基于不确定需求的灾后应急救灾网络规划模型与算法

突发事件发生后,快速应急响应的第一步是启动应急救灾网络,合理配置应急救灾资源,以保证救灾过程顺利进行,提高救援效率。本文以台风灾害为背景,建立二阶段应急救灾网络的混合整数规划模型,解决台风灾害的灾后应急救灾网络的规划与设计问题。在求解模型时,考虑需求信息的分布难以确定,并且在一定范围内变动的特点,利用鲁棒优化的方法处理不确定性需求,从而得到合理的临时救灾中心选址以及应急资源配置信息。数值试验表明,建立的模型是实际可行的,而且算法也是有效的。     

应急管理中车辆受限的生产_库存及配送整合模型

针对突发事件发生后,救灾物资不足、车辆数量及容量有限的情况,本文考虑了制造商生产、包装新的救灾物资,构造了车辆返回制造商需要等待订单完成生产的时间函数,建立了一个生产、库存及配送整合的混合整数规划模型。该模型由原材料供应商、制造商、配送中心及客户需求点四部分构成,以完成原材料的运输、制造商中的订单生产并运送到需求点及配送中心的库存订单运送到需求点的总花费时间最短为目标。本文将模型分为两层子模型进行求解:第一层模型采用改进的遗传算法求解;第二层模型采用隐枚举法求解。最后给出一个具体的案例以验证模型的合理性及算法的有效性。     

不确定环境下应急救援供应链鲁棒优化模型

鉴于灾害救援运作的紧迫性和重要性,考虑需求、供应、成本等参数的不确定性,构建一个由供应商、救援配送中心和受灾区域构成的三级应急救援供应链,旨在确定救援产品数量及救援配送中心的合适位置,以最小化救援供应链总成本,最大化受灾区域满意水平为目标,采用区间数据鲁棒优化方法处理模型的不确定性,应用情景随机规划降低鲁棒优化的计算难度,最后给出一个地震案例的具体数据来证明所提救援供应链鲁棒优化模型的有效性和可行性。实验结果表明,需求保守度的变化对目标函数值的影响大于供给和成本保守度的变化,可为应急救援决策者调整不确定参数保守度提供理论支持。     

The static stochastic knapsack problem with normally distributed item sizes

This paper develops exact and heuristic algorithms for a stochastic knapsack problem where items with random sizes may be assigned to a knapsack. An item’s value is given by the realization of the product of a random unit revenue and the random item size. When the realization of the sum of selected item sizes exceeds the knapsack capacity, a penalty cost is incurred for each unit of overflow, while our model allows for a salvage value for each unit of capacity that remains unused. We seek to maximize the expected net profit resulting from the assignment of items to the knapsack. Although the capacity is fixed in our core model, we show that problems with random capacity, as well as problems in which capacity is a decision variable subject to unit costs, fall within this class of problems as well. We focus on the case where item sizes are independent and normally distributed random variables, and provide an exact solution method for a continuous relaxation of the problem. We show that an optimal solution to this relaxation exists containing no more than two fractionally selected items, and develop a customized branch-and-bound algorithm for obtaining an optimal binary solution. In addition, we present an efficient heuristic solution method based on our algorithm for solving the relaxation and empirically show that it provides high-quality solutions.     

Two-stage stochastic variational inequalities: an ERM-solution procedure

We propose a two-stage stochastic variational inequality model to deal with random variables in variational inequalities, and formulate this model as a two-stage stochastic programming with recourse by using an expected residual minimization solution procedure. The solvability, differentiability and convexity of the two-stage stochastic programming and the convergence of its sample average approximation are established. Examples of this model are given, including the optimality conditions for stochastic programs, a Walras equilibrium problem and Wardrop flow equilibrium. We also formulate stochastic traffic assignments on arcs flow as a two-stage stochastic variational inequality based on Wardrop flow equilibrium and present numerical results of the Douglas–Rachford splitting method for the corresponding two-stage stochastic programming with recourse.     

Geometric partitioning and robust ad-hoc network design

We consider bounds for the price of a European-style call option under regime switching. Stochastic semidefinite programming models are developed that incorporate a lattice generated by a finite-state Markov chain regime-switching model as a representation of scenarios (uncertainty) to compute bounds. The optimal first-stage bound value is equivalent to a Value at Risk quantity, and the optimal solution can be obtained via simple sorting. The upper (lower) bounds from the stochastic model are bounded below (above) by the corresponding deterministic bounds and are always less conservative than their robust optimization (min-max) counterparts. In addition, penalty parameters in the model allow controllability in the degree to which the regime switching dynamics are incorporated into the bounds. We demonstrate the value of the stochastic solution (bound) and computational experiments using the S&P 500 index are performed that illustrate the advantages of the stochastic programming approach over the deterministic strategy.     

A simple recourse model for power dispatch under uncertain demand

Optimal power dispatch under uncertainty of power demand is tackled via a stochastic programming model with simple recourse. The decision variables correspond to generation policies of a system comprising thermal units, pumped storage plants and energy contracts. The paper is a case study to test the kernel estimation method in the context of stochastic programming. Kernel estimates are used to approximate the unknown probability distribution of power demand. General stability results from stochastic programming yield the asymptotic stability of optimal solutions. Kernel estimates lead to favourable numerical properties of the recourse model (no numerical integration, the optimization problem is smooth convex and of moderate dimension). Test runs based on real-life data are reported. We compute the value of the stochastic solution for different problem instances and compare the stochastic programming solution with deterministic solutions involving adjusted demand portions.This research is supported by the Schwerpunktprogramm Anwendungsbezogene Optimierung und Steuerung of the Deutsche Forschungsgemeinschaft.     

考虑时值及通货膨胀率的多阶段变质性物品最优库存模型

本文考虑了时值及通货膨胀率下，部分短缺量拖后的变质性物品最优订购问题。在假定变质率为常数和短缺期间损失率与实际缺货量成正比的前提下，给出了寻找最优订购策略的算法，并且证明了在该策略下费用函数取得最小值。最后给出数字实例以说明本模型及求解过程。     

An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty

This study presents an interval-parameter fuzzy two-stage stochastic programming (IFTSP) method for the planning of water-resources-management systems under uncertainty. The model is derived by incorporating the concepts of interval-parameter and fuzzy programming techniques within a two-stage stochastic optimization framework. The approach has two major advantages in comparison to other optimization techniques. Firstly, the IFTSP method can incorporate pre-defined water policies directly into its optimization process and, secondly, it can readily integrate inherent system uncertainties expressed not only as possibility and probability distributions but also as discrete intervals directly into its solution procedure. The IFTSP process is applied to an earlier case study of regional water resources management and it is demonstrated how the method efficiently produces stable solutions together with different risk levels of violating pre-established allocation criteria. In addition, a variety of decision alternatives are generated under different combinations of water shortage.     

基于网格化管理视角下灾后应急物流决策模型与算法研究

灾害发生后,应急资源的需求预测与应急配送中心的合理选址是实现高效救援的关键。本文通过在网格化管理视角下的信息更新将应急救援过程划分为多个阶段,在开展救援的过程中实现救援信息收集和救援预测的同步开展,建立一种多阶段带时间约束的应急救援物资配送响应-时效性的选址模型。借助遗传算法(NSGA-II),实现了基于编码结构独立、路径相互关联基础上的多目标规划求解。本研究的决策模型及算法有着较好的搜索与寻优能力,对实际救援开展具有指导意义。     

A stochastic discounted multi-objective solid transportation problem for breakable items using Analytical Hierarchy Process

In this paper, a multi-objective solid transportation problem (MOSTP) for a breakable item is considered with two different criteria: cost and time for transportation. Here breaking for the item depends on two modes- (i) type of conveyance and (ii) transported amount. The item breaks at constant rate for the modes of conveyance and randomly for the transported amount. The requirement of the destination is crisp, but due to presence of breakability, the fulfillment of demand at destination is stochastic, which is solved by the chance-constraint method. In this paper, a nested discount (IQD within AUD) is presented on the transportation cost. The considered model is formulated to minimize the total transportation cost and time to transport all units of the item with respect to the transported amounts of the item from origins to destinations. Thus the problem reduces to a multi-objective problem. A set of pareto optimal solutions are obtained by multi-objective genetic algorithm (MOGA). The best solution out of this set is presented using Analytical Hierarchy Process (AHP). The MOSTP has also been formulated with entropy function defined by Shannons measure of entropy. The entropy function is used as an additional objective function which acts as a measure of dispersion. To illustrate the model, numerical example has been presented. The effect of entropy on transported amount is illustrated. A sensitivity analysis on the total cost due to the changes in breakability rate is presented.     

Modelling and computing (Rn, Sn) policies for inventory systems with non-stationary stochastic demand

This paper addresses the single-item, non-stationary stochastic demand inventory control problem under the non-stationary (R, S) policy. In non-stationary (R, S) policies two sets of control parameters—the review intervals, which are not necessarily equal, and the order-up-to-levels for replenishment periods—are fixed at the beginning of the planning horizon to minimize the expected total cost. It is assumed that the total cost is comprised of fixed ordering costs and proportional direct item, inventory holding and shortage costs. With the common assumption that the actual demand per period is a normally distributed random variable about some forecast value, a certainty equivalent mixed integer linear programming model is developed for computing policy parameters. The model is obtained by means of a piecewise linear approximation to the non-linear terms in the cost function. Numerical examples are provided.     

A technique for speeding up the solution of the Lagrangean dual

We propose techniques for the solution of the LP relaxation and the Lagrangean dual in combinatorial optimization and nonlinear programming problems. Our techniques find the optimal solution value and the optimal dual multipliers of the LP relaxation and the Lagrangean dual in polynomial time using as a subroutine either the Ellipsoid algorithm or the recent algorithm of Vaidya. Moreover, in problems of a certain structure our techniques find not only the optimal solution value, but the solution as well. Our techniques lead to significant improvements in the theoretical running time compared with previously known methods (interior point methods, Ellipsoid algorithm, Vaidya's algorithm). We use our method to the solution of the LP relaxation and the Langrangean dual of several classical combinatorial problems, like the traveling salesman problem, the vehicle routing problem, the Steiner tree problem, thek-connected problem, multicommodity flows, network design problems, network flow problems with side constraints, facility location problems,K-polymatroid intersection, multiple item capacitated lot sizing problem, and stochastic programming. In all these problems our techniques significantly improve the theoretical running time and yield the fastest way to solve them.     

A dynamic programming model of energy storage and transformer deployments to relieve distribution constraints

We introduce a stochastic dynamic programming (SDP) model that co-optimizes multiple uses of distributed energy storage, including energy and ancillary service sales, backup capacity, and transformer loading relief, while accounting for market and system uncertainty. We propose an approximation technique to efficiently solve the SDP. We also use a case study with high residential loads to demonstrate that a deployment consisting of both storage and transformer upgrades decreases costs and increases value relative to a transformer-only deployment.     

Smooth Solutions to Optimal Investment Models with Stochastic Volatilities and Portfolio Constraints

Abstract. This paper deals with an extension of Merton's optimal investment problem to a multidimensional model with stochastic volatility and portfolio constraints. The classical dynamic programming approach leads to a characterization of the value function as a viscosity solution of the highly nonlinear associated Bellman equation. A logarithmic transformation expresses the value function in terms of the solution to a semilinear parabolic equation with quadratic growth on the derivative term. Using a stochastic control representation and some approximations, we prove the existence of a smooth solution to this semilinear equation. An optimal portfolio is shown to exist, and is expressed in terms of the classical solution to this semilinear equation. This reduction is useful for studying numerical schemes for both the value function and the optimal portfolio. We illustrate our results with several examples of stochastic volatility models popular in the financial literature.     

Stochastic optimal control and algorithm of the trajectory of horizontal wells

This paper presents a nonlinear, multi-phase and stochastic dynamical system according to engineering background. We show that the stochastic dynamical system exists a unique solution for every initial state. A stochastic optimal control model is constructed and the sufficient and necessary conditions for optimality are proved via dynamic programming principle. This model can be converted into a parametric nonlinear stochastic programming by integrating the state equation. It is discussed here that the local optimal solution depends in a continuous way on the parameters. A revised Hooke–Jeeves algorithm based on this property has been developed. Computer simulation is used for this paper, and the numerical results illustrate the validity and efficiency of the algorithm.     

Optimal management of DC pension plan in a stochastic interest rate and stochastic volatility framework

This paper investigates an optimal investment strategy of DC pension plan in a stochastic interest rate and stochastic volatility framework. We apply an affine model including the Cox–Ingersoll–Ross (CIR) model and the Vasicek mode to characterize the interest rate while the stock price is given by the Heston’s stochastic volatility (SV) model. The pension manager can invest in cash, bond and stock in the financial market. Thus, the wealth of the pension fund is influenced by the financial risks in the market and the stochastic contribution from the fund participant. The goal of the fund manager is, coping with the contribution rate, to maximize the expectation of the constant relative risk aversion (CRRA) utility of the terminal value of the pension fund over a guarantee which serves as an annuity after retirement. We first transform the problem into a single investment problem, then derive an explicit solution via the stochastic programming method. Finally, the numerical analysis is given to show the impact of financial parameters on the optimal strategies.     

Natural gas cash-out problem: Bilevel stochastic optimization approach

A stochastic formulation of the natural gas cash-out problem is given in a form of a bilevel multi-stage stochastic programming model with recourse. After reducing the original formulation to a bilevel linear problem, a stochastic scenario tree is defined by its node events, and time series forecasting is used to produce stochastic values for data of natural gas price and demand. Numerical experiments were run to compare the stochastic solution with the perfect information solution and the expected value solutions.     

极大极小随机规划逼近问题最优解集和最优值的稳定性

研究了特殊的二层极大极小随机规划逼近收敛问题. 首先将下层初始随机规划最优解集拓展到非单点集情形, 且可行集正则的条件下, 讨论了下层随机规划逼近问题最优解集关于上层决策变量参数的上半收敛性和最优值函数的连续性. 然后把下层随机规划的epsilon-最优解向量函数反馈到上层随机规划的目标函数中, 得到了上层随机规划逼近问题的最优解集关于最小信息概率度量收敛的上半收敛性和最优值的连续性.     

A Deterministic Approach To Stochastic Optimal Control With Application To Anticipative Control

Using the decomposition of solution of SDE, we consider the stochastic optimal control problem with anticipative controls as a family of deterministic control problems parametrized by the paths of the driving Wiener process and of a newly introduced Lagrange multiplier stochastic process (nonanticipativity equality constraint). It is shown that the value function of these problems is the unique global solution of a robust equation (random partial differential equation) associated to a linear backward Hamilton-Jacobi-Bellman stochastic partial differential equation (HJB SPDE). This appears as limiting SPDE for a sequence of random HJB PDE's when linear interpolation approximation of the Wiener process is used. Our approach extends the Wong-Zakai type results [20] from SDE to the stochastic dynamic programming equation by showing how this arises as average of the limit of a sequence of deterministic dynamic programming equations. The stochastic characteristics method of Kunita [13] is used to represent the value function. By choosing the Lagrange multiplier equal to its nonanticipative constraint value the usual stochastic (nonanticipative) optimal control and optimal cost are recovered. This suggests a method for solving the anticipative control problems by almost sure deterministic optimal control. We obtain a PDE for the “cost of perfect information” the difference between the cost function of the nonanticipative control problem and the cost of the anticipative problem which satisfies a nonlinear backward HJB SPDE. Poisson bracket conditions are found ensuring this has a global solution. The cost of perfect information is shown to be zero when a Lagrangian submanifold is invariant for the stochastic characteristics. The LQG problem and a nonlinear anticipative control problem are considered as examples in this framework