有整数限制的运输问题

经典的运输问题是一个线性规划模型。本文讨论了把产地运输到销地的物资数量限制为非负整数时的运输问题，从理论上证明了这种有整数限制的运输问题模型可以转化为相应的线性规划模型来求解，有效地降低了计算难度。     

乘用车物流运输装载模型研究

对乘用车物流运输计划问题进行了研究,首先,建立了以单辆轿运车实载率最大为目标的单车最佳装载方案模型,以该方案配送乘用车辆直到运输任务完成以获得初始轿运车数,其次以空载率最小为目标设计了数量调整模型对初始轿运车数进行优化,得到最优装载方案模型.基于上述优化模型计算最小的轿运车数,建立在轿运车数量限制下的最短行驶里程模型,选择总行驶里程最小的装载方案作为最优的配送方案.最后建立基于路径的物流运输装载模型,运用Floyd算法计算任意起讫点间的最短路径,设计了一种全局搜索算法得到一种合理的配送方案,并以空载率最小为目标对方案进行优化.     

与运输路线有关的货物装载模型与算法

本文提出了一种同时考虑选择优化的运输路线的货物装载模型。该模型运用递归和试验式的方法以及选择最短运输路线的思想，得到在选定型号的运输工具上以装载量尽可能大以及运输路线为最短的运输装载方案。数值实验结果表明，该模型仍较大的应用价值。     

一类运输问题的启发式算法

本文讨论一类运输问题,并对这类问题给出启发式算法。     

考虑交货期的生产运输联合优化问题研究

结合企业实际场景研究了考虑交货期的多个工厂、多条生产线、单一产品的生产与运输联合优化问题.已知客户订单需求量和交货时间窗,考虑了各条生产线在不同时段的生产能力约束,在满足交货时间窗约束的前提下,以生产、存储、运输费用之和极小化为目标建立了生产与运输联合优化问题的混合整数规划模型,通过分析模型结构证明了在不考虑固定生产成...     

求解整数规划代理对偶的一个新方法

考虑如下的整数线性规划问题: (P)min Cx, s.tAx≥b, x≥0,且为整数向量,其中c,b是具有适当维数的行向量或列向量,A是已知的矩阵,c的分量均为正数,且假定(P)是可行的,x是n维变量。 用V(·)表示优化问题(·)的最优值。如果对x放弃整数限制要求,问题(P)的线     

凸整数规划最优点的判定条件

本文得到判别已知可行整值点为凸整数规划最优解的一个充分条件,此条件只涉及目标函数在该整值点为中心的边长为2的超立方体上的性态.     

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".     

邻域整点搜索法求解整数规划

从剖析线性规划的优化机理入手,将纯整数规划分为标准型和非标型两类.首先以标准型纯整数规划为突破口,提出一种新的解法,并在理论上加以证明,然后将其拓广延伸,用于求解非标准型纯整数规划和混合整数规划.这种新解法命名为松驰最优解邻域整点搜索法,属于常规解法,但在简捷高效方面,远胜过现有的两种常规解法—分枝定界法和割平面法.     

Optimal Loading of Tankers

This paper describes a method for maximising payloads carried by a fleet of road tankers. The tankers carry a range of products in multicompartment tanks. A case study demonstrates the method's potential to increase payload and remove the risk of overload penalties.     

Combined Designs and Labour Programming

In this paper, the problem of labour scheduling is solved using Combinatorial analysis with Combined designs; characteristic applications to scheduling industrial labour, itinerary distribution and supervision are given.     

多目标半定规划的加权中心路径法

对于线性型多目标半定规划问题，引进加权中心路径的概念，并利用单目标半定规划的中心路径法，提出了求解多目标半定规划问题的加权中心路径法，先得型对一个叔向量的有效解，然后在此基础上，提出了通过一次迭代得到对应一定范围内其他任意权向量的有效解的一步修正方法．     

线性规划无穷多最优解的讨论

利用线性规划单纯形表对线性规划原问题存在无穷多最优解和对偶问题存在无穷多最优解的情况进行了讨论,并分析了对偶问题存在无穷多最优解情况下的影子价格的方向性。最后以实例说明了各种情况。对初学者加深理解及决策者决策参考有一定帮助     

Time Optimal Path Planning for Industrial Robots: A Dynamic Programming Approach Considering Torque Derivative and Jerk Constraints

This paper presents a dynamic programming approach for calculating time optimal trajectories for industrial robots, subject to various physical constraints. In addition to path velocity, motor torque, joint velocity and acceleration constraints, the present contribution also shows how to deal with torque derivative and joint jerk limitations. First a Cartesian path for the endeffector is defined by splines using Bernstein polynomials as basis functions and is parameterized via a scalar path parameter. In order to compute the belonging quantities in configuration space, inverse kinematics is solved numerically. Using this and in combination with the dynamical model, joint torques as well as their derivatives can be constrained. For that purpose the equations of motion are calculated with the help of the Projection Equation. As a consequence of the used optimization problem formulation, the dynamical model as well as the restrictions have to be transformed to path parameter space. Due to the additional consideration of jerk and torque derivative constraints, the phase plane is expanded to a phase space. The parameterized restrictions lead to feasible regions in this space, in which the optimal solution is sought. Result of the optimization is the time behavior of the path parameter and subsequently the feed forward torques for the optimal motion on the spatial path defined by previously mentioned splines. Simulation results as well as experimental results for a three axes industrial robot are presented. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Programming for Optimal Decisions.

Journal of the Operational Research Society -     

Stochastic Optimal Control and Linear Programming Approach

We study a classical stochastic optimal control problem with constraints and discounted payoff in an infinite horizon setting. The main result of the present paper lies in the fact that this optimal control problem is shown to have the same value as a linear optimization problem stated on some appropriate space of probability measures. This enables one to derive a dual formulation that appears to be strongly connected to the notion of (viscosity sub) solution to a suitable Hamilton-Jacobi-Bellman equation. We also discuss relation with long-time average 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．     

Scaled Optimal Path Trust-Region Algorithm

Trust-region algorithms solve a trust-region subproblem at each iteration. Among the methods solving the subproblem, the optimal path algorithm obtains the solution to the subproblem in full-dimensional space by using the eigenvalues and eigenvectors of the system. Although the idea is attractive, the existing optimal path method seems impractical because, in addition to factorization, it requires either the calculation of the full eigensystem of a matrix or repeated factorizations of matrices at each iteration. In this paper, we propose a scaled optimal path trust-region algorithm. The algorithm finds a solution of the subproblem in full-dimensional space by just one Bunch–Parlett factorization for symmetric matrices at each iteration and by using the resulting unit lower triangular factor to scale the variables in the problem. A scaled optimal path can then be formed easily. The algorithm has good convergence properties under commonly used conditions. Computational results for small-scale and large-scale optimization problems are presented which show that the algorithm is robust and effective.