对带有盒约束的二次整数规划的一种线性化方法

本文主要讨论了二次整数规划问题的线性化方法.在目标函数为二次函数的情况下,我们讨论了带有二次约束的整数规划问题的线性化方法,并将文献中对二次0-1问题的研究拓展为对带有盒约束的二次整数规划问题的研究.最终将带有盒约束的二次整数规划问题转化为线性混合本文主要讨论了二次整数规划问题的线性化方法.在目标函数为二次函数的情况下,我们讨论了带有二次约束的整数规划问题的线性化方法,并将文献中对二次0-1问题的研究拓展为对带有盒约束的二次整数规划问题的研究.最终将带有盒约束的二次整数规划问题转化为线性混合0-1整数规划问题,然后利用Ilog-cplex或Excel软件中的规划求解工具进行求解,从而解决原二次整数规划.     

整数规划新进展

整数规划是对全部或部分决策变量为整数的最优化问题的模型、算法及应用等的研究, 是运筹学和管理科学中应用最广泛的优化模型之一. 首先简要回顾整数规划的历史和发展进程, 概述线性和非线性整数规划的一些经典方法. 然后着重讨论整数规划若干新进展, 包括0-1二次规划的半定规划~(SDP)~松弛和随机化方法, 带半连续变量和稀疏约束的优化问题的整数规划模型和方法, 以及0-1二次规划的协正锥规划表示和协正锥的层级半定规划~(SDP)~逼近. 最后, 对整数规划未来研究方向进行展望并对一些公开问题进行讨论.     

整数规划中割平面法的研究

割平面法是求解整数规划问题常用方法之一.用割平面法求解整数规划的基本思路是:先用单纯形表格方法去求解不考虑整数约束条件的松弛问题的最优解,如果获得的最优解的值都是整数,即为所求,运算停止.如果所得最优解不完全是整数,即松弛问题最优解中存在某个基变量为非整数值时,就从最优表中提取出关于这个基变量的约束等式,再从这个约束式出发构造一个割平面方程加入最优表中,再求出新的最优解,这样不断重复的构造割平面方程,直到找到整数解为止.主要研究以下四个关键点:一是研究从最优表中提取出的、关于基变量的约束等式出发,通过将式中的系数进行整数和非负真分数的分解,从而得到一个小于等于0的另外一个不等式的推导过程;二是总结出从小于等于0的那个约束不等式出发构造割平面方程的四种方法;三是分析构造割平面方程的这四种方法相互之间的区别和联系;四是探讨割平面法的几何意义.通过对这四个方面的分析和研究,对割平面法进行透彻的剖析,使读者能够全面把握割平面法.     

线性规划标准型和整数线性规划最优解的两个注记

本文研究线性规划标准型的基本假设所蕴含的一些性质,并探讨整数线性规划最优解和其松弛问题最优解的关系.首先,分别讨论四种情形下线性规划最优解的性质,即无约束线性规划问题、仅有非负约束的线性规划问题、仅有等式约束的线性规划问题,以及标准线性规划问题系数矩阵的列向量有为零的情形等.然后,构造两族二维整数线性规划,其松弛问题的最优解与其(整数)最优解"相距甚远".     

多供应商单配送中心的库存优化模型研究

针对有容量约束的多供应商单配送中心的库存问题,通过对影响物流运作成本各因素的分析,确定各变量之间的关系;并结合企业运作实际,首先构建了混合整数非线性规划的数学模型;进而对该模型进行优化分析,提出了最优解的解决方法;最后,运用算例对该模型做进一步的分析总结.     

一类混合0-1非凸二次约束二次规划问题的近似算法

研究一类混合0-1非凸二次约束二次规划问题的近似算法.该问题是在M个非凸二次约束与一个基数约束下,求解一个n维向量的极小范数,变量包含M个0-1变量与一个n维连续向量.该问题是NP-难的.在求解其半正定规划(SDP)松弛问题的基础上,提出了一种随机舍入算法,能够得到原始的问题的一个可行解.数值仿真实验结果表明该方法是十分有效的.     

一类系数为梯形模糊数的整数规划

介绍了模糊数学和整数规划的背景、现状、以及发展趋势,并以模糊结构元理论定义了梯形模糊加权序,进一步证明了模糊整数规划模型的最优解等价于整数规划模型的最优解,再利用整数规划模型的最优解的求解方法求解模糊整数规划模型的最优解,最后,通过算例验证方法的可行性.     

多无人机对组网雷达的协同干扰问题研究

在现代战场中,航迹欺骗干扰技术是针对组网雷达提出的协同干扰技术,旨在利用多架无人机在同一时刻对组网雷达中的部分雷达实施相互关联的虚假目标欺骗干扰.充分考虑了实际情况下无人机飞行模态、飞行速度、飞行高度的约束限制,对雷达的位置分布以及虚假航迹线进行可视化,并将三维空间模型投影到二维平面后进行建模分析.主要针对一个虚拟的无人机航迹欺骗干扰问题,建立时空模型,通过构建可达交互矩阵和雷达扫描算法,结合0-1整数规划,得到无人机协同飞行的最优策略.     

N车探险问题的一种ε-近似度的近似算法

本文探讨了一类N车探险问题的近似算法,首先通过建模将N车问题转变为一个等价的非线性0-1混合整数规划问题,进而将该非线性0-1混合整数规划问题转化为一个一般的带约束非线性规划问题,并用罚函数的方法将得到的带约束非线性规划问题化为相应的无约束问题.我们证明了可通过求解该无约束非线性规划问题得到原N车问题的ε-近似度的近似解,并设计了-个收敛速度为二阶的迭代箅法,文章最后给出算法实例.     

区间二次双层规划的最好最优解

双层规划问题是一类具有递阶结构的优化问题.在不确定的双层规划优化问题中,目标函数系数或约束条件系数为区间数的双层规划模型在实际问题中有着广泛的应用.在二次-线性双层规划模型的基础上,提出了上、下层目标函数以及约束条件系数均具有区间系数的二次-线性双层规划模型,给出了求解其最好最优解的方法.首先,通过选取约束条件中不同的基矩阵,求得区间二次-线性双层规划的可能最优解.再比较求得的全部可能最优解,便可得到区间二次-线性双层规划模型的最好最优解.最后给出数值算例验证该方法的有效性.     

Zero-one integer programs with few constraints —Lower bounding theory

The zero-one integer programming problem and its special case, the multiconstraint knapsack problem frequently appear as subproblems in many combinatorial optimization problems. We present several methods for computing lower bounds on the optimal solution of the zero-one integer programming problem. They include Lagrangean, surrogate and composite relaxations. New heuristic procedures are suggested for determining good surrogate multipliers. Based on theoretical results and extensive computational testing, it is shown that for zero-one integer problems with few constraints surrogate relaxation is a viable alternative to the commonly used Lagrangean and linear programming relaxations. These results are used in a follow up paper to develop an efficient branch and bound algorithm for solving zero-one integer programming problems.     

Least trimmed squares regression,least median squares regression,and mathematical programming

In this paper, we study LTS and LMS regression, two high breakdown regression estimators, from an optimization point of view. We show that LTS regression is a nonlinear optimization problem that can be treated as a concave minimization problem over a polytope. We derive several important properties of the corresponding objective function that can be used to obtain algorithms for the exact solution of LTS regression problems, i.e., to find a global optimum to the problem. Because of today's limited problem-solving capabilities in exact concave minimization, we give an easy-to-implement pivoting algorithm to determine regression parameters corresponding to local optima of the LTS regression problem. For the LMS regression problem, we briefly survey the existing solution methods which are all based on enumeration. We formulate the LMS regression problem as a mixed zero-one linear programming problem which we analyze in depth to obtain theoretical insights required for future algorithmic and computational work.     

A finitely convergent procedure for facial disjunctive programs

This paper addresses an important class of disjunctive programs called facial disjunctive programs, examples of which include the zero-one linear integer programming problem and the linear complementarity problem. Balas has characterized some fundamental properties of such problems, one of which has been used by Jeroslow to obtain a finitely convergent procedure. This paper exploits another basic property of facial disjunctive programs in order to develop an alternative finitely convergent algorithm.     

Computing exact solution to nonlinear integer programming: Convergent Lagrangian and objective level cut method

In this paper, we propose a convergent Lagrangian and objective level cut method for computing exact solution to two classes of nonlinear integer programming problems: separable nonlinear integer programming and polynomial zero-one programming. The method exposes an optimal solution to the convex hull of a revised perturbation function by successively reshaping or re-confining the perturbation function. The objective level cut is used to eliminate the duality gap and thus to guarantee the convergence of the Lagrangian method on a revised domain. Computational results are reported for a variety of nonlinear integer programming problems and demonstrate that the proposed method is promising in solving medium-size nonlinear integer programming problems.     

Goal programming in the context of the assignment problem and a computationally effective solution method

This paper develops the goal programming technique to solve the multiple objective assignment problem. The required model is formulated and an appropriate solution method is presented. The proposed method, which is a decomposition method, exploits the total unimodularity feature of the assignment problem and effectively reduces the computational efforts. Some issues related to the efficiency of a GP solution are stated and some specialized techniques for detecting and restoring efficiency are proposed.     

A new model and hybrid approach for large scale inventory routing problems

This paper studies an inventory routing problem (IRP) with split delivery and vehicle fleet size constraint. Due to the complexity of the IRP, it is very difficult to develop an exact algorithm that can solve large scale problems in a reasonable computation time. As an alternative, an approximate approach that can quickly and near-optimally solve the problem is developed based on an approximate model of the problem and Lagrangian relaxation. In the approach, the model is solved by using a Lagrangian relaxation method in which the relaxed problem is decomposed into an inventory problem and a routing problem that are solved by a linear programming algorithm and a minimum cost flow algorithm, respectively, and the dual problem is solved by using the surrogate subgradient method. The solution of the model obtained by the Lagrangian relaxation method is used to construct a near-optimal solution of the IRP by solving a series of assignment problems. Numerical experiments show that the proposed hybrid approach can find a high quality near-optimal solution for the IRP with up to 200 customers in a reasonable computation time.     

Convergence of a continuous approach for zero-one programming problems

In this paper a new continuous formulation for the zero-one programming problem is presented, followed by an investigation of the algorithm for it. This paper first reformulates the zero-one programming problem as an equivalent mathematical programs with complementarity constraints, then as a smooth ordinary nonlinear programming problem with the help of the Fischer-Burmeister function. After that the augmented Lagrangian method is introduced to solve the resulting continuous problem, with optimality conditions for the non-smooth augmented Lagrangian problem derived on the basis of approximate smooth variational principle, and with convergence properties established. To our benefit, the sequence of solutions generated converges to feasible solutions of the original problem, which provides a necessary basis for the convergence results.     

A modeling/solution approach for optimal deployment of a weapons arsenal

This paper reports a real-world application of a large-scale assignment/allocation mixed-integer program for optimal deployment and targeting of missiles for the U.S. Strategic Air Command. We provide a NETFORM model that reduces the number of zero-one variables of a standard integer programming formulation by more than two orders of magnitude (by factors approaching 500) and a tailored NETFORM software system that solves problems involving 2,400 zero-one variables and 984,000 continuous variables to within 99.9% of optimality in less than one minute on an IBM 4381.This research was supported in part by the Center for Business Decision Analysis, the Hugh Roy cullen Centennial Chair in Business Administration, and the Office of Naval Research under Contract N00014-87-K-0190. Reproduction in whole or in part is permitted for any purpose of the U.S. Government.     

Exact and approximate methods for a one-dimensional minimax bin-packing problem

One-dimensional bin-packing problems require the assignment of a collection of items to bins with the goal of optimizing some criterion related to the number of bins used or the ‘weights’ of the items assigned to the bins. In many instances, the number of bins is fixed and the goal is to assign the items such that the sums of the item weights for each bin are approximately equal. Among the possible applications of one-dimensional bin-packing in the field of psychology are the assignment of subjects to treatments and the allocation of students to groups. An especially important application in the psychometric literature pertains to splitting of a set of test items to create distinct subtests, each containing the same number of items, such that the maximum sum of item weights across all bins is minimized. In this context, the weights typically correspond to item statistics derived from difficulty and discrimination indices. We present a mixed zero-one integer linear programming (MZOILP) formulation of this one-dimensional minimax bin-packing problem and develop an approximate procedure for its solution that is based on the simulated annealing algorithm. In two comparisons that focused on 34 practically-sized test problems (up to 6000 items and 300 bins), the simulated annealing heuristic generally provided better solutions than were obtained when using a commercial mathematical programming software package to solve the MZOILP formulation directly.     

On the bottleneck assignment problem

In this paper, a new approach for solving the bottleneck assignment problem is presented. The problem is treated as a special class of permutation problems which we call max-min permutation problems. By defining a suitable neighborhood system in the space of permutations and designating certain permutations as critical solutions, it is shown that any critical solution yields a global optimum. This theorem is then used as a basis to develop a general method to solve max-min permutation problems.This work was carried out by the junior author while holding a Purdue University Fellowship.