需求依赖于服务水平的易变质品库存策略研究

本文研究需求依赖于上一周期服务水平、缺货时订单部分损失的两周期易变质品库存问题。分别考虑一次订货和多次订货两种情况,以平均利润最大化为目标构建库存模型,证明了模型解的存在性和唯一性,得到了最优库存服务水平和最优补货策略。最后,通过算例给出两个模型的应用,对重要参数进行了灵敏度分析,并且将两种模型的结果进行了对比分析。结果表明:订单损失率的增加会提高服务水平,但会使得利润降低;顾客期望服务水平的提高会降低第一阶段的服务水平,同时使利润减少;单位库存持有成本或变质率的增加会降低服务水平和平均利润。通常情况,企业通过多次订货能获得更大的利润,而只有当库存持有成本极小时,一次订购才能够获得更大的利润。同时,结果也表明:服务水平对库存策略有较大的影响,因此在进行库存决策时考虑服务水平具有重要的作用。     

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。     

求解带有非线性边界条件的涡流方程的A-φ解耦有限元格式

本文研究边界条件符合幂指数型非线性关系H × n = n × (|E × n|^α-1E × n)(0 < α ≤ 1)的涡流方程.使用A-φ耦合有限元格式数值求解这类问题具有较高精度,但计算开销大. A-φ解耦有限元计算格式能够在每个时间步上分别求解矢量A和标量φ,以此降低计算规模,提高计算效率.我们证明了解耦格式中解的存在唯一性,并且给出了它的误差估计.最后给出的数值实验证明了本文所提供的解耦算法是稳定和有效的.     

基于Backroom Effect的生鲜品零售库存决策优化

本文基于传统的Backroom Effect,考虑生鲜品零售中普遍存在的现象——仓库较之货架具有更为优越的腐损控制和保鲜能力,重新定义了仓库在保管生鲜品过程中存在的数量与保鲜方面的双重Backroom Effect。基于此,面向生鲜品零售商,研究包括仓库-货架补给决策以及订货补给的库存决策优化问题,并利用数值算例剖析了Backroom Effect下,货架与仓库存在的腐损率差异对零售商库存决策和相应利润的影响。文章的主要结论包括:生鲜零售商应在零售库存决策中充分考虑Backroom Effect的影响,借助其提高生鲜品的最优订货量,从而带来单位时间平均利润的提升;随着Backroom Effect效果的提升,零售商的最优订货量和对应的平均利润也随之增加;不同品类的生鲜品对Backroom Effect的敏感程度不同,零售商在进行仓库保鲜投资时应优先投资更易腐损且具有更高边际利润的生鲜品品种。     

需求依赖于展示区库存水平的易腐品订购和转运策略

本文主要研究易腐品零售商的订货和转运策略。零售商的库存分为两部分,即展示区/货架库存和仓库库存。零售商定期向供应商订货,零售商收到订购的商品首先将其中一部分商品存放在展示区中,余下的部分储存在仓库。展示区的空间是有限的,并且需求依赖于展示区商品的库存量。本文首先建立了以平均利润最大化为目标的库存优化模型并对模型最优解的存在性进行了分析,然后得到了求解最优订购量、转运量、转运时间间隔以及再订购点的算法,最后给出了不同参数条件下的算例。     

部分延迟订购的易变质品联合定价与生产策略

构建了一个需求同时依赖于销售价格和库存水平,生产率和变质率均为常数,允许缺货且缺货量部分延迟订购的易变质品联合定价与生产控制模型。首先证明了在销售价格给定的情况下,系统的总利润函数是关于生产计划的严格凹函数,平均利润函数是严格的伪凹函数,即存在唯一的最优解,并给出其充分条件。接着给出问题的一个数值求解算法。最后通过算例,展示了模型及相关算法的应用,并对相关参数进行了灵敏度分析,结果显示:当产品的生产成本、缺货成本和机会成本增加时,系统的平均利润将下降;生产成本和延迟订购阻力系数对最优定价和生产策略以及平均利润的影响较大。     

具有Weibull生存死亡特征的冷链品库存补货定价策略研究

建立了无限期内冷链品具有Weibull生存死亡特征、随机需求且受售价影响的库存补货定价模型,其中售价是连续变化的,需求率是售价的指数函数,变质率服从的三参数Weibull分布,提前期固定。系统以利润最大化为目标函数,在(r,Q)库存策略下,建立库存模型,采用直接法,对模型近似求解,得到最优补货定价策略。利用Matlab进行算例模拟和灵敏度分析发现:补货提前期和单位仓储成本对补货定价策略影响较大,二者增大会导致系统利润降低;单位处理成本的增加,在一定程度上使得系统降低最优补货量,使系统利润增加;保鲜期固定的前提下,受冷链品的流动环境因子和存储环境因子影响的变质率对补货定价策略影响较大,它的增大会使系统利润降低。这些发现能够帮助优化系统模型,对现实问题具有一定的指导意义。     

需求率基于库存的变质物品的两货栈模型

针对需求受库存水平影响的变质品的两货栈系统,本文首先考虑了允许缺货但缺货要补情形下,分别给出了L₁系统(库存系统只用自己的货栈存放物品)和L₂系统(库存系统使用自己货栈和租用货栈存放物品)时,销售商的库存和运输策略模型。其次给出了允许缺货但缺货不补L₁系统和L₂系统时,销售商的库存和运输策略,并对模型最优解作了相关分析,最后给出算例,给销售商实际的库存管理提供了理论依据。     

基于改进NSGA-II算法的云制造服务组合优化研究

云制造任务日趋复杂,与基于云制造的云服务组合优化问题相关的指标日益增多,需要综合考虑各个评价指标,从海量备选云服务中筛选出最优服务组合。本文针对云制造的特点,从线上、线下两方面构建了云制造服务评价指标体系;为了更好地处理高维多目标优化问题并消除实际问题中的量纲影响,本文利用改进的α支配策略代替帕累托支配改进NSGA-II算法,提出了基于nα支配的NSGA-II算法。最后,本文通过一个电机制造案例验证了提出算法的可行性,并通过与标准NSGA-II算法、r-NSGA-II算法和基于模糊支配的NSGA-II算法对比,证明了提出算法得到的解集更优、更小,能够大大减小后续组合优选的计算量。     

基于启发式Q学习的汽车涂装车间作业排序优化

针对汽车涂装车间中的作业优化排序问题,提出一种基于启发式Q学习的优化算法。首先,建立包括满足总装车间生产顺序和最小化喷枪颜色切换次数的多目标整数规划模型。将涂装作业优化排序问题抽象为马尔可夫过程,建立基于启发式Q算法的求解方法。通过具体案例,对比分析了启发式Q学习、Q学习、遗传算法三种方案的优劣。结果表明:在大规模问题域中,启发式Q学习算法具有寻优效率更高、效果更好的优势。本研究为机器学习算法在汽车涂装作业优化排序问题的应用提出了新思路。     

A Note on Strong Edge Coloring of Sparse Graphs

A strong edge coloring of a graph is a proper edge coloring where the edges at distance at most 2 receive distinct colors. The strong chromatic index χ'_s(G) of a graph G is the minimum number of colors used in a strong edge coloring of G. In an ordering Q of the vertices of G, the back degree of a vertex x of G in Q is the number of vertices adjacent to x, each of which has smaller index than x in Q. Let G be a graph of maximum degree Δ and maximum average degree at most 2 k. Yang and Zhu [J. Graph Theory, 83, 334–339(2016)] presented an algorithm that produces an ordering of the edges of G in which each edge has back degree at most 4 kΔ-2 k in the square of the line graph of G, implying that χ'_s(G) ≤ 4 kΔ-2 k + 1. In this note, we improve the algorithm of Yang and Zhu by introducing a new procedure dealing with local structures. Our algorithm generates an ordering of the edges of G in which each edge has back degree at most(4 k-1)Δ-2 k in the square of the line graph of G, implying that χ'_s(G) ≤(4 k-1)Δ-2 k + 1.     

Constructions of (q, K, λ, t, Q) almost difference families

The concept of a (q, k, λ, t) almost difference family (ADF) has been introduced and studied by C. Ding and J. Yin as a useful generalization of the concept of an almost difference set. In this paper, we consider, more generally, (q, K, λ, t, Q)-ADFs, where K = {k₁, k₂, ..., k_r} is a set of positive integers and Q = (q₁, q₂,..., q_r) is a given block-size distribution sequence. A necessary condition for the existence of a (q, K, λ, t, Q)-ADF is given, and several infinite classes of (q, K, λ, t, Q)-ADFs are constructed.     

物流配送装载率分析与四阶段算法研究

在城市物流配送中,租用车型的选择与车辆平均装载率具有密切的关系。然而,在带能力约束的车辆路径问题(Capacitated Vehicle Routing Problem, CVRP)中, 假设配送车辆装载量为事先已知。在实际物流配送中, 很多配送车辆为租用, 因此需要确定租用的车型大小。本文基于CVRP问题,假设配送车辆载量Q为变量,以车辆平均装载率为优化目标构建了数学模型. 通过数学推导证明了,派送车辆的平均装载率ρ的理论区间为(50%, 100%]。分析得出结论:当顾客需求数据中需求数据大于且接近0.5倍载量Q的越多,车辆平均装载率越低。为了验证分析结论的正确性, 分别设计一个求解CVRP问题的多阶段算法和具有大需求量的CVRP问题算例. 通过求解算例表明:本文理论分析的正确性, 其中四阶段算法的求解结果与当前已知最优解平均偏差仅为0.92%,达到优秀算法水平。     

Fast algorithm for viscous Cahn-Hilliard equation

The main purpose of this paper is to solve the viscous Cahn-Hilliard equation via a fast algorithm based on the two time-mesh (TT-M) finite element (FE) method to ease the problem caused by strong nonlinearities. The TT-M FE algorithm includes the following main computing steps. First, a nonlinear FE method is applied on a coarse time-mesh τ_c. Here, the FE method is used for spatial discretization and the implicit second-order θ scheme (containing both implicit Crank-Nicolson and second-order backward difference) is used for temporal discretization. Second, based on the chosen initial iterative value, a linearized FE system on time fine mesh is solved, where some useful coarse numerical solutions are found by Lagrange’s interpolation formula. The analysis for both stability and a priori error estimates is made in detail. Numerical examples are given to demonstrate the validity of the proposed algorithm. Our algorithm is compared with the traditional Galerkin FE method and it is evident that our fast algorithm can save computational time.     

强化学习在运筹学的应用:研究进展与展望

强化学习已经成为人工智能领域一个新的研究热点,并已成功应用于各领域,强化学习将运筹优化领域的很多问题视为序贯决策问题,建模为马尔可夫决策过程并进行求解,在求解复杂、动态、随机运筹优化问题具有较大的优势。本文主要对强化学习在运筹优化领域的应用进行综述,首先介绍了强化学习的基本原理及其应用于运筹优化领域的研究框架,然后回顾并总结了强化学习在库存控制、路径优化、装箱配载和车间作业调度等方面的研究成果,并将最新的深度强化学习以及传统方法在运筹学领域的应用研究进行了对比分析,以突出深度强化学习的优越性。最后提出几个值得进一步探讨的研究方向,期望能为强化学习在运筹优化领域的研究提供参考。     

Complementarity forms of theorems of Lyapunov and Stein, and related results

The well-known Lyapunov's theorem in matrix theory / continuous dynamical systems asserts that a (complex) square matrix A is positive stable (i.e., all eigenvalues lie in the open right-half plane) if and only if there exists a positive definite matrix X such that AX+XA^* is positive definite. In this paper, we prove a complementarity form of this theorem: A is positive stable if and only if for any Hermitian matrix Q, there exists a positive semidefinite matrix X such that AX+XA^*+Q is positive semidefinite and X[AX+XA^*+Q]=0. By considering cone complementarity problems corresponding to linear transformations of the form I−S, we show that a (complex) matrix A has all eigenvalues in the open unit disk of the complex plane if and only if for every Hermitian matrix Q, there exists a positive semidefinite matrix X such that X−AXA^*+Q is positive semidefinite and X[X−AXA^*+Q]=0. By specializing Q (to −I), we deduce the well known Stein's theorem in discrete linear dynamical systems: A has all eigenvalues in the open unit disk if and only if there exists a positive definite matrix X such that X−AXA^* is positive definite.     

The mixed volume optimization problem

The mixed volume optimization problem is to determine the point of duality Q for a given convex set K that minimizes the “mixed volume” of the associated polar set (K^*;Q). In the plane, the mixed volumes translate as the area and length; in space, the mixed volumes include the volume, surface area, and mean width. In this paper, the geometric optimization problems associated with minimizing mixed volumes are examined from two perspectives: enumerative search and symbolic computation. The problem of minimizing the polar area through an enumerative search is first considered. The dual polygon (P_m^*;Q) is constructed for an arbitrary point of duality QP_m° by using an algebraic correspondence between the edges of P_m and the vertices of (P_m^*;Q), and the area of (P_m^*;Q), A(P^*_m;Q), is calculated and minimized using naive search techniques. A result due to Santaló is applied to verify the minimizing solution, and computational tests are described for various classes of randomly generated polygons. Statistical evidence indicates that a “good” approximation to the minimum area polar polygon occurs when the duality point is located at the center-of-gravity of P_m. The polar area problem is then investigated using symbolic procedures. Explicit symbolic expressions for the polar area and length functionals are computed and solved directly using the differential optimality conditions and Newton's iterative method of solution. The mixed volume and surface area functionals are formulated and solved using numerical products, and the mean width functional is described. Examples are used throughout to illustratethe methodology.     

重大事故规避的危险品运输车辆路径优化研究

基于重大事故规避的思想,建立以最大事故后果最小及运输成本最小为双目标,且事故后果基于实时装载量的危险品运输车辆路径优化模型。基于ε-约束法,设计可求得帕累托最优解的精确算法,该算法包含通过性质求ε下界、规避被支配解的预处理及不可行路径禁止约束3处改进。进一步设计处理大规模问题的多项式时间近似算法,并分析了算法的近似比。最后通过算例对模型和算法进行测试,并通过出灵敏度分析给出管理启示。