救灾物资军用车辆合理调度方法研究与仿真

研究救灾物资军用车辆高效调度问题.灾区环境复杂,部分路段容易发生意外阻塞,这种灾区路径阻塞在路径和拥堵放向都是不定的,存在较大随机性和突发性,造成后期调度模型的路径选取存在突发性错误.传统算法多是基于车辆调度模型进行调度的,如果路径选取出现随机突发性错误,导致车辆调度效率降低.提出了一种蚁群信息反馈算法的救灾物资军用车辆调度方法.计算路段阻塞系数,并将其反馈到调度端,利用蚁群极值方法选取最优路径.实验证明,改进算法能够避免路径突发性阻塞造成的的缺陷,提高了救灾物资军用车辆的调度效率.     

基于满意大规模含整变量多目标决策方法

针对具有块角结构的大规模含整变量多目标决策问题，提出一种基于目标满意度的交互式分解协调方法。定义了若干概念，给出了若干定理，介绍了交互式决策方法的具体步骤，并对其软件实现作了说明。     

考虑心理代价的灾民疏散及救灾物资分派问题研究

在灾后灾民疏散和救灾物资调运过程中,灾民的心理恐慌影响到整个救灾行动的开展。考虑灾民在受灾点等待疏散以及在安置点内等待救灾物资的两种心理代价对救灾工作的影响,以救灾经济成本和灾民心理惩罚成本最小化为目标,建立一个灾民疏散及救灾物资调运的混合整数规划模型,确定灾后短期内的物资供应地、灾民疏散和救灾物资的调运计划,并用一个具体的算例验证所提出的模型。结果表明,救灾行动中应该充分考虑灾民的恐慌心理。     

一种求解整数规划与混合整数规划非线性罚函数方法

证明了任何一个变量有界的整数规划问题（IP）和混合整数规划问题（MIP）都可以转化为一个等价的非整数（或连续化）规划问题（NIP）,并给出一个用非线性精确罚函数法来求解该等价NIP的方法,从而达到求解IP或MIP的目的,数值实验表明了算法的可行性。该方法可广泛用于各应用领域里IP和MIP的求解,特别是为非线性IP和MIP问题提供了一条通用 的求解途径,对解决许多实际优化问题具有重要意义。     

最小化决策规则集的计算方法

在决策算法中，并不是所有的决策规则都是必要的，一些过剩的决策规则应该消去，而不影响作决策，因此，研究最小化决策规则集的计算方法是很有意义的．传统的决策算法并没有给出最小化决策规则集的形式化计算方法，为了解决最小化决策规则集的形式化计算问题，引入了最小化决策规则可辨识矩阵概念，提供了基于可辨识矩阵的基本决策规则的最小化决策规则集的计算方法．     

基于改进模糊决策粗糙集的最小化决策代价特征选择算法

模糊决策粗糙集是决策粗糙集理论在模糊集环境下的重要延伸,然而该模型对含噪声的数据不具有很好的容忍性。为此在传统的模糊相似关系中引入一个限定阈值,提出一种改进的模糊相似关系。在其基础上对原始的模糊决策粗糙集进行重构,提出一种改进的模糊决策粗糙集模型。根据不同的特征选择方式,利用所提出的改进模型设计出两种搜索策略的最小化决策代价特征选择算法。实验分析表明,该算法比传统算法具有更高的优越性。     

CDMA有限精度序列解相关NP-hard问题的求解方法

该文首先分析了应用有限精度序列为解相关矩阵序列的解相关接收机,将有限精度解相关的多用户检测问题归约为线性约束整数优化问题,同时证明此问题为NP－hard问题。然后给出了用于寻找最优有限精度序列即求解此NP－hard问题的算法。结果说明,最优有限精度解相关器的性能甚至在大的信道占用时较无限精度解相关多用户检测器下降很小。     

基于满意度大规模含整变量多目标决策方法

针对具有块角结构的大规模含整变量多目标决策问题，提出一种基于目标满意度的交互式分解协调方法。定义了若干概念，给出了若干定理，介绍了交互式决策方法的具体步骤，并对其软件实现作了说明。     

不完备邻域决策粗糙集的最小化代价属性约简算法

目前的决策粗糙集研究主要集中在完备离散型信息系统,很少有对不完备连续型数据进行研究,考虑这一问题,提出一种不完备邻域决策粗糙集模型。首先在不完备连续型数据中引入了不完备邻域关系,然后利用该二元关系对传统的决策粗糙集进行重构,一种称之为不完备邻域决策粗糙集的模型被提出,同时基于决策代价原则,进一步地提出了最小化决策代价的属性约简算法。最后通过实验表明了所提出的算法具有更高的属性约简性能。     

混流生产线的排产方法

1　引言混流固定比例流水线 (简称为混流生产线 )是一种广泛应用于冰箱、电热水器等家用电器生产的流水线生产方式 ,其排产问题是指确定在一定时间段上 (通常在冰箱行业中为一周或两周 )各条生产线上各种产品的生产数量 .混流生产线的主要特点是多种产品在多条流水线上同时生产     

Vehicle routing and resource distribution in postdisaster humanitarian relief operations

After a disaster, supplies must be efficiently and equitably distributed to those in need, wounded persons must be evacuated to triage centers, and relief workers must be transported to affected areas. This complex humanitarian relief problem requires the coordination of numerous vehicles of varying capacities to transport goods, disaster victims, and volunteer workers through a network of roads, some of which may be impassable. To address this problem, a detailed mathematical programming model is presented. Owing to the complexity of this formulation, only small‐scale problem instances may be solved optimally via commercial solver software. Therefore, a new heuristic approach is proposed to solve problems of practical size within acceptable time restrictions. The performance of the heuristic is evaluated for numerous representative test instances.     

灾后应急物资保障的多目标定位—联运模型

为了将应急物资快速准确地配送至灾区, 在考虑灾后应急物资保障阶段性特征的基础上, 构建了一个以应急物资未满足率最小、系统总时间最短以及系统总成本最小为目标的多周期混合整数规划模型, 模型考虑了应急配送中心定位及多种类应急物资联运等问题。采用GAMS软件编程, 并用分层序列法求解多目标规划。以“4·20”四川芦山地震为例验证模型的可行性和有效性, 结果表明该模型能有效地解决灾后应急物资保障的定位—联运问题。     

RAIN:一种面向软硬件和门限实现的轻量分组密码算法

RAIN算法的设计基于国际上分组密码设计广泛采用的SPN(substitution permutation network)结构,通过迭代混淆层S盒和扩散层字混合提供强雪崩效应,不仅保证强的安全性,还兼顾了软硬件实现.算法支持64b分组和128b分组,2种不同的分组长度采用相同的轮函数结构实现,方案简洁优美.混淆层采用4b的S盒实现,在S盒实现的时候不仅考虑了其安全性,还考虑S盒的软硬件实现,与扩散层的混合运算结合提供高的实现性能.从差分分析、不可能差分分析、积分攻击和不变子空间分析4个方面对算法进行了自评估,在分析的过程中使用了一些最新的分析方法以及基于MILP(mixed integer linear programming)的自动化搜索等,结果显示：算法可以抵抗现有的分析方法,并且具有较大的安全冗余.RAIN算法软硬件实现效率高,在PC机、ARM平台和硬件FPGA(field programmable gate array)平台下都具有出色的实现性能.算法S盒可以转换为基本逻辑运算,抗侧信道攻击实现代价低.     

The progressive party problem: Integer linear programming and constraint programming compared

Many discrete optimization problems can be formulated as either integer linear programming problems or constraint satisfaction problems. Although ILP methods appear to be more powerful, sometimes constraint programming can solve these problems more quickly. This paper describes a problem in which the difference in performance between the two approaches was particularly marked, since a solution could not be found using ILP.The problem arose in the context of organizing a progressive party at a yachting rally. Some yachts were to be designated hosts; the crews of the remaining yachts would then visit the hosts for six successive half-hour periods. A guest crew could not revisit the same host, and two guest crews could not meet more than once. Additional constraints were imposed by the capacities of the host yachts and the crew sizes of the guests.Integer linear programming formulations which included all the constraints resulted in very large models, and despite trying several different strategies, all attempts to find a solution failed. Constraint programming was tried instead and solved the problem very quickly, with a little manual assistance. Reasons for the success of constraint programming in this problem are identified and discussed.     

Control structure selection for energy integrated distillation column

This paper treats a case study on control structure selection for an almost binary distillation column. The column is energy integrated with a heat pump in order to transfer heat from the condenser to the reboiler. This integrated plant configuration renders the possible control structures somewhat different from what is usual for binary distillation columns. Further the heat pump enables disturbances to propagate faster through the system. The plant has six possible actuators of which three must be used to stabilize the system. Hereby three actuators are left for product purity control. An MILP screening method based on a linear state space model is used to determine economically optimal sets of controlled and manipulated variables. The generated sets of inputs and outputs are analysed with frequency dependent relative gain array (RGA), relative disturbance gain (RDG) and condition number (CN) to determine the best structure in terms disturbance rejection and setpoint tracking. The pairing and controller design are implemented and evaluated through nonlinear simulation. The suggested control structure is also qualitatively compared to a control structure applied experimentally.     

A Cut-Based Algorithm for the Nonlinear Dual of the Minimum Cost Network Flow Problem

We consider a convex, or nonlinear, separable minimization problem with constraints that are dual to the minimum cost network flow problem. We show how to reduce this problem to a polynomial number of minimum s,t-cut problems. The solution of the reduced problem utilizes the technique for solving integer programs on monotone inequalities in three variables, and a so-called proximity-scaling technique that reduces a convex problem to its linear objective counterpart. The problem is solved in this case in a logarithmic number of calls, O(log U), to a minimum cut procedure, where U is the range of the variables. For a convex problem on n variables the minimum cut is solved on a graph with O(n²) nodes. Among the consequences of this result is a new cut-based scaling algorithm for the minimum cost network flow problem. When the objective function is an arbitrary nonlinear function we demonstrate that this constrained problem is solved in pseudopolynomial time by applying a minimum cut procedure to a graph on O(nU) nodes.     

Multiple Choice Programming: A State-of-the-Art Review

Multiple choice programming, originated from Healy Jr, belongs to the non-convex mathematical programming with special ordered sets of variables type 1 as identified by Beale and Tomlin. The high applicability of multiple choice programming in many decision-making problems has made it a focus of many OR researchers and practitioners in the fields of integer programming and combinatories. We report a survey on the structures of multiple choice programming problems and their solution approaches. Applications of multiple choice programming and some prospective future researches are also discussed.