多重因素约束下的网格检查对策问题研究

基于物品数量及每列容量等限制因素,构造局中人的可行策略集合;考虑隐藏成本,处罚规则与检查成功概率等因素,构造相应的支付函数,建立多重因素约束下的网格检查对策模型.根据矩阵对策性质,将对策论问题转化为非线性整数规划问题,利用H(o|¨)lder不等式获得实数条件下的规划问题的解,然后转化为整数解,得到特定条件下的模型的对策值及局中人的最优混合策略.最后,给出一个实例,说明上述模型的实用性及方法的有效性.     

考虑共享不确定因素的应急设施最大覆盖选址模型

为提升应急设施的服务质量和抵御中断风险的能力,研究应急设施最大覆盖选址-分配决策问题。扩展无容量限制的固定费用的可靠性选址决策模型,建立考虑共享不确定因素的应急设施最大覆盖选址优化模型,通过在目标和约束中引入budget不确定集刻画共享不确定因素,基于Bertsimas和Sim鲁棒优化方法建立混合整数规划模型,并将非线性问题转化为易于求解的鲁棒等价模型,利用带混沌搜索策略的改进灰狼优化算法求解模型,并对不确定鲁棒水平和中断概率进行敏感性分析。最后通过案例及数据仿真结果的对比分析,验证了模型的合理性和有效性,并给出最优的选址分配布局。     

基于遗传算法的多约束网格检查对策问题研究

综合考虑物品数量以及列容量约束,将隐藏成本与检查概率引入支付函数,建立一种新的多约束的网格检查对策模型.根据矩阵对策性质及Hlder不等式,将对策论问题转化为非线性整数规划问题.提出一个基于遗传算法的模型求解方法,将归一化处理得到的变量进行二进制编码,通过数据变换将问题转化为无约束问题,采用轮盘赌选择、多点交叉及单点变异操作求解模型.仿真结果表明了模型及所提算法的有效性.     

基于贝叶斯信息更新的失事飞机发现概率模型

了目标搜索区域的确定方法以及失事飞机在目标搜索区域的初始概率分布,得到发现概率的计算公式。以发现概率为目标,构造了一个求解最优搜寻策略的Max Max化规划模型,模型可以动基于贝叶斯方法,提出了一个失事飞机的发现概率模型,利用飞机失联前后的信息数据,给出态地对坠机点的概率分布进行更新,使下一步搜寻任务得到及时的修正和调整。考虑到洋流对坠机点的影响,本文还提出了一个关于基点先验概率分布的重构策略。此外,对任务搜索区域最优路径的选取问题做了进一步探讨,给出了一个任务搜索区域上搜寻路径的选取方法     

不确定运行时间环境下的车辆调度问题及启发式算法

考虑到物流公司或者配送中心车辆实际运行过程中时间的不确定性,提出了配送服务线路包含时间窗口、车辆容量约束的随机规划模型,以最小化车辆运行成本同时尽可能降低所服务顾客的不满意度.同时,又稍作改进给出了平均-风险模型,由于VRP问题是NP难的,给出了一种基于禁忌搜索的启发式算法,并以北京市13个点的为例,给出求解结果.     

基于遗传算法和非线性规划的设备预防维修周期优化模型

本文通过建立预防性维修前后故障率的关系,给出了带约束的非线性设备预防性维修策略模型.该模型通过综合考虑故障维修成本、预防性维修成本、以及生产损失成本,在有限的生产运行时间内使得系统总成本最小化.模型利用遗传算法的全局搜索能力和非线性规划的局部搜索能力进行求解.计算结果表明,遗传算法结合非线性规划可以以较快的收敛速度达到全局最优.     

一个新的最优模糊存储模型

提出一个新的具有积压定单的关于模糊订购量的模糊存储模型.在模糊函数原理下,给出了模糊总存储成本.为了寻找最优解,把最优模糊存储模型转化为双目标最优化模型,利用L ingo8.0求解不等式约束问题,我们发现最优解都是确定的实数.此外,当模糊订购量和模糊总需求都是三角形(或权重均为1/2梯形)模糊数时,我们提出模型的最优解与经典的具有积压定单存储模型具有相同的结果.     

一种求解非线性约束优化问题的无罚函数无滤子的方法

借助于强次可行方向法的思想和滤子法的思想,给出了一种求解非线性约束优化问题的无罚函数无滤子的方法.方法借助于广义投影技术产生搜索方向,直接通过原目标函数和约束违反度函数作为搜索函数来产生步长,有效地避免了消耗计算成本的恢复阶段.最后在适当的假设条件下,给出了算法的全局收敛性和有效性.     

求解带有交易费的CVaR投资组合模型的L-S算法

本文假设投资者是风险厌恶型,用CVaR作为测量投资组合风险的方法.在预算约束的条件下,以最小化CVaR为目标函数,建立了带有交易费用的投资组合模型.将模型转化为两阶段补偿随机优化模型,构造了求解模型的随机L-S算法.为了验证算法的有效性,用中国证券市场中的股票进行数值试验,得到了最优投资组合、VaR和CVaR的值.而且对比分析了有交易费和没有交易费的最优投资组合的不同,给出了相应的有效前沿.     

一类带约束min-max-min问题的区间算法

构造了求解一类带不等式约束的min-max-min问题的区间算法,其中目标函数和约束函数都是一阶连续可微函数,证明了方法的收敛性,给出了数值算例.该方法可以同时求出问题的最优值和全部全局最优解,是有效和可靠的.     

An Exact Penalty Function Method for Continuous Inequality Constrained Optimal Control Problem

In this paper, we consider a class of optimal control problems subject to equality terminal state constraints and continuous state and control inequality constraints. By using the control parametrization technique and a time scaling transformation, the constrained optimal control problem is approximated by a sequence of optimal parameter selection problems with equality terminal state constraints and continuous state inequality constraints. Each of these constrained optimal parameter selection problems can be regarded as an optimization problem subject to equality constraints and continuous inequality constraints. On this basis, an exact penalty function method is used to devise a computational method to solve these optimization problems with equality constraints and continuous inequality constraints. The main idea is to augment the exact penalty function constructed from the equality constraints and continuous inequality constraints to the objective function, forming a new one. This gives rise to a sequence of unconstrained optimization problems. It is shown that, for sufficiently large penalty parameter value, any local minimizer of the unconstrained optimization problem is a local minimizer of the optimization problem with equality constraints and continuous inequality constraints. The convergent properties of the optimal parameter selection problems with equality constraints and continuous inequality constraints to the original optimal control problem are also discussed. For illustration, three examples are solved showing the effectiveness and applicability of the approach proposed.     

Epsilon-Ritz Method for Solving a Class of Fractional Constrained Optimization Problems

In this paper, epsilon and Ritz methods are applied for solving a general class of fractional constrained optimization problems. The goal is to minimize a functional subject to a number of constraints. The functional and constraints can have multiple dependent variables, multiorder fractional derivatives, and a group of initial and boundary conditions. The fractional derivative in the problem is in the Caputo sense. The constrained optimization problems include isoperimetric fractional variational problems (IFVPs) and fractional optimal control problems (FOCPs). In the presented approach, first by implementing epsilon method, we transform the given constrained optimization problem into an unconstrained problem, then by applying Ritz method and polynomial basis functions, we reduce the optimization problem to the problem of optimizing a real value function. The choice of polynomial basis functions provides the method with such a flexibility that initial and boundary conditions can be easily imposed. The convergence of the method is analytically studied and some illustrative examples including IFVPs and FOCPs are presented to demonstrate validity and applicability of the new technique.     

A General Vehicle Routing Problem

In this paper, we study a rich vehicle routing problem incorporating various complexities found in real-life applications. The General Vehicle Routing Problem (GVRP) is a combined load acceptance and generalised vehicle routing problem. Among the real-life requirements are time window restrictions, a heterogeneous vehicle fleet with different travel times, travel costs and capacity, multi-dimensional capacity constraints, order/vehicle compatibility constraints, orders with multiple pickup, delivery and service locations, different start and end locations for vehicles, and route restrictions for vehicles. The GVRP is highly constrained and the search space is likely to contain many solutions such that it is impossible to go from one solution to another using a single neighbourhood structure. Therefore, we propose iterative improvement approaches based on the idea of changing the neighbourhood structure during the search.     

Using conical partition to globally maximizing the nonlinear sum of ratios

This article presents a global optimization algorithm for globally maximizing the sum of concave–convex ratios problem with a convex feasible region. The algorithm uses a branch and bound scheme where a concave envelope of the objective function is constructed to obtain an upper bound of the optimal value by using conical partition. As a result, the upper-bound subproblems during the algorithm search are all ordinary convex programs with less variables and constraints and do not grow in size from iterations to iterations in the computation procedure, and furthermore a new bounding tightening strategy is proposed such that the upper-bound convex relaxation subproblems are closer to the original nonconvex problem to enhance solution procedure. At last, some numerical examples are given to vindicate our conclusions.     

带策略约束的区间数双矩阵博弈的双线性规划求解方法

传统区间数双矩阵博弈理论研究局中人支付值为区间数的策略选择问题,但没有考虑局中人策略选择可能受到各种约束.创建一种求解局中人策略选择受约束且支付值为区间数的双矩阵博弈（简称带策略约束的区间数双矩阵博弈）的简单、有效的双线性规划求解方法.首先,将局中人的博弈支付看作支付值区间中数值的函数.通过证明这种函数具有单调性,据此利用支付值区间的上、下界,构造了一对辅助双线性规划模型,可分别用于显式地计算任意带策略约束的区间数双矩阵博弈中局中人区间数博弈支付的上、下界及其相应的最优策略.最后,利用考虑策略约束条件下企业和政府针对发展低碳经济策略问题的算例,通过比较其与不考虑策略约束情形下的结果,说明了提出的模型和方法的有效性、优越性及可应用性.     

Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions

We present a new strategy for the constrained global optimization of expensive black box functions using response surface models. A response surface model is simply a multivariate approximation of a continuous black box function which is used as a surrogate model for optimization in situations where function evaluations are computationally expensive. Prior global optimization methods that utilize response surface models were limited to box-constrained problems, but the new method can easily incorporate general nonlinear constraints. In the proposed method, which we refer to as the Constrained Optimization using Response Surfaces (CORS) Method, the next point for costly function evaluation is chosen to be the one that minimizes the current response surface model subject to the given constraints and to additional constraints that the point be of some distance from previously evaluated points. The distance requirement is allowed to cycle, starting from a high value (global search) and ending with a low value (local search). The purpose of the constraint is to drive the method towards unexplored regions of the domain and to prevent the premature convergence of the method to some point which may not even be a local minimizer of the black box function. The new method can be shown to converge to the global minimizer of any continuous function on a compact set regardless of the response surface model that is used. Finally, we considered two particular implementations of the CORS method which utilize a radial basis function model (CORS-RBF) and applied it on the box-constrained Dixon–Szegö test functions and on a simple nonlinearly constrained test function. The results indicate that the CORS-RBF algorithms are competitive with existing global optimization algorithms for costly functions on the box-constrained test problems. The results also show that the CORS-RBF algorithms are better than other algorithms for constrained global optimization on the nonlinearly constrained test problem.     

Solving balanced Procrustes problem with some constraints by eigenvalue decomposition

The balanced Procrustes problem with some special constraints such as symmetric orthogonality and symmetric idempotence are considered. By one time eigenvalue decomposition of the matrix product generated by the matrices A and B, the constrained solutions are constructed simply. Similar strategy is applied to the problem with the corresponding P-commuting constraints with a given symmetric matrix P. Numerical examples are presented to show the efficiency of the proposed methods.     

Sensitivity of constrained Markov decision processes

We consider the optimization of finite-state, finite-action Markov decision processes under constraints. Costs and constraints are of the discounted or average type, and possibly finite-horizon. We investigate the sensitivity of the optimal cost and optimal policy to changes in various parameters. We relate several optimization problems to a generic linear program, through which we investigate sensitivity issues. We establish conditions for the continuity of the optimal value in the discount factor. In particular, the optimal value and optimal policy for the expected average cost are obtained as limits of the dicounted case, as the discount factor goes to one. This generalizes a well-known result for the unconstrained case. We also establish the continuity in the discount factor for certain non-stationary policies. We then discuss the sensitivity of optimal policies and optimal values to small changes in the transition matrix and in the instantaneous cost functions. The importance of the last two results is related to the performance of adaptive policies for constrained MDP under various cost criteria [3,5]. Finally, we establish the convergence of the optimal value for the discounted constrained finite horizon problem to the optimal value of the corresponding infinite horizon problem.     

The Π-strategy in a differential game with linear control constraints

The concept of a linear constraint on the controls of the players in a differential game of pursuit, which, in a certain sense, generalizes both integral and geometrical constraints, is introduced. The optimal parallel pursuit strategy (Π-strategy) is constructed for the corresponding problem.