广义指派问题及其在军事装备运输中的推广应用

军事装备中的运输问题复杂多样,如何建立数学模型是寻求优化方案的关键.本文首先将最优线性指派模型推广到广义指派模型并给出其两种算法,其次对带有时间约束的运输问题进行建模,并设法将其转化为广义指派问题来处理,从而为这类运输问题提供了一种有效可行的算法.     

二次规划逆问题的牛顿方法

针对二次规划逆问题,将其表达为带有互补约束的锥约束优化问题.借助于对偶理论,将问题转化为变量更少的线性互补约束非光滑优化问题.通过扰动的方法求解转化后的问题并证明了收敛性.采用非精确牛顿法求解扰动问题,给出了算法的全局收敛性与局部二阶收敛速度.最后通过数值实验验证了该算法的可行性.     

多态不确定环境下生产与运输方案的优化方法

研究了一类生产运输问题的优化模型,其中产地可供应量、机器可使用最大时间为模糊参数,市场需求和生产单位产品时间随机参数,在产地可供应量,市场需求,预算,产地机器可运转时间,目的地库存空间等约束下,该模型同时优化了生产运输的总成本和运输时间.基于修正后的S型曲线隶属函数和机会约束规划方法,推导了原模型的确定型等价式,并据此设计了寻求满意生产与运输方案的交互式算法。     

带时间限制的最小费用运输问题的求解方法

本文研究了带时间限制的最小费用运输问题。首先分析了运输量与运输时间的关系,并把运输时间划分成两部分,一部分与运输量无关,一部分与运输量有关;进一步根据运输时间与运输量的关系,把带时间限制的最小费用运输问题转化为变量有上界的运输问题,给出了求解该问题的有效算法,并通过实例进行了计算。     

求解一类逆鲁棒优化问题的牛顿型扰动方法

逆优化问题是指通过调整目标函数和约束中的某些参数使得已知的一个解成为参数调整后的优化问题的最优解.本文考虑求解一类逆鲁棒优化问题.首先,我们将该问题转化为带有一个线性等式约束,一个二阶锥互补约束和一个线性互补约束的极小化问题;其次,通过一类扰动方法来对转化后的极小化问题进行求解,然后利用带Armijo线搜索的非精确牛顿法求解每一个扰动问题.最后,通过数值实验验证该方法行之有效.     

基于类Lyapunov函数的非线性切换系统的Reach-While-Stay性质验证

考虑了一类连续时间切换系统在任意切换信号下的Reach-While-Stay性质验证问题,提出了基于类Lyapunov函数的验证方法.首先,利用不变集构建了新的RWS性质判定准则,将RWS性质验证问题转化为关于类Lyapunov函数的非线性约束求解问题,然后运用平方和松弛进行编码,进而将其转化为双线性矩阵不等式问题并应用迭代的半定规划进行求解.最后,通过实例表明了该方法的可行性和有效性.     

混合整数半无限规划问题

本文主要讨论混合整数半无限规划(mixed integer semi-infinite programming, MISIP)问题的求解方法.首先分离内层约束中的连续变量和整数变量并将原问题转化为混合整数互补约束规划(mixed integer mathematical programming with complementarity constraints, MIMPCC)问题.其次在假设内层问题满足Slater约束规范的条件下得到了转化前后问题的等价性.继而分别将MIMPCC问题转化为可用常规优化软件求解的混合整数规划问题和非线性规划问题.由于在转化过程中会生成大量的变量和约束,为求解内层问题中变量较多的MISIP问题,本文提出一种行约束生成法,并证明该算法可在最多O(|Z|)次迭代之后得到最优解.最后通过一些数值实例验证算法的有效性.     

一个基于货物运输的批处理机随机调度模型研究

本文以货物运输为背景新建立了一个批处理机随机调度模型,目的是为了应付货物运输中运输时间的不确定性和货主取货时间的不确定性.首先将模型转化为与其等价的确定优化问题,接着研究给出了确定优化问题的性质,最后基于这些性质给出了一个求解确定优化问题的启发式算法.该问题的解决可望为物流公司等进一步改善服务质量提供了一些理论依据     

线性互补约束规划问题的一种Filter算法

本文研究了求解线性互补约束规划问题的算法问题.首先基于广义互补函数和摄动技术将问题转化为带参数的非线性优化问题,利用SlQP-Filter算法方法,求解线性互补约束规划问题的一种Filter算法.在适当条件下,证明了该算法的全局收敛性.     

运筹学中几种运输问题的求解方法探析

从目前研究生入学考试中出现的几种新的运筹学运输问题出发,探讨了各种运输问题与传统运输问题的差异。提出以传统运输问题为本,将非传统运输问题转化为传统运输问题借助表上作业法求解的思路。并针对6种不同的非传统运输问题分析了转化的过程和步骤,为运输问题的研究提供了新的内容.     

A multi-item mixture inventory model involving random lead time and demand with budget constraint and surprise function

This study deals with a multi-item mixture inventory model in which both demand and lead time are random. A budget constraint is also added to this model. The optimization problem with budget constraint is then transformed into a multi-objective optimization problem with the help of fuzzy chance-constrained programming technique and surprise function. In our studies, we relax the assumption about the demand, lead time and demand during lead time that follows a known distribution and then apply the minimax distribution free procedure to solve the problem. We develop an algorithm procedure to find the optimal order quantity and optimal value of the safety factor. Finally, the model is illustrated by a numerical example.     

A Simple Semi-Implicit Scheme for Partial Differential Equations with Obstacle Constraints

We develop a simple and efficient numerical scheme to solve a class of obstacle problems encountered in various applications. Mathematically, obstacle problems are usually formulated using nonlinear partial differential equations (PDE). To construct a computationally efficient scheme, we introduce a time derivative term and convert the PDE into a time-dependent problem. But due to its nonlinearity, the time step is in general chosen to satisfy a very restrictive stability condition. To relax such a time step constraint when solving a time dependent evolution equation, we decompose the nonlinear obstacle constraint in the PDE into a linear part and a nonlinear part and apply the semi-implicit technique. We take the linear part implicitly while treating the nonlinear part explicitly. Our method can be easily applied to solve the fractional obstacle problem and min curvature flow problem. The article will analyze the convergence of our proposed algorithm. Numerical experiments are given to demonstrate the efficiency of our algorithm.     

Computational Discretization Algorithms for Functional Inequality Constrained Optimization

In this paper, a functional inequality constrained optimization problem is studied using a discretization method and an adaptive scheme. The problem is discretized by partitioning the interval of the independent parameter. Two methods are investigated as to how to treat the discretized optimization problem. The discretization problem is firstly converted into an optimization problem with a single nonsmooth equality constraint. Since the obtained equality constraint is nonsmooth and does not satisfy the usual constraint qualification condition, relaxation and smoothing techniques are used to approximate the equality constraint via a smooth inequality constraint. This leads to a sequence of approximate smooth optimization problems with one constraint. An adaptive scheme is incorporated into the method to facilitate the computation of the sum in the inequality constraint. The second method is to apply an adaptive scheme directly to the discretization problem. Thus a sequence of optimization problems with a small number of inequality constraints are obtained. Convergence analysis for both methods is established. Numerical examples show that each of the two proposed methods has its own advantages and disadvantages over the other.     

Bounded knapsack sharing

A bounded knapsack sharing problem is a maximin or minimax mathematical programming problem with one or more linear inequality constraints, an objective function composed of single variable continuous functions called tradeoff functions, and lower and upper bounds on the variables. A single constraint problem which can have negative or positive constraint coefficients and any type of continuous tradeoff functions (including multi-modal, multiple-valued and staircase functions) is considered first. Limiting conditions where the optimal value of a variable may be plus or minus infinity are explicitly considered. A preprocessor procedure to transform any single constraint problem to a finite form problem (an optimal feasible solution exists with finite variable values) is developed. Optimality conditions and three algorithms are then developed for the finite form problem. For piecewise linear tradeoff functions, the preprocessor and algorithms are polynomially bounded. The preprocessor is then modified to handle bounded knapsack sharing problems with multiple constraints. An optimality condition and algorithm is developed for the multiple constraint finite form problem. For multiple constraints, the time needed for the multiple constraint finite form algorithm is the time needed to solve a single constraint finite form problem multiplied by the number of constraints. Some multiple constraint problems cannot be transformed to multiple constraint finite form problems.     

一些约束规划问题的近似全局最优解

首先将一个具有多个约束的规划问题转化为一个只有一个约束的规划问题,然后通过利用这个单约束的规划问题,对原来的多约束规划问题提出了一些凸化、凹化的方法,这样这些多约束的规划问题可以被转化为一些凹规划、反凸规划问题．最后,还证明了得到的凹规划和反凸规划的全局最优解就是原问题的近似全局最优解．     

Expanded models of the project portfolio selection problem with loss in divisibility

This research develops three new models for the project portfolio selection problem with multiple periods. To reflect some real situations, three loss assumptions are considered for the interruption of project execution for the first time. The mathematical representations of the loss assumptions are provided and proved. Besides, the workload constraint, capital flow constraint, cardinality constraint, and precedence relationship are incorporated into the models. One benchmark example and one real-world application case are used to demonstrate the capability and characteristics of the proposed models.     

A transformation technique for optimal control problems with partially linear state inequality constraints

This paper considers optimal control problems involving the minimization of a functional subject to differential constraints, terminal constraints, and a state inequality constraint. The state inequality constraint is of a special type, namely, it is linear in some or all of the components of the state vector.A transformation technique is introduced, by means of which the inequality-constrained problem is converted into an equality-constrained problem involving differential constraints, terminal constraints, and a control equality constraint. The transformation technique takes advantage of the partial linearity of the state inequality constraint so as to yield a transformed problem characterized by a new state vector of minimal size. This concept is important computationally, in that the computer time per iteration increases with the square of the dimension of the state vector.In order to illustrate the advantages of the new transformation technique, several numerical examples are solved by means of the sequential gradient-restoration algorithm for optimal control problems involving nondifferential constraints. The examples show the substantial savings in computer time for convergence, which are associated with the new transformation technique.This research was supported by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant No. AF-AFOSR-76-3075, and by the National Science Foundation, Grant No. MCS-76-21657.     

Knapsack problem with probability constraints

This paper is dedicated to a study of different extensions of the classical knapsack problem to the case when different elements of the problem formulation are subject to a degree of uncertainty described by random variables. This brings the knapsack problem into the realm of stochastic programming. Two different model formulations are proposed, based on the introduction of probability constraints. The first one is a static quadratic knapsack with a probability constraint on the capacity of the knapsack. The second one is a two-stage quadratic knapsack model, with recourse, where we introduce a probability constraint on the capacity of the knapsack in the second stage. As far as we know, this is the first time such a constraint has been used in a two-stage model. The solution techniques are based on the semidefinite relaxations. This allows for solving large instances, for which exact methods cannot be used. Numerical experiments on a set of randomly generated instances are discussed below.     

基于非均匀参数化的自由终端时间最优控制问题求解

针对自由终端时间最优控制问题,提出了一种基于非均匀控制向量参数化的数值解法.将控制时域离散化为不同长度的时间段,各时间段长度作为新的控制变量.通过引入标准化的时间变量,原问题转化为均匀参数化的固定终端时间最优控制问题.建立目标和约束函数的Hamilton函数,通过求解伴随方程获得目标和约束函数的梯度,采用序列二次规划(SQP)获得数值解.针对两个经典的化工过程自由终端时间最优控制问题进行仿真研究,验证了所提出算法的可行性和有效性.     

Application of the method of multipliers to state space constrained optimal control problems with discrete time

The paper deals with convergence conditions of multiplier algorithms for solving optimal control problems with discrete time suggested by J. Bjbvonek in some earlier papers. In this approach the original state space constrained problem is converted into a control-constrained problem by introducing an additional control variable and an equality constraint which is taken into consideration by a multiplier method. Convergence conditions for the multiplier Iteration of global and local nature are given for exact and inexact solution of the subproblems.