Openshop scheduling under linear resources constraints

Consider n jobs (J₁, …, J_n), m working stations (M₁, …, M_m) and λ linear resources (R₁, …, R_λ). Job J_i consists of m operations (O_i1, …, O_im). Operation O_ij requires P_k(i, j) units of resource R_k to be realized in an M_j. The availability of resource R_k and the ability of the working station M_h to consume resource R_k, vary over time. An operation involving more than one resource consumes them in constant proportions equal to those in which they are required. The order in which operations are realized is immaterial. We seek an allocation of the resources such that the schedule length is minimized. In this paper, polynomial algorithms are developed for several problems, while NP-hardness is demonstrated for several others. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 51–66, 1998     

A self‐adapting genetic algorithm for project scheduling under resource constraints

This papers deals with the classical resource‐constrained project scheduling problem (RCPSP). There, the activities of a project have to be scheduled subject to precedence and resource constraints. The objective is to minimize the makespan of the project. We propose a new heuristic called self‐adapting genetic algorithm to solve the RCPSP. The heuristic employs the well‐known activity list representation and considers two different decoding procedures. An additional gene in the representation determines which of the two decoding procedures is actually used to compute a schedule for an individual. This allows the genetic algorithm to adapt itself to the problem instance actually solved. That is, the genetic algorithm learns which of the alternative decoding procedures is the more successful one for this instance. In other words, not only the solution for the problem, but also the algorithm itself is subject to genetic optimization. Computational experiments show that the mechanism of self‐adaptation is capable to exploit the benefits of both decoding procedures. Moreover, the tests show that the proposed heuristic is among the best ones currently available for the RCPSP. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 433–448, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10029     

基于共进化的多任务分配与调度

在考虑多种约束关系基础上 ,提出一种并行与分布式系统中多约束关系的任务分配与调度的共同进化遗传算法。仿真试验结果表明所给算法比传统单种遗传算法更能有效地进行多任务分配与调度 ,具有一定的工程价值     

Optimal control policies in service systems with limited information on the downstream stage

Existing models in multistage service systems assume full information on the state of downstream stages. In this paper, we investigate how much the lack of such information impacts jobs' waiting time in a two‐stage system with two types of jobs at the first stage. The goal is to find the optimal control policy for the server at the first stage to switch between type‐1 and type‐2 jobs, while minimizing the long‐run average number of jobs in the system. We identify control policies and corresponding conditions under which having no or partial information, the system can still capture the most benefit of having full information.     

Scheduling with variable time slot costs

In this article, we study a class of new scheduling models where time slot costs have to be taken into consideration. In such models, processing a job will incur certain cost which is determined by the time slots occupied by the job in a schedule. The models apply when operational costs vary over time. The objective of the scheduling models is to minimize the total time slot costs plus a traditional scheduling performance measure. We consider the following performance measures: total completion time, maximum lateness/tardiness, total weighted number of tardy jobs, and total tardiness. We prove the intractability of the models under general parameters and provide polynomial‐time algorithms for special cases with non‐increasing time slot costs.© 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

天地测控资源调度的两阶段递进遗传算法

为对天地测控资源调度进行高效求解,设计了两阶段递进遗传算法对其进行优化调度。鉴于问题的目标具有一定的可分性,以时间为依据将被调度弧段划分成两个部分,在对第一部分形成种群并作进化求解的基础上,将其最优解与第二部分弧段组合,并作进一步的进化求解。为了缓解两阶段优化的局部搜索特性,在第一阶段个体适应度计算中,以一定概率设定虚拟弧段,保留具有潜在全局优势的个体。仿真表明该方法能在确保求解质量的前提下明显减少运算时间。     

大型工程项目集成化管理环境下的知识协同与任务调度

将各个项目单元抽象为不同的知识源,详细分析了集成化管理环境下的知识协同问题,提出了基于权威性加权原则的知识协同处理策略,并就策略修正、协同求解等问题作了分析说明。在此基础上,采取前向调度算法和逆向调度算法相结合的迭代算法,建立了协同工作中的任务调度模型,算例证明,模型能够有效满足各约束条件下任务的增删需求。     

Transporting jobs through a two‐machine open shop

We consider the two‐machine open shop scheduling problem in which the jobs are brought to the system by a single transporter and moved between the processing machines by the same transporter. The purpose is to split the jobs into batches and to find the sequence of moves of the transporter so that the time by which the completed jobs are collected together on board the transporter is minimal. We present a ‐approximation algorithm. © 2008 Wiley Periodicals, Inc. Naval Research Logistics 2009     

基于LMI的不确定性无尾飞行器鲁棒变增益控制器设计

无尾式飞行器是飞行器发展的方向,研究无尾式控制具有重要意义.研究了线性变参数系统的增益调度控制器的设计方法,采用多胞形进行增益调度,提出了一种简单实用的变参数顶点凸分解方法,该方法在保证系统稳定的情况下,确保系统达到最优性能指标.同时还充分考虑了系统的不确定性因素,利用线性矩阵不等式(LMI)对系统进行鲁棒控制器设计,大大减少了计算量以及对系统的约束,设计出了基于LMI的增益调度控制器,通过非线性仿真结果可以看出,该控制器在调节变量变化很大的情况下,使得系统在0.5s内收敛,而且超调量很小,论证了该方法在无尾式飞控系统中应用的可行性.