborhood search(VNS)metaheuristic,customizing the algorithm to efficiently solve our model.Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm,demonstrating its scalability for both large-scale and real-scale instances.Sensitivity experiments and a case study based on an actual engineering project are also conducted,providing valuable insights for engineering managers to enhance inspection work efficiency.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

IaaS Public Cloud Computing Platform Scheduling Model and Optimization Analysis

IaaS (Infrastructure as a Platform) public cloud is one mainstream service mode for public cloud computing. The design aim of one IaaS public cloud is to enlarge the hardware-usage of whole platform, optimize the virtual machine deployment and enhance the accept rate of service demand. In this paper we create one service model for IaaS public cloud, and based on the waiting-line theory to optimize the service model, the queue length and the configuration of scheduling server. And create one demand-vector based scheduling model, to filter the available host machine according to the match of demand and metadata of available resource. The scheduling model can be bonded with the virtual machine motion to reallocate the resources to guarantee the available rate of the whole platform. The feasibility of the algorithm is verified on our own IaaS public cloud computing platform.

Balancing timber production and habitat conservation of Okinawa Rails(Gallirallus okinawae): Application of a harvest scheduling optimization model in subtropical forest in Okinawa, Japan

Finding the right balance between timber production and the management of forest-dependent wildlife species,present a difficult challenge for forest resource managers and policy makers in Okinawa,Japan.A possible explanation of this can be found in the unique nature of the forest management area which is populated with various kinds of rare and endangered species.This issue has been brought to light as a result of the nomination of northern Okinawa Island in 2018 as a candidate for World Natural Heritage site.The nomination has raised public awareness to the possibility of conflicting management objectives between timber extraction and the conservation of habitat for forest-dependent wildlife species.Managing exclusively for one objective over the other may fail to meet the demand for both forest products and wildlife habitat,ultimately jeopardizing the stability of human and wildlife communities.It is therefore important to achieve a better balance between the objective of timber production and conservation of wildlife habitat.Despite the significance of this subject area,current ongoing discussions on how to effectively manage for forest resources,often lack scientific basis to make sound judgement or evaluate tradeoffs between conflicting objectives.Quantifying the effect of these forest management activities on wildlife habitat provides useful and important information needed to make forest management and policy decisions.In this study we develop a spatial timber harvest scheduling model that incorporates habitat suitability index(HSI)models for the Okinawa Rail(Gallirallus okinawae),an endangered avian species found on Okinawa,Japan.To illustrate how the proposed coupling model assembles spatial information,which ultimately aids the study of forest management effects on wildlife habitat,we apply these models to a forest area in Okinawa and conduct a simple simulation analysis.

Joint optimization scheduling for water conservancy projects incomplex river networks

In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.

Mid-long Term Optimal Dispatching Method of Hydro-thermal Power System Considering Scheduled Maintenance

在中长期水火发电调度中考虑检修计划的影响是目前中长期水火发电调度面临的难题。利用现代整数代数建模技术，建立发电计划和检修计划协调优化的多场景调度模型。在该模型中，鉴于设备检修计划的连续性，在预测场景树的基础上，将场景节点划分成不同的场景，通过节点和场景关联矩阵，实现多场景下设备检修模型的构建。同时，鉴于中长期调度计划中发电计划和检修计划对时段间隔要求的不同，分别设置电量相关节点和电力相关节点，实现中长期发电计划和检修计划的协调。上述模型是一个大规模混合整数线性规划（mixed integer linear programming，MILP）问题，采用商用MILP求解器进行求解。大规模实际水火电系统的实例分析结果表明，所提模型和方法是可行、有效的。

A Heuristics-Based Cost Model for Scientic Workow Scheduling in Clou

Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.

Global Optimization of Nonlinear Blend-Scheduling Problems

The scheduling of gasoline-blending operations is an important problem in the oil refining industry. Thisproblem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but alsonon-convex nonlinear behavior, due to the blending of various materials with different quality properties.In this work, a global optimization algorithm is proposed to solve a previously published continuous-timemixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimi-zation, the distribution problem, and several important operational features and constraints. The algorithmemploys piecewise McCormick relaxation （PMCR） and normalized multiparametric disaggregation tech-nique （NMDT） to compute estimates of the global optimum. These techniques partition the domain of oneof the variables in a bilinear term and generate convex relaxations for each partition. By increasing the num-ber of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates ofthe global solution. The algorithm is compared to two commercial global solvers and two heuristic methodsby solving four examples from the literature. Results show that the proposed global optimization algorithmperforms on par with commercial solvers but is not as fast as heuristic approaches.

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.

An Optimal Method to Schedule Dynamic Maintenance Task with Subject Taken into Account

The task of maintenance organization is very heavy at wartime.The usability of armaments may be greatly improved by efficient task scheduling.In order to recover the battle effectiveness of units in battlefield as fast as possible,dynamic maintenance scheduling models with subject taken into account were built on the basis of analysis the feature of maintenance task.Maintenance task scheduling problem is very complicated.So it is decomposed into two sub-problems:static maintenance task scheduling and dynamic maintenance task scheduling problem with subject taken into account.Corresponding mathematic models were built to these sub-problems and their solutions were proposed.Dynamic maintenance task scheduling with subject taken into account is on the basis of static maintenance task scheduling.With the task changing in battlefield,dynamic task scheduling can be realized by repeatedly call of static maintenance task scheduling with subject taken into account.The experimented results show that dynamic maintenance task scheduling method with maintenance subject taken into account is valid.

Economical Aspects of the Vehicle Scheduling Optimization

The paper deals with the vehicle scheduling problem related to regional public transport. Linear programming methods are used to solve the problem. A mathematical model is created including the constraints and the objective function minimizing costs and the number of vehicles. A minimum costs and a number of vehicles are forced at the same time by special economical input data analysis and an allocation of costs. Determining of the costs coefficients is done by three methods, which differs primarily by how much of the total costs they take into account. The decomposition of the set of lines into disjoint subsets can be used instead of the ＂direct＂ optimization. The decomposition has proven to be a suitable alternative in solving large optimization problems. The problem was applied to optimize vehicle scheduling in the region, which is situated in the north-east of the Czech Republic. There is used Xpress-IVE software, which solve the problem by simplex algorithm and branch and bound method. Research results show that there are large reserves in the organization of public transport. The implementation of the new vehicle scheduling would bring significant costs reductions in amount of at least 10% for the optimal solution and in amount of about 10% for the decomposition solution. The number of drivers could be decreased and the total time of the vehicles being outside the garage could be also reduced by at least 10%.

Fair Scheduling Models for Doubles Group Competitions

This paper gives integer linear programming models for scheduling doubles tennis group competitions. The goal is to build a fair and competitive schedule for all players. Our basic model achieves that for each player the average ranking of his partners in all matches is as close as possible to the average ranking of his opponents in all matches. One of the variations of the basic model provides that each matchup is fair and competitive. We also give models for the case when the number of players is 4n + 2, and thus one of the matches has to be singles. Our models were implemented and tested using optimization software AMPL. Computational results along with schedules for some typical situations are also given the paper.

Multi-satellite observation integrated scheduling method oriented to emergency tasks and common tasks

Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.

A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty

A strategy for the integration of production planning and scheduling in refineries is proposed. This strategy relies on rolling horizon strategy and a two-level decomposition strategy. This strategy involves an upper level multiperiod mixed integer linear programming （MILP） model and a lower level simulation system, which is extended from our previous framework for short-term scheduling problems [Luo, C.E, Rong, G, ＂Hierarchical apthis extended framework is to reduce the number of variables and the size of the optimization model and, to quickly find the optimal solution for the integrated planning/scheduling problem in refineries. Uncertainties are also considered in this article. An integrated robust optimization approach is introduced to cope with uncertain parameters with both continuous and discrete probability distribution.

Medium-term scheduling of operating states in electric power systems

This article presents a mathematical model for the medium-term scheduling of the operating states of electric power systems.The scheduling period is divided into several time intervals.The model can be used to determine the equilibrium state in which each supplier earns maximum profit from supplying electricity to the wholesale market.We estimated the maximum value of public welfare,which indicates the total financial gains of suppliers and consumers,to determine the prices at the nodes of the power system.This was done by considering the balance constraints at the nodes of the power system and constraints on the allowable values of generation,power flows,and volumes of energy resources consumed over several time intervals.This problem belongs to the class of bi-level Stackelberg game-theoretic models with several leaders.The market equilibrium is modeled simultaneously in several intervals,given the multiplicity and duration of interactions.We considered two approaches for solving the multi-interval equilibrium state problem.The first approach involved directly solving a system of joint optimality conditions for electricity suppliers and consumers.The second approach involved iterative searches until the equilibrium state was reached.This article presents the results of medium-term scheduling using a case study of a simplified real-world power system.

A Method for Solving Textile Production Scheduling Problem

The modern textile production is driven by order forms, in which continuous and discrete characteristics both exist. Hybrid Petri net (HPN) was selected to build the textile production model that described all operations in a uniform form, and connected them by discrete places and inhibitor arcs. Based on this structured model, the mathematical programming description was given. In order to take the minimal costs and complete all order forms in due time, genetic algorithm (GA) was used to optimize the production scheduling scheme. Using the data from a textile enterprise, the simulation proves that the proposed method is effective.