基于差分进化算法的动态环境经济电力系统调度优化

电力系统动态环境经济调度优化隶属于非线性优化问题范畴,并具有多目标、高维、多约束条件等特点。经 典的数学规划方法无法处理此类复杂问题。为此,提出了新的方法来解决这个问题。首先,通过代价惩罚因子将双目 标优化问题转化为单目标优化问题。然后,设计启发式搜索策略来解决调度问题中的爬坡约束、动态电力平衡约束。 采用启发式策略修正解决方案,能够提高群体的多样性,拓展搜索空间。基于优先列表的启发式策略能够使能耗低的 火力发电机拥有更高的优先级进行更多的电力输出,以得到更优的调度解决方案。最后,改进差分进化算法,以加快 搜索的速度并提高解决方案的质量。     

A bi-objective genetic algorithm for intelligent rehabilitation scheduling considering therapy precedence constraints

The rehabilitation inpatients in hospitals often complain about the service quality due to the long waiting time between the therapeutic processes. To enhance service quality, this study aims to propose an intelligent solution to reduce the waiting time through solving the rehabilitation scheduling problem. In particular, a bi-objective genetic algorithm is developed for rehabilitation scheduling via minimizing the total waiting time and the makespan. The conjunctive therapy concept is employed to preserve the partial precedence constraints between the therapies and thus the present rehabilitation scheduling problem can be formulated as an open shop scheduling problem, in which a special decoding algorithm is designed. We conducted an empirical study based on real data collected in a general hospital for validation. The proposed approach considered both the hospital operational efficiency and the patient centralized service needs. The results have shown that the waiting time of each inpatient can be reduced significantly and thus demonstrated the practical viability of the proposed bi-objective heuristic genetic algorithm.     

基于差分进化算法多处理机任务调度研究

本文主要基于现代启发式差分算法讨论多处理机调度,多处理机调度是NP组合优化问题,目前多采用启发算法。差分进化算法是最近提出的进化算法,主要根据父代个体之间矢量差构造下一代,是一种全局优化搜索方式。本文考虑采用差分进化矢量优先级模型描述调度顺序进行调度,与模拟退火算法比较得到较好调度结果。     

Robust berth scheduling at marine container terminals via hierarchical optimization

In this paper, we present a mathematical model and a solution approach for the discrete berth scheduling problem, where vessel arrival and handling times are not known with certainty. The proposed model provides a robust berth schedule by minimizing the average and the range of the total service times required for serving all vessels at a marine container terminal. Particularly, a bi-objective optimization problem is formulated such that each of the two objective functions contains another optimization problem in its definition. A heuristic algorithm is proposed to solve the resulting robust berth scheduling problem. Simulation is utilized to evaluate the proposed berth scheduling policy as well as to compare it to three vessel service policies usually adopted in practice for scheduling under uncertainty.     

Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects

The dynamic economic dispatch (DED), with the consideration of valve-point effects, is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. In this paper, three chaotic differential evolution (CDE) methods are proposed based on the Tent equation to solve DED problem with valve-point effects. In the proposed methods, chaotic sequences are applied to obtain the dynamic parameter settings in DE. Meanwhile, a chaotic local search (CLS) operation for solving DED problem is designed to help DE avoiding premature convergence effectively. Finally, in order to handle the complicated constraints with efficiency, new heuristic constraints handling methods and feasibility based selection strategy are embedded into the proposed CDE methods. The feasibility and effectiveness of the proposed CDE methods are demonstrated for two test systems. The simulation results reveal that, compared with DE and those other methods reported in literatures recently, the proposed CDE methods are capable of obtaining better quality solutions with higher efficiency.     

An improved self-adaptive PSO technique for short-term hydrothermal scheduling

This paper presents an improved self-adaptive particle swarm optimization algorithm (ISAPSO) to solve hydrothermal scheduling (HS) problem. To overcome the premature convergence of particle swarm optimization (PSO), the evolution direction of each particle is redirected dynamically by adjusting the two sensitive parameters of PSO in the evolution process. Moreover, a new strategy is proposed to handle the various constraints of HS problem in this paper. The results solved by this proposed strategy can strictly satisfy the constraints of HS problem. Finally, the feasibility and effectiveness of proposed ISAPSO algorithm is validated by a test system containing four hydro plants and an equivalent thermal plant. The results demonstrate that the proposed ISAPSO can get a better solution in both robustness and accuracy while compared with the other methods reported in this literature.     

改进的差分进化算法求解炼钢-连铸动态调度问题

为了求解炼钢-连铸动态调度问题,提出了一种将拉格朗日插值算法与差分进化算法相融合得到的改进的差分进化算法。改进后的差分进化算法通过自适应调整进化参数,动态的调整差分进化的方向,并结合拉格朗日插值来优化差分进化算法的局部搜索能力,引入权重系数对全局搜索和局部搜索加以平衡。针对国内某大型钢厂的实际生产数据建立实验模型,以最小化总完工时间、最小化总断浇时间、最小化炉次间总等待时间和最小化总偏差量时间为目标,将改进的差分进化算法应用于求解炼钢-连铸转炉出现故障的动态扰动事件调度问题,实验结果表明,改进的差分进化算法应用在炼钢-连铸动态调度问题上,有效的缩短了炉次加工总完工时间、炉次间总等待时间和总断浇时间,在合理范围内,有效控制了新生产的调度计划与原始调度计划的时间偏差量,避免了因扰动事件的发生而引起连铸机断浇。     

提前/延期惩罚最小化互替机床调度算法

对经提前/延期（E/T）惩罚最小为目标的互替机床调度问题进行了分析，对互替机床E/T调度问题进行了描述，提出了解决调度问题的具体策略，在此基础上，建立了基于启发式的互替机床E/T调度算法，最后通过仿真实验验证了本算法的有效性和实用性。     

Minimizing total flow time for the non-permutation flow shop scheduling problem with learning effects and availability constraints

The flow shop scheduling problem is an attractive subject in the field of scheduling, which has attracted the attention of many researchers in the past five decades. In this paper, the non-permutation flow shop scheduling problem with the learning effects and machine availability constraints has been studied for minimizing the total flow time as a performance measure. First, a mixed integer linear programming model has been proposed for the modeling of the problem and then, an effective improving heuristic method, which is able to find proper non-permutation solutions, has been presented. Finally, the computational results are used for evaluation the performance and effectiveness of the proposed heuristic.     

Unit commitment problem using enhanced particle swarm optimization algorithm

This paper proposes an enhanced PSO (EPSO) approach to solve the unit commitment (UC) problem in electric power system, which is an integrated improved discrete binary particle swarm optimization (DBPSO) with the Lambda-iteration method. The EPSO is enhanced by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. The implementation of EPSO for UC problem consists of three stages. First, the DBPSO based on priority list is applied for unit scheduling when neglecting the minimum up/down time constraints. Second, heuristic search strategies are used to handle the minimum up/down time constraints and decommit excess spinning reserve units. Finally, Lambda-iteration method is adopted to solve economic load dispatch based on the obtained unit schedule. To verify the advantages of the EPSO method, the EPSO is tested and compared to the other methods on the systems with the number of units in the range of 10 to 100. Numerical results demonstrate that the EPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time.     

Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.     

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

The emergence of Cloud Computing as a model of service provisioning in distributed systems instigated researchers to explore its pros and cons on executing different large scale scientific applications, i.e., Workflows. One of the most challenging problems in clouds is to execute workflows while minimizing the execution time as well as cost incurred by using a set of heterogeneous resources over the cloud simultaneously. In this paper, we present, Budget and Deadline Constrained Heuristic based upon Heterogeneous Earliest Finish Time (HEFT) to schedule workflow tasks over the available cloud resources. The proposed heuristic presents a beneficial trade-off between execution time and execution cost under given constraints. The proposed heuristic is evaluated for different synthetic workflow applications by a simulation process and comparison is done with state-of-art algorithm i.e. BHEFT. The simulation results show that our proposed scheduling heuristic can significantly decrease the execution cost while producing makespan as good as the best known scheduling heuristic under the same deadline and budget constraints.     

Flexible open shop scheduling problem to minimize makespan

This study investigates the static and dynamic versions of the flexible open shop scheduling problem with the goal of minimizing makespan. The asymptotic optimality of the general dense scheduling (GDS) algorithm is proven by the boundedness hypothesis. For large-scale problems, the GDS-based heuristic algorithms are presented to accelerate convergence. For moderate-scale problems, the differential evolution algorithm is employed to obtain high-quality solutions. A series of random experiments are conducted to demonstrate the effectiveness of the proposed algorithms.     

Scheduling of short-term hydrothermal energy system by parallel multi-objective differential evolution

With the growing concerns on energy and environment, the short-term hydrothermal scheduling (SHTS) which minimizes the fuel cost and pollutant emission simultaneously is playing an increasing important role in the modern electric power system. Due to the complicated operation constraints and objectives, SHTS is classified as a multi-objective optimization problem. Thus, to efficiently resolve this problem, this paper develops a novel parallel multi-objective differential evolution (PMODE) combining the merits of parallel technology and multi-objective differential evolution. In PMODE, the population with larger size is first divided into several smaller subtasks to be concurrently executed in different computing units, and then the main thread collects the results of each subpopulation to form the final Pareto solutions set for the SHTS problem. During the evolutionary process of each subpopulation, the mutation crossover and selection operators are modified to enhance the performance of population. Besides, an external archive set is used to conserve the Pareto solutions and provide multiple evolutionary directions for individuals, while the constraint handling method is introduced to address the complicated operational constraints. The results from a mature hydrothermal system indicate that when compared with several existing methods, PMODE can obtain satisfactory solutions in both fuel cost and environmental pollutant, which is an effective tool to generate trade-off schemes for the hydrothermal scheduling problem.     

差分进化粒子群算法求解发电资源调度问题

为了有效地解决水火电力系统资源短期优化调度问题,提出了一种基于差分进化粒子群的调度算法。设计了水火电力系统资源调度问题的数学模型,给出了差分进化粒子群优化算法的框架,通过PSO种群和DE种群之间的信息交流机制以寻求全局最优位置,从而使算法具有动态自适应性,能够较容易地跳出局部最优。实验结果表明,该算法能有效解决水火发电资源调度问题,具有较好的应用价值。     

Hybrid heuristic search approach for deadlock-free scheduling of flexible manufacturing systems using Petri nets

Based on the Petri net models of flexible manufacturing systems (FMSs), this paper focuses on deadlock-free scheduling problem with the objective of minimizing the makespan. Two hybrid heuristic search algorithms for solving such scheduling problems of FMSs are proposed. To avoid deadlocks, the deadlock control policy is embedded into heuristic search strategies. The proposed algorithms combine the heuristic best-first strategy with the controlled backtracking strategy based on the execution of the Petri nets. The scheduling problem is transformed into a heuristic search problem in the reachability graph of the Petri net, and a schedule is a transition sequence from the initial marking to the final marking in the reachability graph. By using the one-step look-ahead method in the deadlock control policy, the safety of a state in the reachability graph is checked, and hence, deadlock is avoided. Experimental results are provided and indicate the effectiveness of the proposed hybrid heuristic search algorithms in solving deadlock-free scheduling problems of FMSs. Especially, the comparison against previous work shows that both new algorithms are promising in terms of solution quality and computing times.     

A simulation‐based differential evolution algorithm for stochastic parallel machine scheduling with operational considerations

We consider a parallel machine scheduling problem with the objective of minimizing two types of costs: the cost related to production operations and the cost related to due date performances. The former could be reduced by reasonable settings of the operational variables (e.g., the number of workers, the frequency of maintenance), while the latter could be reduced by appropriate scheduling of the production process. However, the optimization of both targets is significantly complicated by the influence of human factors that play a dominant role in real‐world manufacturing systems. To cope with this issue, a simulation‐based optimization framework is adopted in this paper for obtaining high‐quality robust solutions to the integrated scheduling problem. Meanwhile, differential evolution, a metaheuristic algorithm based on swarm intelligence, is applied for a systematic search of the huge solution space. Finally, numerical computations are conducted to verify the effectiveness of the proposed approach. Sensitivity analysis and practical implications are also presented.     

A constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling

Scheduling activities in concurrent product development process is of great sig-nificance to shorten developements lead time and minimize the cost.Moreover,it can eliminate the unnecessary redesign periods and guarantee that serial activities can be executed as concurrently as possible,This paper presents a constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling.In the combined approack,the neural network is used to obtain a feasible starting time of all the activities based on sequence constraints ,the heuristic algorithm is used to obtain a feasible solution of the scheduling problem based on resource constrainsts.The feasible scheduling solution is obtained by a gradient optimization function .Sim-ulations have shown that the proposed combined approach is efficient and fasible with respect to concurrent activities scheduling.     

A Constraint Satisfaction Neural Network and Heuristic Combined Approach for Concurrent Activities Scheduling

Scheduling activities in concurrent product development process is of great significance to shorten development lead time and minimize the cost. Moreover, it can eliminate the unnecessary redesign periods and guarantee that serial activities can be executed as concurrently as possible. This paper presents a constraint satisfaction neural network and heuristic combined approach for concurrent activities scheduling. In the combined approach, the neural network is used to obtain a feasible starting time of all the activities based on sequence constraints, the heuristic algorithm is used to obtain a feasible solution of the scheduling problem based on resource constraints. The feasible scheduling solution is obtained by a gradient optimization function. Simulations have shown that the proposed combined approach is efficient and feasible with respect to concurrent activities scheduling.     

A novel chaotic differential evolution hybridized with quadratic programming for short-term hydrothermal coordination

In this paper, a viable global optimizer based on chaotic differential evolution is hybridized with sequential quadratic programming, an efficient local search technique to exploit short-term hydrothermal coordination (STHTC) involved for power generation and its efficient management. A multi-objective optimization framework is established for minimizing the total cost of thermal generators with valve point loading effects satisfying power balance constraint as well as generator operating and hydrodischarge limits, respectively. The proposed model is implemented on various systems comprising hydrogenerating units as well as different thermal units. The results are compared with state-of-the-art heuristic techniques recently employed on STHTC problems, while the reliability, stability and effectiveness of the proposed framework are validated through the comprehensive analysis of Monte Carlo simulations.