Accelerated solution of the transmission maintenance schedule problem:a Bayesian optimization approach

To maximize the maintenance willingness of the owner of transmission lines,this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of on-site maintenance operations.Considering the computational complexity of the mixed integer programming(MIP)problem,a machine learning(ML)approach is presented to solve the transmission maintenance scheduling model efficiently.The value of the branching score factor value is optimized by Bayesian optimization(BO)in the proposed algorithm,which plays an important role in the size of the branch-and-bound search tree in the solution process.The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency.

Day-ahead Optimization Schedule for Gas-electric Integrated Energy System Based on Second-order Cone Programming

This paper proposes an optimal day-ahead opti-mization schedule for gas-electric integrated energy system(IES)considering the bi-directional energy flow.The hourly topology of electric power system(EPS),natural gas system(NGS),energy hubs(EH)integrated power to gas(P2G)unit,are modeled to minimize the day-ahead operation cost of IES.Then,a second-order cone programming(SOCP)method is utilized to solve the optimization problem,which is actually a mixed integer nonconvex and nonlinear programming issue.Besides,cutting planes are added to ensure the exactness of the global optimal solution.Finally,simulation results demonstrate that the proposed optimization schedule can provide a safe,effective and economical day-ahead scheduling scheme for gas-electric IES.

Modeling and optimization for oil well production scheduling

In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir,subject to a number of constraints such as minimum up/down time limits and well grouping.The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost.Due to the NP-hardness of the problem,an improved particle swarm optimization(PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately.Computational experiments on randomly generated instances were carried out to evaluate the performance of the model and the algorithm's effectiveness.Compared with the commercial solver CPLEX,the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.

MODELING AND OPTIMIZATION OF CYCLIC HOIST SCHEDULES IN AN ELECTROPLATING LINE

This paper deals with the modeling, analysis and optimization of a specific kind of real industrial problems. This class of problems is known in the literature as Cyclic Hoist Scheduling Problem （CHSP）. In such class of problems, several jobs have to flow through a production line according to an ordered bath sequence. The CHSPs appear in the manufacturing facilities to achieve a mass production and to search a repetitive sequence of moves for the hoist. In this paper, we develop P-Temporal Petri Net models to represent the behavior and validate certain qualitative properties of the basic production line. Afterward, complex configurations of the production line are modeled and their properties such as reachability of desired functioning （cyclic operation）, deadlock-free, resource sharing and management are checked and validated. A mathematical analysis and a simulation study of all proposed Petri net models are carried out using mathematical fundaments of Petri nets and a Visual Object Net ＋＋ tool. The second part of the paper deals with the development of a mixed integer linear programming models to optimize processing of each line configuration. Optimal manufacturing plans of the studied system with cyclic processing sequences are defined and the feasibility of optimal cyclic scheduling of each configuration is proved.

Three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants

In order to explore the potential of profit margin improvement,a novel three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants is proposed and evaluated.A decoupling strategy is proposed for the solution of the three-scale model,which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling.This optimization framework simplifies the fundamental mixed-integer nonlinear programming(MINLP)into several sub-models,and improves the interpretability and extendibility.In the evaluation of an industrial case,a profit increase at a percentage of 3.25%is attained in optimization compared to the practical operations.Further sensitivity analysis is carried out for strategy evolving study when price policy,supply chain,and production requirement parameters are varied.These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.

Robust Optimization of Performance Scheduling Problem under Accepting Strategy

In this paper, the problem of program performance scheduling with accepting strategy is studied. Considering the uncertainty of actual situation, the duration of a program is expressed as a bounded interval. Firstly, we decide which programs are accepted. Secondly, the risk preference coefficient of the decision maker is introduced. Thirdly, the min-max robust optimization model of the uncertain program show scheduling is built to minimize the performance cost and determine the sequence of these programs. Based on the above model, an effective algorithm for the original problem is proposed. The computational experiment shows that the performance’s cost (revenue) will increase (decrease) with decision maker’s risk aversion.

An Optimization Model for Exercise Scheduling

The paper gives an optimization model for a special type of exercise session, circuit training. Circuit training involves a series of exercises performed in rotation with minimal rest. The goal of our model is to minimize the total circuit time while accomplishing a number of training goals. Our primary model is a linear integer program;additional constraints are added for muscle group and intensity requirements. The model is implemented and tested on algebraic modeling language AMPL. Our computational results show that the model can return an exercise schedule for a typical real-life data set within a few seconds.

Learning to optimize:A tutorial for continuous and mixed-integer optimization

Learning to optimize(L2O)stands at the intersection of traditional optimization and machine learning,utilizing the capabilities of machine learning to enhance conventional optimization techniques.As real-world optimization problems frequently share common structures,L2O provides a tool to exploit these structures for better or faster solutions.This tutorial dives deep into L2O techniques,introducing how to accelerate optimization algorithms,promptly estimate the solutions,or even reshape the optimization problem itself,making it more adaptive to real-world applications.By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand,this tutorial provides a comprehensive guide for practitioners and researchers alike.

Bridging Reinforcement Learning and Planning to Solve Combinatorial Optimization Problems with Nested Sub-Tasks

Combinatorial Optimization(CO)problems have been intensively studied for decades with a wide range of applications.For some classic CO problems,e.g.,the Traveling Salesman Problem(TSP),both traditional planning algorithms and the emerging reinforcement learning have made solid progress in recent years.However,for CO problems with nested sub-tasks,neither end-to-end reinforcement learning algorithms nor traditional evolutionary methods can obtain satisfactory strategies within a limited time and computational resources.In this paper,we propose an algorithmic framework for solving CO problems with nested sub-tasks,in which learning and planning algorithms can be combined in a modular way.We validate our framework in the Job-Shop Scheduling Problem(JSSP),and the experimental results show that our algorithm has good performance in both solution qualities and model generalizations.

GREENSKY:A fair energy-aware optimization model for UAVs in next-generation wireless networks

Unmanned Aerial Vehicles(UAVs)offer a strategic solution to address the increasing demand for cellular connectivity in rural,remote,and disaster-hit regions lacking traditional infrastructure.However,UAVs’limited onboard energy storage necessitates optimized,energy-efficient communication strategies and intelligent energy expenditure to maximize productivity.This work proposes a novel joint optimization model to coordinate charging operations across multiple UAVs functioning as aerial base stations.The model optimizes charging station assignments and trajectories to maximize UAV flight time and minimize overall energy expenditure.By leveraging both static ground base stations and mobile supercharging stations for opportunistic charging while considering battery chemistry constraints,the mixed integer linear programming approach reduces energy usage by 9.1%versus conventional greedy heuristics.The key results provide insights into separating charging strategies based on UAV mobility patterns,fully utilizing all available infrastructure through balanced distribution,and strategically leveraging existing base stations before deploying dedicated charging assets.Compared to myopic localized decisions,the globally optimized solution extends battery life and enhances productivity.Overall,this work marks a significant advance in UAV energy management by consolidating multiple improvements within a unified coordination framework focused on joint charging optimization across UAV fleets.The model lays a critical foundation for energy-efficient aerial network deployments to serve the connectivity needs of the future.

Jerk-limited feedrate scheduling and optimization for five-axis machining using new piecewise linear programming approach

In this paper, a new computation method and an optimization algorithm are presented for feedrate scheduling of five-axis machining in compliance with both machine drive limits and process limits. Five-axis machine tool with its ability of controlling tool orientation to follow the sculptured surface contour has been widely used in modern manufacturing industry. Feedrate scheduling serving as a kernel of CNC control system plays a critical role to ensure the required machining accuracy and reliability for five-axis machining. Due to the nonlinear coupling effects of all involved drive axes and the saturation limit of servo motors, the feedrate scheduling for multi-axis machining has long been recognized and remains as a critical challenge for achieving five-axis machine tools’ full capacity and advantage. To solve the nonlinearity nature of the five-axis feedrate scheduling problems, a relaxation mathematical process is presented for relaxing both the drive motors’ physical limitations and the kinematic constraints of five-axis tool motions. Based on the primary optimization variable of feedrate, the presented method analytically linearizes the machining-related constraints, in terms of the machines’ axis velocities, axis accelerations and axis jerks. The nonlinear multi-constrained feedrate scheduling problem is transformed into a manageable linear programming problem. An optimization algorithm is presented to find the optimal feedrate scheduling solution for the five-axis machining problems. Both computer implementation and laboratorial experiment testing by actual machine cutting were conducted and presented in this paper. The experiment results demonstrate that the proposed method can effectively generate efficient feedrate scheduling for five-axis machining with constraints of the machine tool physical constraints and limits. Compared with other existing numerical methods, the proposed method is able to find an accurate analytical solution for the nonlinear constrained five-axis feedrate scheduling problems without compromising the efficiency of the machining processes.

Harvest optimization for sustainable agriculture:The case of tea harvest scheduling

To ensure sustainability in agriculture,many optimization problems need to be solved.An important one of them is harvest scheduling problem.In this study,the harvest scheduling problem for the tea is discussed.The tea harvest problem includes the creating a harvest schedule by considering the farmers'quotas under the purchase location and factory capacity.Tea harvesting is carried out in cooperation with the farmer-factory.Factory man-agement is interested in using its resources.So,the factory capacity,purchase location capacities and number of expeditions should be considered during the harvesting process.When the farmer's side is examined,it is seen that the real professions of farmers are different.On harvest days,farmers often cannot attend to their primary professions.Considering the harvest day preferences of farmers in creating the harvest schedule are of great importance for sustainability in agriculture.Two different mathematical models are proposed to solve this problem.The first model minimizes the number of weekly expeditions of factory vehicles within the factor and purchase location capacity restrictions.The second model minimizes the number of expeditions and aims to comply with the preferences of the farmers as much as possible.A sample application was performed in a region with 12 purchase locations,988 farmers,and 3392 decares of tea fields.The results show that the compli-ance rate of farmers to harvesting preferences could be increased from 52%to 97%,and this situation did not affect the number of expeditions of the factory.This result shows that considering the farmers'preferences on the harvest day will have no negative impact on the factory.On the contrary,it was concluded that this situation increases sustainability and encouragement in agriculture.Furthermore,the results show that models are effective for solving the problem.

采用融合遗传算法的高速公路服务区综合能源系统优化调度研究

为达成“碳中和”目标愿景、促进公路交通系统与新能源的融合,以高速公路服务区为研究对象,考虑服务区内电、冷、热、气共4种负荷需求,构建了包含风光发电的新能源发电方式和电转气设备的高速公路服务区综合能源系统。在此基础上,以风电、光伏出力日前预测和多能负荷日前消耗为输入,各能源设备出力及购能分配为输出,以总成本最低为目标函数,考虑能量平衡、设备安全、运行状态等约束,建立了高速公路服务区综合能源系统优化调度模型。针对高速公路服务区综合能源系统调度问题,设计了遗传-序列二次规划融合优化算法,并以某服务区夏季典型日为例进行验证。结果表明:所构建的调度系统能够有效消纳可再生能源出力,协调外部购电、购气的比例,最终达到降低成本的效果;所提融合算法的调度结果与传统遗传算法、传统序列二次规划算法相比,在成本上分别降低了11.52%、0.70%,求解耗时仅为传统遗传算法的6.7%,独立性相比传统序列二次规划算法得到了提高。

含SOP的交直流混合配电网日前优化调度

为实现交直流混合配电网的高效运行,提出一种含智能软开关的交直流配电网优化调度方法。在对应用于交直流配电网的智能软开关工作原理进行阐述的基础上,建立含智能软开关的交直流配电网优化调度模型。通过线性化和凸松弛技术,将所建立的非线性优化模型转化为二阶锥规划模型,并且采用改进的50节点算例分析验证模型的有效性。算例结果表明,基于所建模型得到的智能软开关运行策略能够降低配电网运行损耗及改善电压越限的情况,显著提高混合配电网的经济性。

Short-term Transmission Maintenance Scheduling Considering Network Topology Optimization

With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind power.Meanwhile,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power systems.This paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control devices.The problem is formulated as a two-stage stochastic mixed-integer programming model.The first stage arranges the maintenance periods of transmission lines.The second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed scenarios.The proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both stages.Therefore,the progressive hedging algorithm is applied.The results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.

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.

Efficient Convex Optimization Approaches to Variational Image Fusion

Image fusion is an imaging technique to visualize information from multiple imaging sources in one single image,which is widely used in remote sensing,medical imaging etc.In this work,we study two variational approaches to image fusion which are closely related to the standard TV-L_(2) and TV-L_(1) image approximation methods.We investigate their convex optimization formulations,under the perspective of primal and dual,and propose their associated new image decomposition models.In addition,we consider the TV-L_(1) based image fusion approach and study the specified problem of fusing two discrete-constrained images f_(1)(x)∈L_(1) and f_(2)(x)∈L_(2),where L_(1) and L_(2) are the sets of linearly-ordered discrete values.We prove that the TV-L_(1) based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discretevalued set u(x)∈L_(1)∪L_(2).This extends the results for the global optimization of the discrete-constrained TV-L_(1) image approximation[8,36]to the case of image fusion.As a big numerical advantage of the two proposed dual models,we show both of them directly lead to new fast and reliable algorithms,based on modern convex optimization techniques.Experiments with medical images,remote sensing images and multi-focus images visibly show the qualitative differences between the two studied variational models of image fusion.We also apply the new variational approaches to fusing 3D medical images.

耦合调峰与通航需求的梯级水电站群短期多目标优化调度的MILP方法

电网调峰任务与河道通航需求间的矛盾是水电航运梯级调度时所面临的突出问题,区间回水的顶托作用增大了该问题的复杂性。建立考虑回水影响的梯级水电站群短期多目标优化调度的混合整数线性规划模型,模型以剩余负荷平均距与下游尾水位平均距最小为目标,在将非线性约束通过函数聚合后,利用六面体栅格化技术与第二类特殊有序集约束方法实现该约束的线性化。利用法线边界交叉方法对模型进行求解。算例结果表明,所提方法可以充分计及回水顶托的影响,兼顾调峰与通航需求,高效求解多目标调度问题并获得较理想的结果。

云资源调度的回答集程序描述性求解

针对求解难度为NP完全的基础设施即服务(IaaS)模式云资源调度问题,本文提出一种基于回答集程序(ASP)的描述性优化求解方法,并对其正确性进行分析。首先,把满足虚拟机CPU使用的情况下关闭尽可能多的主机做为减少云平台能耗的方法,将云资源调度问题形式化表述;其次,结合形式化描述以及减少云平台能耗的策略,将云资源调度问题用ASP编码为描述性(优化)问题,并分析其正确性;最后,在公开的PlanetLab数据集上进行实验,结果显示,ASP方法可在保障服务质量的同时减少集群能耗,最高可节能13%以上。这表明ASP方法在云资源调度问题上是有效的,从而提供一种易理解、易修改并能充分利用ASP最新工具成果的有效云资源调度新方法。

兼顾本地与直流受端电网调峰需求的水电站群短期优化调度模型

面对巨型水电站群高压直流送端电网和受端电网日益严峻的调峰压力,建立了一种兼顾本地与直流受端电网调峰需求的梯级水电站群短期优化调度模型。模型以多电网剩余负荷最大值最小为目标,考虑常规梯级水电站群水力、电力约束,以及复杂高压直流输电线路运行约束,并引入直流线路与水电站之间耦合运行约束。在模型求解阶段,引入辅助变量将目标函数、水电运行约束、直流运行约束线性化,并采用大M法对直流线路与水电站之间双向区间耦合约束进行线性化,将原模型转换为混合整数线性规划模型。以中国西南地区某巨型水电站群冬季和夏季典型日为实例研究,结果表明:送端电网负荷峰谷差降幅高达100%,受端电网负荷峰谷差降幅分别达28.1%和31.6%。所提模型能够利用高压直流输电线路共享水电灵活性,有效实现多电网调峰,并保证直流输电线路运行在安全区间,可以为巨型梯级水电站群短期多电网调峰提供参考和借鉴。