Reservoir operation for hydropower optimization: A chance-constrained approach

This paper presents a chance-constrained linear programming formulation for reservoir operation of a multipurpose reservoir. The release policy is defined by a chance constraint that the probability of irrigation release in any period equalling or exceeding the irrigation demand is at least equal to a specified valueP (called reliability level). The model determines the maximum annual hydropower produced while meeting the irrigation demand at a specified reliability level. The model considers variation in reservoir water level elevation and also the operating range within which the turbine operates. A linear approximation for nonlinear power production function is assumed and the solution obtained within a specified tolerance limit. The inflow into the reservoir is considered random. The chance constraint is converted into its deterministic equivalent using a linear decision rule and inflow probability distribution. The model application is demonstrated through a case study.     

R-藤Pair Copula模型下的投资组合最优套期保值比例研究

目前我国证券市场尚未成熟,风险对冲手段单一,因此构建一个期货对多资产进行套期保值策略对于投资组合的风险管理尤为重要．本文构建了基于线性规划的最小CVaR（Conditional Value at Risk）套期保值模型,同时运用R-藤Pair Copula-GARCH模型结合蒙特卡洛模拟生成投资组合中各资产收益率的情景和概率,将结果用于基于线性规划的最小CVaR套期保值模型,可确定投资组合的最优套期保值比例．针对沪深300指数和5支沪深300成分股的实证研究表明,相对改良后的正态假定模型,经本文模型套期保值后的投资组合在风险和收益上均有更好的表现．     

淮河群库优化调度系统的设计与实现

首次提出了采用基于并行计算技术的高性能计算方法进行加优化调度的方法,并采用客户／服务器模式在曙光１０００并行机上设计实现了淮河群库优化调度系统。结果表明：其调度的准确性大大优于常规调度,并缩短了整个决策时间,由此产生的经济效益和社会效益是难以估量的。     

有模具约束的并行机批量流调度问题研究

大多数调度问题均假设产品以单个或整批的方式进行生产,而实际生产过程中,会把产品分批后再进行生产。但当考虑模具约束时,对如何解决产品分批以及制定合理调度方案的问题,本文以最小化最大完工时间为优化目标,建立了考虑模具约束的并行机批量流调度模型,并提出了一种基于遗传算法和差分算法结合的混合差分遗传算法（DEGA）,实现分批与调度两个问题并行优化。最后通过对算例测试,DEGA算法得到更优的解,证明了该算法的优越性和稳定性。结合实际案例,验证了模型和算法的可行性。     

卡特里娜飓风的启示——有关海洋和水利工程的风险分析

2005年卡特里娜（Katrina）和丽塔（Rita）飓风对美国新奥尔良市和佛罗里达东部海岸带来的灾难性破坏,验证了笔者在20世纪80年代初期提出的复合极值分布理论及其对上述海域飓风强度预测结果的正确性。以此为鉴,讨论了海岸、近海、水利和城市防灾工程中引入不确定性分析和多维联合概率理论进行风险分析的必要性。     

深地能源工程热水力多场耦合效应高效模拟方法

深地能源工程多场耦合效应数值模拟面临的主要难题有深部岩体的非线性力学特性、复杂的赋存环境、以及围岩-结构尺寸相差悬殊的计算瓶颈。针对深地能源工程井筒结构独特的细长型几何特点，将井筒简化为一维线单元，考虑井筒内流体沿井筒轴向的渗流传热，而井筒内流体与周围岩体的换热过程则采用等效换热系数近似考虑，套管和砂浆层的影响包含在等效换热系数中，利用该方法实现了远场尺度深地能源工程热-水-力多场耦合效应的高效模拟。依托中国京津冀及周边地区典型水热型地热田群井系统开展案例研究，不仅证明了该文所提出的数值模拟方法可兼顾百平方千米尺度深地能源工程多场耦合效应模拟分析的精度与效率，还揭示了深部热储温度场、渗流场、变形场的长期演化特征。     

Transient inter-production scheduling based on Petri nets and constraint programming

In this article, we focus on the transient inter-production scheduling problem between two cyclic productions in the framework of flexible manufacturing systems. This problem is first formulated as a reachability problem in timed Petri nets (TPN), then solved using a methodology based on constraint programming. Our work is based on the controlled executions proposed by Chretienne to model the sequence of transition firing dates. Our methodology is based on a preliminary resolution of the state equation between initial and final states in the underlying non-TPN. Then, we choose a duration T _max corresponding to the maximal duration time of the scheduling. For each solution S of the state equation, we build a controlled execution from the sequence of firings in S. After the propagation of firing date constraints and reachability constraints in the TPN, we use constraint programming to enumerate the set of feasible controlled executions.     

Two-stage stochastic programming with robust constraints for the logistics network post-disruption response strategy optimization

Logistics networks (LNs) are essential for the transportation and distribution of goods or services from suppliers to consumers. However, LNs with complex structures are more vulnerable to disruptions due to natural disasters and accidents. To address the LN post-disruption response strategy optimization problem, this study proposes a novel two-stage stochastic programming model with robust delivery time constraints. The proposed model jointly optimizes the new-line-opening and rerouting decisions in the face of uncertain transport demands and transportation times. To enhance the robustness of the response strategy obtained, the conditional value at risk (CVaR) criterion is utilized to reduce the operational risk, and robust constraints based on the scenario-based uncertainty sets are proposed to guarantee the delivery time requirement. An equivalent tractable mixed-integer linear programming reformulation is further derived by linearizing the CVaR objective function and dualizing the infinite number of robust constraints into finite ones. A case study based on the practical operations of the JD LN is conducted to validate the practical significance of the proposed model. A comparison with the rerouting strategy and two benchmark models demonstrates the superiority of the proposed model in terms of operational cost, delivery time, and loading rate.     

Generation self-scheduling with partial information on the probability distribution of prices

Recent research has shown that generation self-scheduling in electricity markets can be approached using conditional value-at-risk (CVaR). This study considers a worst-case CVaR methodology applicable to cases where only partial information on the underlying probability distribution of prices is given. In particular, the probability distribution is considered under box and ellipsoidal uncertainty structures. It is shown that both structures result in self-scheduling problems that can be formulated as a quadratic cone program. The cone program can be used to (i) compute the worst-case conditional robust profit with probability level β and (ii) optimise the self-schedule for a pre-specified probability β of the corresponding worst-case conditional robust profit. Simulation results are used to demonstrate the self-scheduling model based on the worst-case CVaR. The usefulness of the proposed model is established by contrasting it with the CVaR approach.     

Models and optimisation approaches for scheduling steelmaking–refining–continuous casting production under variable electricity price

This paper studies the steelmaking–refining–continuous casting (SRCC) scheduling problem with considering variable electricity price (SRCCSPVEP). SRCC is one of the critical production processes for steel manufacturing and energy intensive. Combining the technical rules used in iron-steel production practice, time-dependent electricity price is considered to reduce the electricity cost and the associate production cost. A decomposition approach is proposed for the SRCCSPVEP. Without considering the electrical factor, the first phase applies the mathematical programming method to determine the relative schedule plan for each cast. In the second phase, we formulate a scheduling problem of all casts subject to resource constraint and time-dependent electricity price. A heuristic algorithm combined with the constraint propagation is developed to solve this scheduling problem. To investigate and measure the performance of the proposed approach, numerous instances are randomly generated according to the collective data from a well-known iron-steel plant in China. Computational results demonstrate that our algorithm is very efficient and effective in providing high-quality scheduling plans, and the electricity cost can be reduced for the iron-steel plant.     

Simulated annealing-based optimal wind-thermal coordination scheduling

The rise of environmental protection and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating wind energy sources into existing power systems. Development of better wind-thermal coordination algorithms is necessary to determine the optimal proportion of wind generator capacity that can be integrated into the system for operating an isolated hybrid power system reliably and efficiently. A stochastic searching technique, which utilises a simulated annealing (SA) approach combined with an efficient constrained dynamic economic dispatch (CDED) method, is developed to coordinate the wind and thermal generation scheduling in isolated power systems with large integration of wind capacity. The SA algorithm is used for the scheduling of the generating units, whereas a direct search method routine carries out the ramp rate CDED when considering wind power generation. Several technique constraints are applied to determine the maximum proportion of wind generator capacity that can be integrated into the system. A constraint satisfaction technique for generating feasible neighbouring solution is also developed to improve the SA solution process. Numerical experiments are included to understand the wind generator capacity in the operating cost analysis and to provide valuable information for both the operational and planning problems     

Hydro energy management optimization in a deregulated electricity market

When electricity prices were regulated, hydropower optimization often considered only the inflow uncertainty. In a deregulated electricity market, price uncertainty must be also considered in addition to inflow uncertainty. This makes the operation problem more challenging due to inclusion of the objective of minimizing risk. It also makes the objective function nonlinear while the estimation of risk is problematic. For dealing with uncertainty, a set of finite scenarios of inflow or price sequences may form the evolution of information over the stages that are used in optimization algorithms. Such implicit optimization methods can be seen as an extension of deterministic optimization. A disadvantage is the number of scenarios may grow exponentially in multi-stage optimization problems, even with only a few branches at each stage. An explicit method, denoted as the Fletcher-Ponnambalam model (FP), has been recently developed for the first and second moments of the storage state distributions in terms of moments of the inflow distributions. This method provides statistical information on the nature of random behaviour of the system state variables without any discretization and hence suitable for multireservoir problems. Also not considering scenarios makes it computationally inexpensive, as there is little growth to the size of the original problem. In this paper, we introduce the price uncertainty into the FP model; our results indicate that the method could achieve optimum policy considering also the reduction of risk, using the second moment information. Our study is for medium term operations of a single reservoir. The results are compared with corresponding results from simulation and where possible, with the well-known Benders' Decomposition method (BD).     

An energy-efficient scheduling and rescheduling method for production and logistics systems†

Scheduling can be defined as the allocation of available resources over time while optimising a set of criteria like early completion time of task, holding inventory, etc. The complexity of the scheduling problem, already known to be high, increases if dynamic events and disruptions are considered. In addition, in production and logistics, designers of scheduling systems must consider sustainability-related expectations. This paper presents an energy-efficient scheduling and rescheduling method (named Green Rescheduling Method, GRM). GRM aims at the solving of the dynamic scheduling problem under the condition of a certain level of routing flexibility enabling the reassignment of tasks to new resources. The key performance indicators integrated into the proposed GRM are effectiveness and efficiency-oriented. Applications concern the domains of production and logistics. In order to assess the proposed approach, experimentations have been made and results illustrate the applicability of GRM to build efficient and effective scheduling and rescheduling both for flexible manufacturing systems and inventory distribution systems in a physical internet network. A mathematical formulation for flexible job shop problem with energy consumption is also proposed using mixed Integer programming to evaluate the performance of the predictive part of GRM.     

A tabu search algorithm for simultaneous machine/AGV scheduling problem

Machines and automated guided vehicles (AGVs) scheduling problems are two essential issues that need to be addressed for the efficiency of the overall production system. The purpose of this paper is to study the simultaneous scheduling problem of machines and AGVs in a flexible manufacturing system (FMS) since the global optimum cannot be reached by considering each of them individually. In this paper, a mixed integer linear programming (MILP) model is developed with the objective of makespan minimisation. The MILP model consists of the following two constraint sets: machines and AGVs scheduling sub-problems. As both sub-problems are known to be NP-hard, a heuristic algorithm based on tabu search (TS) is proposed to get optimal or near to optimal solution for large-size problems within reasonable computation time. The proposed algorithm includes a novel two-dimensional solution representation and the generation of two neighbour solutions, which are alternately and iteratively applied to improve solutions. Moreover, an improved lower bound calculation method is introduced for the large-size problems. Computational results show the superior performance of the TS algorithm for the simultaneous scheduling problem.     

A desired load distribution model for scheduling of unrelated parallel machines

Scheduling problems concern the allocation of limited resources over time among both parallel and sequential activities. Load balancing has been adopted as an optimization criterion for several scheduling problems. However, in many practical situations, a load-balanced solution may not be feasible or attainable. To deal with this limitation, this paper presents a generic mathematical model of load distribution for resource allocation, called desired load distribution (DLD). The objective is to develop a DLD model for scheduling of unrelated parallel machines that can be used both in centralized resource management settings and in agent-based distributed scheduling systems. The paper describes the proposed DLD model in details, presents a dynamic programming based optimization algorithm for the proposed model, and then discusses its application to agent-based distributed scheduling.     

Modelling and scheduling analysis of multi-cluster tools with residency constraints based on time constraint sets

This paper is dedicated to the scheduling problem of multi-cluster tools with process module residency constraints and multiple wafer product types. The problem is formulated as a non-linear programming model based on a set of time constraint sets. An effective algorithm called the time constraint sets based (TCSB) algorithm is presented as a new method to schedule the transport modules to minimise the makespan of a number of wafers. In approach, time constraint sets are maintained for all the resources and necessary operations to exploit the remaining production capacities during the scheduling process. To validate the proposed algorithm on a broader basis, a series of simulation experiments are designed to compare our TCSB algorithm with the benchmark with regard to cluster factor, configuration flexibilities and the variation of the processing times and residency constraint times. The results indicate that the proposed TCSB algorithm gives optimal or near optimal scheduling solutions in most cases.     

考虑经济性的多微电网优化调度研究

多微电网并网方式主要有单独并网方式和联合协调并网方式,这2种并网方式下微电网内部的微电源工作状态不同导致发电成本不同。为了减少发电成本,在满足相关微电源约束条件的前提下,建立一种考虑经济性的多微电网能量优化调度模型和单微电网的优化调度模型。由于传统人工鱼群算法精度低,收敛速度慢,提出一种基于模拟退火算法改进的人工鱼群（the artificial fish swarm algorithm based on simulated annealing algorithm,SA_AFSA）算法,该算法克服了传统鱼群算法的缺点。结合具体算例,通过该算法来求解单微电网和多微电网经济性优化调度问题,并比较2种并网方式下的发电成本。实验结果表明,在仅考虑经济性的前提下,多微电网联合协调并网的发电成本低于单微电网单独并网。     

Catastrophe loss modelling of storm-surge flood risk in eastern England

Probabilistic catastrophe loss modelling techniques, comprising a large stochastic set of potential storm-surge flood events, each assigned an annual rate of occurrence, have been employed for quantifying risk in the coastal flood plain of eastern England. Based on the tracks of the causative extratropical cyclones, historical storm-surge events are categorized into three classes, with distinct windfields and surge geographies. Extreme combinations of "tide with surge" are then generated for an extreme value distribution developed for each class. Fragility curves are used to determine the probability and magnitude of breaching relative to water levels and wave action for each section of sea defence. Based on the time-history of water levels in the surge, and the simulated configuration of breaching, flow is time-stepped through the defences and propagated into the flood plain using a 50 m horizontal-resolution digital elevation model. Based on the values and locations of the building stock in the flood plain, losses are calculated using vulnerability functions linking flood depth and flood velocity to measures of property loss. The outputs from this model for a UK insurance industry portfolio include "loss exceedence probabilities" as well as "average annualized losses", which can be employed for calculating coastal flood risk premiums in each postcode.     

Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy

The re-entrant flow shop scheduling problem considering time windows constraint is one of the most important problems in hard-disc drive (HDD) manufacturing systems. In order to maximise the system throughput, the problem of minimising the makespan with zero loss is considered. In this paper, evolutionary techniques are proposed to solve the complex re-entrant scheduling problem with time windows constraint in manufacturing HDD devices with lot size. This problem can be formulated as a deterministic Fm?|?fmls, rcrc, temp?|?C_max problem. A hybrid genetic algorithm was used for constructing chromosomes by checking and repairing time window constraints, and improving chromosomes by a left-shift heuristic as a local search algorithm. An adaptive hybrid genetic algorithm was eventually developed to solve this problem by using fuzzy logic control in order to enhance the search ability of the genetic algorithm. Finally, numerical experiments were carried out to demonstrate the efficiency of the developed approaches.     

Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions

Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. To improve the probability of system successfully completing the next mission, maintenance action is carried out on components during the breaks. In this work, a selective maintenance model with stochastic maintenance quality for multi-component systems is investigated. At each scheduled break, a set of maintenance actions with different degrees of impact are available for each component. The impact of a maintenance action is assumed to be random and follow an identified probability distribution. The corresponding maintenance cost and time are modelled based on the expected impact of the maintenance action. The objective of selective maintenance scheduling is to find the cost-optimal maintenance action for each component at every scheduled break subject to reliability and duration constraints. A simulated annealing algorithm is used to solve the complicated optimisation problem where both multiple maintenance actions and stochastic quality model are taken into account. Two illustrative numerical examples and a real case study have been solved to demonstrate the performance of the proposed approach. A comparison with deterministic maintenance shows the importance of considering the proposed stochastic quality in selective maintenance scheduling.