基于覆盖模型的pm2.5监测站点布局优化方法研究

科学的监测站点选址与布局优化是pm2.5等空气污染监测面临的重要问题.针对pm2.5重点监测城市哈尔滨,分析了影响pm2.5浓度及分布的主要因素,构建了以区域地理邻接关系及覆盖水平为基础的监测设施覆盖模型.模型综合了人口、地域面积以pm2.5关注度等因素,实现了重点监测区域覆盖指数及总体覆盖水平最大化目标.实证分析说明了模型的有效性和实用性,为相关环境监测网点选址和优化问题提供了科学的依据和参考.     

收缩喷嘴中的湍流Ⅰ——边界层解

应用边界层积分法,研究锥形喷嘴入口区域中湍动涡流的发展.球面坐标系中的控制方程,通过边界层的假定得到简化,并对边界层进行了积分.应用4阶Adams预测校正法求解该微分方程组.入口区域的切向和轴向速度,分别应用自由涡流和均匀速度分布来表示.由于缺乏收缩喷嘴中涡流的实验数据,需要用数值模拟对该发展模式进行逆向验证.数值模拟的结果证明,该解析模型在预测边界层参数中的能力,例如边界层的生长、剪切率和边界层厚度,以及不同锥度角时的涡流强度衰减率等.为所提出的方法引进一个简明而有效的程序,用以研究几何形状收缩设备内的边界层参数.     

一类正值线性过程的参数估计

在研究水质污染问题时,文[1]提出了非负一阶自回归模型:X_t=(?)X_(t-1)+ξ_t,其中{ξ_t}为独立同分布非负随机序列,0<(?)<1.此模型中 X_t 表示在时刻 t 时净化池中的污水量,1-(?)_1表示在单位时间间隔内被净化污水的比例,ξ_t 表示在时刻 t 注入净化池中的污水量.文[1]给出了模型参数的极为简便的强相合估计和相应的模拟结果.文[2]把[1]的结果推广到二阶自回归情形,克服了本质上的困难获得相应的结果.本文提出一类更为广泛的正值线性模型     

M_t/GI/∞随机服务系统的串联网络

本文讨论n个无穷台服务系统的串联网络。这种模型可表示随机吸入的微量物质在机体内各部分的分布过程,也可描述放射性元素或随机产生的污染物沿相接连区域的扩散传播。     

关于M|M|n排队系統中的А.Я.Хинчин假定

1°问题的提出.考虑一个M|M|n排队系统.即输入为带参数λ的Poisson流;服务时间分布是带参数β的负指数分布;有n个服务员,先来先服务的排队系统(关于排队论的一般知识可参看[1][2]或[3]).若用X(t)表示此系统在时刻t时的队长,则如所周知(参看[3]§34),X(t)是一个带有平稳转移概率的可数马尔可夫过程(关于带有平稳转移概率的可数马尔可夫过程的一般知识可参看[4]).它的Q矩阵(qij)是:     

具有中途准人机制和多重休假的离散时间GI/Geom(a,b)/1/N早到排队系统

运用补充变量方法和嵌入Markov链方法讨论了一个具有批量服务中途准入机制的离散时间多重休假排队系统.利用一种有效的数值迭代算法获得了系统中三种时刻的队长分布.进一步,使用不同时刻的队长分布,通过数值化方法研究了系统参数对阻塞概率,批量服务中途准入概率,顾客在缓冲空间中平均等待时间等几类重要性能指标的影响.     

两部件并行系统的可靠性分析

本文考虑两个不同部件的并行系统,其中一个部件的寿命分布和修理时间分布均为负指数分布,另一个部件的寿命分布和修理时间分布均为一般分布。本文使用Markov更新过程和交替更新过程的理论,求得了:(1)系统首次失效时间和其后停工时间的联合分布;(2)系统的利用率;(3)在任意时刻t修理工空闲的概率;(4)(0,t]内系统失效次数的概率分布,以及(0,t]内系统平均失效次数。     

基于Stackelberg博弈的时变双层交通分配模型

提出一个时变双层交通分配模型,其中上层网络管理者设立了一个路段的最大排队长度,其目标是使由网络流和排队长度定义的总出行时间最小.目标函数在离散时段内以路段流量和排队长度作为决策变量,同时考虑不同类型的信号交叉口延误的影响.下层网络用户的反应依赖于上层管理者的决策,其选择是使自身感知阻抗最小的路径,服从一个基于成对组合Logit的路径选择模型,构成一个成对组合Logit的均衡分配问题.结合了交通分配和流传播方法,将其表示为一个均衡约束下的双层数学规划问题,形成了一个Stackelberg非合作博弈.使用遗传算法求解该双层规划问题,并采用实证分析来表现模型的特征和算法的计算表现.结果表明路径重叠、路段流量、路段排队长度等因素对网络均衡流分布均有显著影响.     

新冠肺炎疫情条件下的企业复工复产优化规划方法

新冠病毒肺炎疫情对整个经济社会发展造成了很大冲击,如何在不放松疫情防控的前提下科学规划企业复工复产,这是地方政府面临的一个重要挑战。基于浙江省在统筹疫情防控和经济社会发展工作中的有关经验,建立一个疫情条件下企业复工复产规划问题的整数规划模型,其目的是要在不违反疫情传播风险等约束下,从大批申请企业中选择一部分批准复工复产并安排优先顺序,以尽可能满足社会对相关产业产能的需求。为有效求解该问题,提出了一个改进的禁忌搜索算法,它使用贪心策略来构造一个初始解,并不断通过可变规模的邻域搜索来探寻更优的解,在多个地区企业复工复产规划问题实例上的计算结果验证了该算法的效率。     

稠密稀疏N体问题解析函数近似计算

考虑粒子相互作用的N体问题解析函数近似计算,建立了粒子稠密和稀疏区域的数学模型,用多重积分表示粒子相互作用径向分布函数的解析表达式,得到了稀疏区域到稠密球域的分析解,计算了蛋白质的径向函数.在球外粒子数M=100,球内粒子数为N=2000的工况下,计算误差小于千分之二,计算时间远小于直接计算时间.     

Efficient algorithms for combined heat and power production planning under the deregulated electricity market

Combined heat and power (CHP) production is an important energy production technology that can yield much higher total energy efficiency than separate heat and power generation. In CHP production, the heat and power production follows a joint characteristic, which means that the production planning must be done in coordination. Cost-efficient operation of a CHP system can be planned by using an optimization model. A long-term planning model decomposes into thousands of hourly models. Earlier, in the regulated electric power market, the planning problem was symmetrically driven by heat and power demand. The liberalization of the power market has created an asymmetrical planning problem, where heat production responds to the demand and power production to the volatile market price. In this paper, we utilize this asymmetry to develop novel envelope-based dual algorithms for solving the hourly CHP models efficiently. The basic idea is to transform the three-dimensional characteristic operating region for heat and power production of each CHP plant into a two-dimensional envelope by taking the power price as a parameter. Then the envelopes of each plant are used for looking up the optimal solution rapidly. We propose two versions of the algorithm: the on-line envelope construction algorithm (ECON) where the envelopes are constructed for each hour based on the power price and the off-line envelope construction algorithm (ECOFF) where envelopes are pre-computed for all different power price ranges. We derive the theoretical time complexity of the two algorithms and compare their performance empirically with realistic test models against the ILOG CPLEX solver and the Power Simplex (PS) algorithm. PS is an extremely efficient specialized primal algorithm developed for the symmetrical CHP planning problem under the regulated market. On average, when reusing previous basic solutions, ECON is 603 times faster than CPLEX and 1.3 times faster than PS. ECOFF is 1860 times faster than CPLEX and four times faster than PS.     

A numerical method to approximate optimal production and maintenance plan in a flexible manufacturing system

The simultaneous planning of the production and the maintenance in a flexible manufacturing system is considered in this paper. The manufacturing system is composed of one machine that produces a single product. There is a preventive maintenance plan to reduce the failure rate of the machine. This paper is different from the previous researches in this area in two separate ways. First, the failure rate of the machine is supposed to be a function of its age. Second, we assume that the demand of the manufacturing product is time dependent and its rate depends on the level of advertisement on that product. The objective is to maximize the expected discounted total profit of the firm over an infinite time horizon. In the process of finding a solution to the problem, we first characterize an optimal control by introducing a set of Hamilton–Jacobi–Bellman partial differential equations. Then we realize that under practical assumptions, this set of equations can not be solved analytically. Thus to find a suboptimal control, we approximate the original stochastic optimal control model by a discrete-time deterministic optimal control problem. Then proposing a numerical method to solve the steady state Riccati equation, we approximate a suboptimal solution to the problem.     

Two-stage fuzzy production planning expected value model and its approximation method

This work develops a novel two-stage fuzzy optimization method for solving the multi-product multi-period (MPMP) production planning problem, in which the market demands and some of the inventory costs are assumed to be uncertainty and characterized by fuzzy variables with known possibility distributions. Some basic properties about the MPMP production planning problem are discussed. Since the fuzzy market demands and inventory costs usually have infinite supports, the proposed two-stage fuzzy MPMP production planning problem is an infinite-dimensional optimization problem that cannot be solved directly by conventional numerical solution methods. To overcome this difficulty, this paper adopts an approximation method (AM) to turn the original two-stage fuzzy MPMP production planning problem into a finite-dimensional optimization problem. The convergence about the AM is discussed to ensure the solution quality. After that, we design a heuristic algorithm, which combines the AM and simulated annealing (SA) algorithm, to solve the proposed two-stage fuzzy MPMP production planning problem. Finally, one real case study about a furniture manufacturing company is presented to illustrate the effectiveness and feasibility of the proposed modeling idea and designed algorithm.     

基于加速遗传算法投影寻踪模型的企业可持续发展能力评价研究

可持续发展能力是企业核心竞争力形成的关键,因而科学的评价企业的可持续发展能力对企业的未来发展具有重大的战略意义。针对已有模型存在无法评价动态非线性寻优的问题,构建投影寻踪模型,并运用基于实数编码的加速遗传算法对模型进行求解,选择496家创业板企业为样本进行了实证研究。研究结果表明:仅有1.61%的企业具有强劲的可持续发展能力,36.29%的企业的可持续发展能力处于良好状态,而62.10%的企业的可持续发展能力处于中等偏下水平。基于实证研究,从建立合理的资本结构、增大偿债能力、提高经营能力、拓宽投融资渠道、完善市场环境等方面提出促进企业提升可持续发展能力的方法与途径。     

Optimal Goodwill Path to Introduce a New Product

We consider the problem of determining an optimal goodwill path for the introduction of a new product in a market, while looking for the maximum foreseen profit. The foreseen revenue depends on the product introduction time and on the goodwill level at the same time. We focus on the advertising costs associated with the goodwill evolution and assume that the cost function possesses some rather general features which are shared by the cost functions of the Nerlove-Arrow type models. The dynamic optimization problem is discussed in the calculus of variations framework. A few examples associated with special cost functions are discussed in detail.     

Robust optimization for relief logistics planning under uncertainties in demand and transportation time

Emergency logistics is an essential component of post-disaster relief campaigns. However, there are always various uncertainties when making decisions related to planning and implementing post-disaster relief logistics. Considering the particular environmental conditions during post-disaster relief after a catastrophic earthquake in a mountainous area, this paper proposes a stochastic model for post-disaster relief logistics to guide the tactical design for mobilizing relief supply levels, planning initial helicopter deployments, and creating transportation plans within the disaster region, given the uncertainties in demand and transportation time. We then introduce a robust optimization approach to cope with these uncertainties and deduce the robust counterpart of the proposed stochastic model. A numerical example based on disaster logistics during the Great Sichuan Earthquake demonstrates that the model can help post-disaster managers to determine the initial deployments of emergency resources. Sensitivity analyses explore the trade-off between optimization and robustness by varying the robust optimization parameter values.     

Horizon-unbiased utility functions

In this paper we consider a class of mixed optimal control/optimal stopping problems related to the choice of the best time to sell a single unit of an indivisible asset. We assume that in addition to the indivisible asset, the agent has access to a financial market. Investments in the financial market can be used for hedging, but the financial assets are only partially correlated with the indivisible asset, so that the agent faces an incomplete markets problem.     

Designing multi-vehicle delivery tours in a grid-cell format

This paper presents a methodology to be used in the design of multi-delivery tours associated with the servicing of a heterogeneous region, where the density of visiting points and the amount of cargo vary over the served area. Both vehicle cycle time and vehicle load are treated probabilistically. The region under analysis is partitioned into a number of sectors, rings, and districts, with the polar coordinates centered at the depot. We assume a rectangular grid structure for the representation of the spatial variables. This division is intended to attain a quasi-homogeneous distribution effort (approximate equal number of vehicles) in each partition. The optimization model seeks the best fleet of vehicles that minimizes total daily transport costs. As an example, the model is applied to a parcel delivery problem in the city of São Paulo, Brazil.     

Supply chain optimization of petroleum organization under uncertainty in market demands and prices

A multi-period stochastic planning model has been developed and implemented for a supply chain network of a petroleum organization operating in an oil producing country under uncertain market conditions. The proposed supply chain network consists of all activities related to crude oil production, processing and distribution. Uncertainties were introduced in market demands and prices. A deterministic optimization model was first developed and tested. The impact of uncertainty on the supply chain was studied by performing a sensitivity analysis in which ±20% deviations were introduced in market demands and prices of different commodities. A stochastic formulation was then proposed, which is based on the two-stage problem with finite number of realizations. The proposed stochastic programming approach proved to be quite effective in developing resilient production plans in light of high degree of uncertainty in market conditions. The anticipated production plans have a considerably lower expected value of perfect information (EVPI). The main conclusion of this study is that for an oil producing country with oil processing capabilities, the impact of economic uncertainties may be tolerated by an appropriate balance between crude exports and processing capacities.     

Model and algorithms for multi-period sea cargo mix problem

In this paper, we consider the sea cargo mix problem in international ocean container shipping industry. We describe the characteristics of the cargo mix problem for the carrier in a multi-period planning horizon, and formulate it as a multi-dimensional multiple knapsack problem (MDMKP). In particular, the MDMKP is an optimization model that maximizes the total profit generated by all freight bookings accepted in a multi-period planning horizon subject to the limited shipping capacities. We propose two heuristic algorithms that can solve large scale problems with tens of thousands of decision variables in a short time. Finally, numerical experiments on a wide range of randomly generated problem instances are conducted to demonstrate the efficiency of the algorithms.