Uncertainty and value of information when allocating resources within and between healthcare programmes

A two-stage stochastic mathematical programming formulation has been developed to optimally allocate resources within and between healthcare programmes when there is an exogenous budget and the parameters of the healthcare models are variable and uncertain. This formulation solves the optimal resource allocation problem and calculates the expected value of acquiring additional information to resolve the uncertainties within the allocation. It is shown that the proposed formulation has several advantages over the chance constrained and robust mathematical programming methods.     

Semidefinite complementarity reformulation for robust Nash equilibrium problems with Euclidean uncertainty sets

Consider the N-person non-cooperative game in which each player’s cost function and the opponents’ strategies are uncertain. For such an incomplete information game, the new solution concept called a robust Nash equilibrium has attracted much attention over the past several years. The robust Nash equilibrium results from each player’s decision-making based on the robust optimization policy. In this paper, we focus on the robust Nash equilibrium problem in which each player’s cost function is quadratic, and the uncertainty sets for the opponents’ strategies and the cost matrices are represented by means of Euclidean and Frobenius norms, respectively. Then, we show that the robust Nash equilibrium problem can be reformulated as a semidefinite complementarity problem (SDCP), by utilizing the semidefinite programming (SDP) reformulation technique in robust optimization. We also give some numerical example to illustrate the behavior of robust Nash equilibria.     

Ad Hoc网络区域划分和资源分配问题研究

主要利用图论、概率统计及优化理论对Ad-Hoc网络进行了数学建模.研究了等圆(不等圆)区域覆盖、带障碍区域的覆盖、确定性点覆盖、信道分配、抗毁度、节能性和通信质量等问题.定义了覆盖效率、抗毁性概率指标、覆盖系数、期望覆盖系数、网络寿命等一系列评价系数和衡量标准,提出了基于单位距离覆盖系数和期望覆盖系数的启发式寻优算法,并编程加以实现,得到较满意的近似解.     

凸概率密度分布簇下的多损失鲁棒优化等价模型及在直营连锁企业中的应用

首先,证明了凸概率密度分布簇的单周期期望均值下单损失鲁棒优化等价模型定理,以及凸概率密度分布簇的单周期期望均值下多损失鲁棒优化等价模型。然后,提出了直营连锁企业的产品在凸概率密度分布簇下的期望均值的单周期生产分配供应问题,建立了直营连锁企业的单周期生产分配供应期望均值鲁棒模型,在获得近似周期概率分布簇情形下给出了单周期生产分配供应鲁棒模型,这种近似鲁棒模型等价于一个线性规划问题。最后,通过已知一个产品的4个周期构成的混合分布簇进行了数值实验,数值结果表明了期望均值准则下的生产分配供应鲁棒模型的生产分配供应策略更加稳健。     

Stochastic multi-site supply chain planning in textile and apparel industry under demand and price uncertainties with risk aversion

A multi-product, multi-period, multi-site supply chain production and transportation planning problem, in the textile and apparel industry, under demand and price uncertainties is considered in this paper. The problem is formulated using a two-stage stochastic programming model taking into account the production amount, the inventory and backorder levels as well as the amounts of products to be transported between the different plants and customers in each period. Risk management is addressed by incorporating a risk measure into the stochastic programming model as a second objective function, which leads to a multi-objective optimization model. The objectives aim to simultaneously maximize the expected net profit and minimize the financial risk measured. Two risk measures are compared: the conditional-value-at-risk and the downside risk. As the considered objective functions conflict with each other’s, the problem solution is a front of Pareto optimal robust alternatives, which represents the trade-off among the different objective functions. A case study using real data from textile and apparel industry in Tunisia is presented to illustrate the effectiveness of the proposed model and the robustness of the obtained solutions.     

联合订货下基于ø-散度的多产品库存鲁棒优化模型

针对多产品联合库存决策问题,在市场需求不确定条件下,建立了考虑联合订货成本的多产品库存鲁棒优化模型。针对不确定市场需求,采用一系列未知概率的离散情景进行描述,给出了基于最小最大准则的鲁棒对应模型,并证明了(s,S)库存策略的最优性。进一步,在仅知多产品市场需求历史数据基础上,采用基于ø-散度的数据驱动方法构建了满足一定置信度要求的关于未知需求概率分布的不确定集。在此基础上,为获得(s,S)库存策略的相关参数,运用拉格朗日对偶方法将所建模型等价转化为易于求解的数学规划问题。最后,通过数值计算分析了Kullback-Leibler散度和Cressie-Read散度以及不同的置信水平下的多产品库存绩效,并将其与真实分布下应用鲁棒库存策略得到的库存绩效进行对比。结果表明,需求分布信息的缺失虽然会导致一定的库存绩效损失,但损失值很小,表明基于文中方法得到的库存策略能够有效抑制需求不确定性扰动,具有良好的鲁棒性。     

基于随机规划的建筑集群冷热电联供系统优化研究

面向建筑集群的冷热电联供系统的设计和优化是实现建筑楼宇能源成本节约的重要途径。随机因素对该联供系统的优化决策,具有显著的影响。考虑建筑楼宇的能源需求为随机变量,构建随机混合整数规划模型,解决以最小化建筑楼宇总费用为目标时建筑集群冷热电联供系统的优化问题;其次,提出采用Benders多割平面方法求解多目标规划问题,从而寻找冷热电联供系统的设备配置和系统运行的Pareto最优决策;最后,通过实验验证了模型和算法的有效性。实验结果表明建筑集群在协作模式下,相比于非协作模式,具有更低的总费用。     

Multicriteria energy policy investments and energy market clearance via integer programming

The deregulation of energy markets has created a framework for policy making, still under evolution, which is much more complex than the previous one. As a consequence, new requirements need to be met, concerning both technical design and financial management. This framework renders the use of multicriteria techniques attractive. Here, the investments in suppliers, depending on the policy implemented, are formulated as an integer programming problem, which consists of different sub-problems according to the assumptions made and the market’s regulations. The equivalent relaxed problem is a mixed integer programming problem that can represent the clearance of the energy market by considering several criteria besides price and quantity. Nonlinearities are reformulated by inserting additional binary variables so that the solution algorithms are more effective and efficient in most realistic cases. The feasible solutions and the optimal solution that maximizes every time the market regulator’s gain are obtained, after imposing some thresholds on the criteria used to evaluate the different energy technologies, thus creating a decision support system for the regulator.     

Totally balanced games arising from controlled programming problems

A cooperative game in characteristic-function form is obtained by allowing a number of individuals to esercise partial control over the constraints of a (generally nonlinear) mathematical programming problem, either directly or through committee voting. Conditions are imposed on the functions defining the programming problem and the control system which suffice to make the game totally balanced. This assures a nonempty core and hence a stable allocation of the full value of the programming problem among the controlling palyers. In the linear case the core is closely related to the solutions of the dual problem. Applications are made to a variety of economic models, including the transferable utility trading economies of Shapley and Shubik and a multishipper one-commodity transshipment model with convex cost functions and concave revenue functions. Dropping the assumption of transferable utility leads to a class of controlled multiobjective or ‘Pareto programming’ problems, which again yield totally balanced games.     

高层电梯系统运行方案的建模与求解

随着社会的发展,运用垂直交通系统的高层建筑和智能化建筑不断出现。而有效的电梯交通配置,是垂直交通系统高效运行的基本保证。本文针对高层商务建筑中的电梯运行管理方案设计问题,分析了影响电梯耗能和用户满意度的主要因素。分别建立了电梯数目已知和电梯数目未知情况下的电梯调度优化模型,并设计相应动态规划算法和遗传算法。结合算例,求解算例中的电梯优化调度方案,以验证模型的合理性。最后根据我们建立的电梯调度模型,借助VC++作出可视化的电梯调度示意界面,将本文的研究结果用于实际的电梯调度中。     

Stochastic multi-site capacity planning of TFT-LCD manufacturing using expected shadow-price based decomposition

This paper presents a stochastic optimization model and efficient decomposition algorithm for multi-site capacity planning under the uncertainty of the TFT-LCD industry. The objective of the stochastic capacity planning is to determine a robust capacity allocation and expansion policy hedged against demand uncertainties because the demand forecasts faced by TFT-LCD manufacturers are usually inaccurate and vary rapidly over time. A two-stage scenario-based stochastic mixed integer programming model that extends the deterministic multi-site capacity planning model proposed by Chen et al. (2010) [1] is developed to discuss the multi-site capacity planning problem in the face of uncertain demands. In addition a three-step methodology is proposed to generate discrete demand scenarios within the stochastic optimization model by approximating the stochastic continuous demand process fitted from the historical data. An expected shadow-price based decomposition, a novel algorithm for the stage decomposition approach, is developed to obtain a near-optimal solution efficiently through iterative procedures and parallel computing. Preliminary computational study shows that the proposed decomposition algorithm successfully addresses the large-scale stochastic capacity planning model in terms of solution quality and computation time. The proposed algorithm also outperforms the plain use of the CPLEX MIP solver as the problem size becomes larger and the number of demand scenarios increases.     

End-Effector Trajectory Tracking of Serial Flexible Manipulators

Light weight manipulator design results in low energy consumption and allow often high working speeds. However, due to the increased system flexibility undesired vibrations occur. Thus, in the control design these flexibilities must be taken into account. In order to obtain a good performance in end-effector trajectory tracking an efficient feedforward control is used, which is then supplemented by additional feedback control to account for small disturbances and uncertainties. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Finding Robust Global Optimal Values of Bilevel Polynomial Programs with Uncertain Linear Constraints

This paper studies a bilevel polynomial program involving box data uncertainties in both its linear constraint set and its lower-level optimization problem. We show that the robust global optimal value of the uncertain bilevel polynomial program is the limit of a sequence of values of Lasserre-type hierarchy of semidefinite linear programming relaxations. This is done by first transforming the uncertain bilevel polynomial program into a single-level non-convex polynomial program using a dual characterization of the solution of the lower-level program and then employing the powerful Putinar’s Positivstellensatz of semi-algebraic geometry. We provide a numerical example to show how the robust global optimal value of the uncertain bilevel polynomial program can be calculated by solving a semidefinite programming problem using the MATLAB toolbox YALMIP.     

A dual-uncertainty-based chance-constrained model for municipal solid waste management

A double-sided dual-uncertainty-based chance-constrained programming (DDCCP) model was developed for supporting municipal solid waste management under uncertainty. The model was capable of tackling left-hand- and right-hand-side variables in constraints where those variables were affected by dual uncertainties (i.e. e.g. both fuzziness and randomness); and they were expressed as fuzzy random variables (FRVs). In this study, DDCCP model were formulated and solved based on stochastic and fuzzy chance-constrained programming techniques, leading to optimal solutions under different levels of constraints violation and satisfaction reliabilities. A long-term solid waste management problem was used to demonstrate the feasibility and applicability of DDCCP model. The obtained results indicated that DDCCP was effective in handling constraints with FRVs through satisfying them at a series of allowable levels, generating various solutions that facilitated evaluation of trade-offs between system economy and reliability. The proposed model could help decision makers establish cost-effective waste-flow allocation patterns under complex uncertainties, and gain in-depth insights into the municipal solid waste management system.     

Optimization and mechanism design

Mechanism design is about optimizing the allocation of resources when the parameters needed to determine an optimal allocation are privately held by the agents who will consume the resources. An agent’s report of her information will influence the resulting allocation which in turn will affect her utility. In such a situation, how can one simultaneously elicit the information that is privately held and choose the optimal allocation? This paper illustrates how standard results in linear programming play a role in the analysis of mechanism design problems. It is not a comprehensive survey. Rather, it focuses on two variations of a particular problem: the allocation of a single object.     

Stochastic load modelling for electric energy distribution applications

Summary The problem of electric load modelling for low aggregation levels is addressed in this paper. The objective is to obtain good “demand” and “response” behaviour models of any group of loads in an electric energy distribution system for any of the functional applications that are beeing considered in the framework of the Distribution Management Systems, aimed to improve the energy efficiency, reliability and quality of the system. A brief critical revision of the methodologies used for that purpose is in the paper, and the advantages of using approaches where physical knowledge about the load characteristics is used, are stated and demonstrated.     

Adaptive management of energy transitions in long-term climate change

The UN Framework Convention on Climate Change (UNFCCC) demands stabilization of atmospheric greenhouse gas concentrations at levels that prevent dangerous anthropogenic interference with the climate system. This requires an unprecedented degree of international action for emission reductions and technological change in the energy sector. Extending the established optimal control approach, the paper combines the concepts of adaptive control, inverse modeling and local optimization to climate change decision-making and management. An alternative decision model is described where controls are adjusted towards a moving target under changing conditions. A framework for integrated assessment is introduced, where a basic climate model is coupled to an economic production function with energy as a production factor, which is controlled by the allocation of investments to alternative energy technologies. Investment strategies are shaped by value functions, including utility, costs and climate damages for a given future time horizon, which are translated into admissible emission limits to keep atmospheric carbon concentrations and global mean temperature asymptotically below a given threshold. Conditions for switching between management and technology paths with different costs and carbon intensities are identified. To take account of the substantial uncertainties, an exemplary case discusses the sensitivity of the results to variation of crucial parameters, in particular time discounting, climate damage, taxes and time horizon for decision-making.     

An Interval-Parameter Fuzzy Approach for Multiobjective Linear Programming Under Uncertainty

An interval-parameter fuzzy linear programming method (IFMOLP) is proposed in this study for multiple objective decision-making under uncertainty. As a hybrid of interval-parameter and fuzzy methodologies, the IFMOLP incorporates interval-parameter linear programming and fuzzy multiobjective programming approaches to form an integrated optimization system. The method inherits advantages of interval-parameter programming, and allows uncertainties and decision-makers’ aspirations to be effectively communicated into its programming processes and resulting solutions. Membership functions for both objectives and constraints are formulated to reflect uncertainties in different system components and their interrelationships. An interactive solution procedure has been developed based on solution approaches of the interval-parameter and fuzzy programming techniques, plus necessary measures for handling the multiobjective feature. A didactic example is provided in the paper to illustrate the detailed solution process. Possibilities of further improvements by seeking Pareto optimum and incorporating flexible preference within constraints are also discussed.     

Reliability based limit design of steel frames with limited residual strain energy capacity

The aim of this paper is to take into consideration the influence of the limited load carrying capacity of the connections on the plastic limit state of elasto-plastic steel (or composite) framed structures under multi-parameter stochastic loading and probabilistically given conditions. In addition to the plastic limit design to control the plastic behaviour of the structure, bound on the complementary strain energy of the residual forces is also applied. This bound has significant effect for the load parameter. If the design uncertainties (manufacturing, strength, geometrical) are expressed by the calculation of the complementary strain energy of the residual forces a reliability based extended limit design problem is formed. The formulations of the problems yield to nonlinear mathematical programming which are solved by the use of sequential quadratic algorithm. The bi-level optimization procedure governed by the reliability index calculation. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)