非同类机极小化最大完工时间的保密排序问题

提出并研究了一类非同类机的极小化最大完工时间的保密排序问题Rm||Cmax.该问题的模型参数分为若干组,每个组都由一个不愿意共享或公开自己数据的单位所拥有.基于随机矩阵变换构造了一个不泄露私有数据且与原问题等价的安全规划模型,求解该安全模型可以获得问题的最优解,而且各单位的隐私数据仍然保持不被泄露.     

光突发交换网络中最长队列优先调度组装机制

提出了光突发交换网络中的最长队列优先调度（LQF-SA）机制,将边缘节点的突发组装和突发调度两种技术紧密结合在一起.在现实网络中,业务通常是非均匀分布的,即在某些源宿对之间存在更大的业务流量.仿真结果表明,LQF-SA机制可以很好地完成非均匀分布业务流的组装.即使在均匀分布业务流输入下,LQF-SA机制在突发大小分布、组装效率和突发丢失率等方面也优于已提出的轮询调度组装（RR-SA）机制.     

“仓店一体化”模式下订单拣选与配送集成调度优化

针对“仓店一体化”模式下提供限时送达服务的新零售仓店,研究多拣货员、多客户、配送方式为带限时送达约束的路径优化分批配送情形下的订单拣选与配送集成调度问题。以最小化订单最大履行时间和配送成本为目标,构建了混合整数非线性规划模型,并设计两阶段启发式算法(H-2)进行求解,最后通过数值实验对算法进行验证与分析。数值实验结果表明,H-2具有较高的求解质量;相较于传统调度算法(TS)在效率提升、资源节约以及客户满意度提高方面具有更优的表现,进而为新零售仓店管理者提供决策支持。     

基于时间窗延迟的资源约束项目调度双层优化研究

本研究从业主—承包商交互的视角构建了一种RCPSP(resource-constrained project scheduling problem)双层优化模型,即在可更新资源约束条件下,项目双方如何进行交互决策达到双方NPV(Net present value)最大化的目标。首先对研究问题进行界定,构建资源约束下的max-NPV项目调度双层优化模型;然后利用延迟优先规则设计了一种基于时间窗延迟的嵌套式自适应遗传算法来求解该模型,以达到双方NPV最大化;最后用一个算例验证算法的有效性,同时通过PSPLIB数值实验说明算法的稳定性,并分析关键参数对项目双方收益的影响。研究结果为项目进程的安排以及奖励机制的设计提供依据,以提高双方利益。     

两台自私型机器上自私工件排序的PoA紧界

本文研究了两台自私型机器上有自私型工件的关于二元均衡的排序问题。对任意工件序列$L$, 证明了二元均衡排序的PoA的紧界为$\frac{8}{7}$。如果工件尺寸在区间$[1, r](r\ge1)$内, 得到了二元均衡排序的PoA的紧界为关于$r$的分段线性函数。     

两台恒速机上的MapReduce排序算法研究

研究源自于MapReduce系统的一类排序问题。给定两台恒速机和一组按列表到达的工件，每个工件包含两类任务：Map Task和Reduce Task。假设Map任务和Reduce任务都是不可中断的，Map任务可以并行处理，即可以任意分割成若干小的任务并在两台机器上同时处理，而Reduce任务只可以在单台机器上处理。一旦工件到达，必须为其指派机器和开工时间，目标是使得最后完工时间最小。对LSc算法的竞争比进行了分析，得到其一般情形下的竞争比当$s\geq（1+\sqrt{5}）/2$时为$1+1/s$，否则为$1+s/（s+1）$。而当每个工件$J_j$都满足其Map任务总长大于等于Reduce任务总长时，其竞争比当$s\geq（1+\sqrt{3}）/2$时不超过$1+1/（2s）$，否则为不超过$1+s/（2s+1）$。     

Mathematical models for job-shop scheduling problems with routing and process plan flexibility

As a result of rapid developments in production technologies in recent years, flexible job-shop scheduling problems have become increasingly significant. This paper deals with two NP-hard optimization problems: flexible job-shop scheduling problems (FJSPs) that encompass routing and sequencing sub-problems, and the FJSPs with process plan flexibility (FJSP-PPFs) that additionally include the process plan selection sub-problem. The study is carried out in two steps. In the first step, a mixed-integer linear programming model (MILP-1) is developed for FJSPs and compared to an alternative model in the literature (Model F) in terms of computational efficiency. In the second step, one other mixed-integer linear programming model, a modification of MILP-1, for the FJSP-PPFs is presented along with its computational results on hypothetically generated test problems.     

110警车配置及巡逻方案

针对110警车调度问题,引入了图论中的最短路算法以及计算几何的相关理论,建立了车辆调配模型、巡逻路线模型以及基于模糊数学的评价指标模型.另外,用C++编写了一个可视化的软件,不仅实现了手动描点,自动求出覆盖线段集合的功能,同时利用计算机模拟警车的巡逻路线,最后通过计算机检验得到结果,其合理性和实用性都令人满意.针对问题一,通过人机结合,配置17辆警车就能实现D1的目标,很好地兼顾了警车巡逻的运行成本,减少公安部门车辆和人员等的投入.针对问题二,采用模糊数学相关理论使评价指标实现了从定性到定量的转变.针对问题三和六,在D1的基础上,兼顾了巡逻效果的显著性,采用最少被巡逻道路优先的贪心算法建立了动态巡逻模型,得到了合理的巡逻方案.在此方案中,我们动用了30辆警车完成了问题一的目标.另外还额外考虑了案发事件概率不均匀分布的情况,建立了改进模型.针对问题四,在完成问题三指标的基础上,为了尽可能提高巡逻车辆的隐蔽性和增强巡逻效果,采用轮盘赌算法来引入随机性.针对问题五,采用最远距离道路优先贪心策略,使模型尽可能满足条件D1,D2.针对问题七,提出了一些额外因素及其解决方案,进一步完善了模型,使模型更贴近现实.     

Bicriteria problems to minimize maximum tardiness and due date assignment cost in various scheduling environments

We study bicriteria problems of minimizing maximum tardiness and total due date assignment cost in various scheduling environments. We assume that each job can be assigned a different due date without any restriction, and that each due date assignment cost is a non-decreasing function of the quoted due date. We settle the complexity of most of the problems studied by either proving that they are NP-hard or finding a polynomial time solution for them. We also include approximation and non-approximability results for several parallel-machine problems.     

New Bundle Methods for Solving Lagrangian Relaxation Dual Problems

Bundle methods have been used frequently to solve nonsmooth optimization problems. In these methods, subgradient directions from past iterations are accumulated in a bundle, and a trial direction is obtained by performing quadratic programming based on the information contained in the bundle. A line search is then performed along the trial direction, generating a serious step if the function value is improved by or a null step otherwise. Bundle methods have been used to maximize the nonsmooth dual function in Lagrangian relaxation for integer optimization problems, where the subgradients are obtained by minimizing the performance index of the relaxed problem. This paper improves bundle methods by making good use of near-minimum solutions that are obtained while solving the relaxed problem. The bundle information is thus enriched, leading to better search directions and less number of null steps. Furthermore, a simplified bundle method is developed, where a fuzzy rule is used to combine linearly directions from near-minimum solutions, replacing quadratic programming and line search. When the simplified bundle method is specialized to an important class of problems where the relaxed problem can be solved by using dynamic programming, fuzzy dynamic programming is developed to obtain efficiently near-optimal solutions and their weights for the linear combination. This method is then applied to job shop scheduling problems, leading to better performance than previously reported in the literature.