生化系统稳态优化的双层规划模型与求解方法

针对一类生化系统的稳态优化问题,建立了一种具有二层递阶结构的双层规划优化模型,其上层和下层问题的优化目标分别为最大化产物产率(或代谢物浓度)和最小化生化系统的代谢物浓度之和.模型的生物意义是在尽可能小的代谢成本条件下使产物的产率或浓度达到最大.为了有效求解所建立的NP-hard、非凸双层规划问题,在S-系统建模框架下应用等价变换策略提出了一种可求其最优解的优化算法.算法具有操作简便和计算成本低的优点.最后,将所提双层规划模型与求解方法应用于两个生化系统的稳态优化中.结果表明,方法可行且有效.     

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.     

Hilbert空间中的一类双层规划问题的一阶与二阶最优性条件

本文考虑Hilbert空间中的,上层为有限个不等式约束,下层是一锥约束参数规划的双层规划问题的最优性条件.首先,利用下层问题最优值函数的方向导数的上下界的性质给出一阶最优性条件.之后,在使下层问题的最优值函数是二阶方向可微的条件下,证明了二阶必要性条件.     

双层规划问题基于对偶理论的遗传算法

针对下层为线性规划的非线性双层规划问题，提出了一种基于下层对偶理论的遗传算法。首先利用下层对偶问题可行域的极点对上层变量的取值域进行划分，使得每一个划分区域对应一个极点。根据原一对偶问题最优解的关系，确定每个划分区域对应的下层最优解。其次利用罚函数方法处理了上层约束，设计了一个依赖于种群变化的动态罚因子。对20个测试问题的数值结果表明，所提出的算法是可行有效的。     

二(双)层规划综述

二(双)层规划是研究二层决策的递阶优化问题.其理论、方法和应用在过去的30多年取得了很大的发展.本文对二层规划问题的基本概念、性质和算法作了综述,并且对下层规划问题的解不唯一的情况也作了介绍,最后还给出了几种常见的二层规划模型.     

区域立体物流网络规划模型与算法

提出长株潭区域立体物流网络建构及其网络优化设计.精细化定义了模式分担率,构建了更切合实际的双准则双层规划模型.下层规划描述各核心城市物流枢纽间基于多模式多层阶交通条件下的用户选择行为,上层规划追求最小化长株潭区域立体物流网络系统广义物流费用并最大化整个网络的物流运输量,以满足城市群区域经济发展对物流提出的更高要求.给出了可以克服Frank-Wolfe方法缺陷的惩罚Lagrange对偶方法求解下层规划算法,设计了基于实数编码和组合变异的双层规划改进遗传算法,算法可以保证搜索到近似全局最优解.     

一类多个下层的双层规划问题

本文研究了一类多个下层的双层规划问题.利用文[1]有关理论与方法,获得了该类多下层双层规划问题与一类广义纳什均衡问题的联系,然后通过寻找该广义纳什均衡问题的均衡点求解该双层规划问题.同时给出了一种求解此类广义纳什均衡问题的算法,并进行了一定的理论分析与数值计算.     

一种非增值型凸二次双层规划的有效算法

主要研究了非增值型凸二次双层规划的一种有效求解算法。首先利用数学规划的对偶理论,将所求双层规划转化为一个下层只有一个无约束凸二次子规划的双层规划问题.然后根据两个双层规划的最优解和最优目标值之间的关系,提出一种简单有效的算法来解决非增值型凸二次双层规划问题.并通过数值算例的计算结果说明了该算法的可行性和有效性。     

二层随机规划逼近最优解集的上半收敛性

下层随机规划以上层决策变量作为参数,而上层随机规划是以下层随机规划的唯一最优解作为响应的一类二层随机规划问题,首先在下层随机规划的原问题有唯一最优解的假设下,讨论了下层随机规划的任意一个逼近最优解序列都收敛于原问题的唯一最优解,然后将下层随机规划的唯一最优解反馈到上层,得到了上层随机规划逼近最优解集序列的上半收敛性.     

基于CVaR的次优拥挤收费随机多目标模型北大核心

次优拥挤收费问题一般要考虑不同决策者的不同利益,因此,有必要考虑多个收费策略建立多目标模型来均衡不同决策者的利益.由于决策者常在信息不确定的情况下做决策,在出行需求不确定的条件下,为了确定次优拥挤收费的方案,建立了基于条件风险价值的随机多目标双层规划模型,上层规划的目标函数考虑了系统总阻抗和社会公平性,下层规划是UE用户均衡配流问题.利用基于随机模拟的遗传算法对模型进行求解,并通过数值算例对模型和算法进行分析,验证了模型的有效性.     

Top-Down Fuzzy Decision Making with Partial Preference Information

This paper proposes a multi-stage decision procedure to cope with a hierarchical multiple objective decision environment in which the upper-level DM only provides partial preference information and the lower-level DM is fuzzy about the tradeoff questions such that to achieve substantially more than or equal to some values is delivered to maximize the objectives. Therefore, the procedure consists of two levels, a upper-level and a lower-level. The main idea is that after the upper-level provides partial preference information to the lower-level as a guideline of decision, the lower-level DM determines a satisfactory solution from the reduced non-dominated set in the framework of multi-objective fuzzy programs.     

Bilevel Decision with Generalized Semi-infinite Optimization for Fuzzy Mappings as Lower Level Problems

In this paper, we introduce the bilevel decision problems with parametric generalized semi-infinite optimization for fuzzy mappings as the lower-level problem, and its corresponding MPEC problems. For these problems, we establish two models which are different in the feasible region setting of lower-level problems. Some new existence results are obtained in rather weak conditions.     

A Model for Multi-timescaled Sequential Decision-making Processes with Adversary

Extending the multi-timescale model proposed by the author et al. in the context of Markov decision processes, this paper proposes a simple analytical model called M timescale two-person zero-sum Markov Games (MMGs) for hierarchically structured sequential decision-making processes in two players' competitive situations where one player (the minimizer) wishes to minimize their cost that will be paid to the adversary (the maximizer). In this hierarchical model, for each player, decisions in each level in the M-level hierarchy are made in M different discrete timescales and the state space and the control space of each level in the hierarchy are non-overlapping with those of the other levels, respectively, and the hierarchy is structured in a "pyramid" sense such that a decision made at level m (slower timescale) state and/or the state will affect the evolutionary decision making process of the lower-level m+1 (faster timescale) until a new decision is made at the higher level but the lower-level decisions themselves do not affect the transition dynamics of higher levels. The performance produced by the lower-level decisions will affect the higher level decisions for each player. A hierarchical objective function for the minimizer and the maximizer is defined, and from this we define "multi-level equilibrium value function" and derive a "multi-level equilibrium equation". We also discuss how to solve hierarchical games exactly.     

基于时间窗延迟的资源约束项目调度双层优化研究

本研究从业主—承包商交互的视角构建了一种RCPSP(resource-constrained project scheduling problem)双层优化模型,即在可更新资源约束条件下,项目双方如何进行交互决策达到双方NPV(Net present value)最大化的目标。首先对研究问题进行界定,构建资源约束下的max-NPV项目调度双层优化模型;然后利用延迟优先规则设计了一种基于时间窗延迟的嵌套式自适应遗传算法来求解该模型,以达到双方NPV最大化;最后用一个算例验证算法的有效性,同时通过PSPLIB数值实验说明算法的稳定性,并分析关键参数对项目双方收益的影响。研究结果为项目进程的安排以及奖励机制的设计提供依据,以提高双方利益。     

一类不定性多人两层多目标协调决策模型的初探

考虑到组织决策中分权的普遍存在和高低管理层间依靠信息沟通所发生的控制和协调行为以及组织环境和内部条件的真实特征-不定性，本文将一类特殊的多人两层多目标协调决策模型置于组织不定性环境中予以研究，提出了不定性多人两层多目标协调决策模型．并通过模型的不断转化和K—T条件的应用，最终转化为确定的一般目标规划模型．同时，考虑到上层决策单元对下层决策行为的信息反馈进行处理时的及时性和交互性要求，一个具有快速反应能力的双层人机交互决策模式在问题求解中被设计出来以适应组织对适时目标管理的信息处理需要．     

二层凸规划的基本性质

本文研究了一类抛述二层决策问题的二层数学规划模型，在一定条件下讨论了下层极值函数和上层复合目标函数的凸性和连续性，给出了二层决策问题优决策的存在条件。     

一个带产品定价约束的竞争选址双层规划模型及其求解算法

以大型连锁卖场的选址为研究背景,提出了一个在竞争环境下使获利最大的竞争选址定价双层规划模型,其中上层模型做出选址决策,下层模型确定产品的纳什均衡价格.将设施效用引入到模型中,用指数效用函数来刻画顾客的购物行为偏好,首次证明了不合作状态下双方价格均衡解的存在性和唯一性,并给出了求解最优设施点设置方案和价格均衡解的算法思想及数值算例.     

A simple Tabu Search method to solve the mixed-integer linear bilevel programming problem

Multilevel programming is characterized as mathematical programming to solve decentralized planning problems. The models partition control over decision variables among ordered levels within a hierarchical planning structure of which the linear bilevel form is a special case of a multilevel programming problem. In a system with such a hierarchical structure, the high-level decision making situations generally require inclusion of zero-one variables representing ‘yes-no’ decisions. We provide a mixed-integer linear bilevel programming formulation in which zero-one decision variables are controlled by a high-level decision maker and real-value decision variables are controlled by a low-level decision maker. An algorithm based on the short term memory component of Tabu Search, called Simple Tabu Search, is developed to solve the problem, and two supplementary procedures are proposed that provide variations of the algorithm. Computational results disclose that our approach is effective in terms of both solution quality and efficiency.     

Fuzzy goal programming approach to multilevel programming problems

The purpose of this paper is to propose a procedure for solving multilevel programming problems in a large hierarchical decentralized organization through linear fuzzy goal programming approach. Here, the tolerance membership functions for the fuzzily described objectives of all levels as well as the control vectors of the higher level decision makers are defined by determining individual optimal solution of each of the level decision makers. Since the objectives are potentially conflicting in nature, a possible relaxation of the higher level decision is considered for avoiding decision deadlock. Then fuzzy goal programming approach is used for achieving highest degree of each of the membership goals by minimizing negative deviational variables. Sensitivity analysis with variation of tolerance values on decision vectors is performed to present how the solution is sensitive to the change of tolerance values. The efficiency of our concept is ascertained by comparing results with other fuzzy programming approaches.     

A genetic algorithm for solving linear fractional bilevel problems

Bilevel programming has been proposed for dealing with decision processes involving two decision makers with a hierarchical structure. They are characterized by the existence of two optimization problems in which the constraint region of the upper level problem is implicitly determined by the lower level optimization problem. In this paper a genetic algorithm is proposed for the class of bilevel problems in which both level objective functions are linear fractional and the common constraint region is a bounded polyhedron. The algorithm associates chromosomes with extreme points of the polyhedron and searches for a feasible solution close to the optimal solution by proposing efficient crossover and mutation procedures. The computational study shows a good performance of the algorithm, both in terms of solution quality and computational time.