基于遗传算法的具有主次仓库的企业资源供应链管理模型研究

本文研究如何在有容量限制的主次仓库间分配客户需求的企业资源供应链管理问题,当主仓库不能满足客户需求时,通过一个次仓库弥补主仓库的不足。首先把该问题构造成一个整数规划模型,其次,运用遗传算法加以解决,实际应用和数值分析说明了该遗传算法在解决这类问题中的有效性和实用生。     

级库存(R,Q)策略的简单应用

考查由一个中心仓库和多个零售商构成的两级供应链配送系统,研究基于泊松需求假设的级库存(R,Q)策略的优化和应用.分析中心仓库在随机的提前期内的需求分布和缺货引起的零售商订货时延,提出优化级库存订货点的迭代计算算法.最后给出了具体的算例.     

市场规模、范围经济与供应链聚集可能性

将供应链聚集看作多客户外包的结果,建立一个两客户"自制-外包"决策博弈模型,即两个企业各自独立做出是否将生产外包给一个拥有相同生产技术但存在范围经济的承包商.对均衡每件的分析表明,当范围经济程度与市场规模的比值达到一定程度时,两客户均做出外包决策是一个纳什均衡,从而多客户集体外包(供应链聚集)成为可能.当这一比值进一步增大,供应链聚集下的产品市场价格要低于厂商各自独立进行生产时的产品市场价格.由于可能存在巨额内部交易成本,使得三者一体化不是一个必然的最优结果.这些结果一方面给出了供应链聚集的一个替代原因,另一方面也可用来解释"中国价格"这一广泛观察到的现象.     

供应链库存分散决策研究

本文研究两级供应链库存问题,供应链包含一个客户和一个生产商.通过建立客户订货库存模型和生产商生产库存模型得到供应链库存模型.采用分散决策方法获得最优解,即最佳订货量和最佳订货次数.在此基础上,使用算例对最优解进行了数值分析.     

供应链下游订单处置能力扩张协调策略研究

在一个由供应商和零售商组成的供应链系统中,需求通常与零售商向客户承诺的交货期和产品价格相关,产品价格又与交货期相关.基于此,建立零售商和供应链向客户承诺的交货提前期、产品价格和能力扩张联合决策模型.研究结果表明,与零售商单独决策相比,在供应链一体化决策情形下,如果终端客户为提前期敏感型,零售商向客户承诺的交货提前期越短产品价格越高;如果终端客户为价格敏感型,则其向客户承诺的交货提前期越长产品价格越低;不论是何种类型的客户,随着零售商对交货提前期和产品价格的调整,市场需求都会增加,并要求零售商扩张其能力.为了使供应链利益最优和自身受益,供应商可以通过对零售商的能力扩张进行补贴的方式激励零售商调整其向客户承诺的交货期和产品价格,并进行相应的能力扩张,而产品转移价格越高,供应链通过协调获得的增量利润也越多.     

考虑市场细分的零售商自主以旧换新策略研究

在越来越多企业开始自主推行以旧换新业务的背景下,本文从供应链的视角出发,构建考虑市场细分和消费者效用的策略模型,研究零售商自主以旧换新策略选择及相应的供应链决策问题,着重分析市场细分和旧产品折旧程度对策略选择、决策和供应链效率的影响.研究发现:零售商的策略选择除了受产品制造成本影响外,还依赖于外部客户比例和旧产品折旧程度;当老客户占比和旧产品折旧度处于某区域时,自主以旧换新策略优于无以旧换新策略;在某些情况下,制造商的期望策略与零售商的最优策略相悖;供应链效率受客户比例和旧产品折旧度影响.此外,当老客户占比或旧产品折旧度高于某阈值时,老客户对主体的利润贡献大于新客户.本文的研究结论对企业在供应链环境下推行以旧换新政策有一定的理论指导意义.     

基于客户需求的供应链协同研究

供应链协同的目的在于有效利用和整合供应链整体的资源,提高竞争力,实现共同利益的最大化,因而也成为了供应链管理最为现实的模式。本文在介绍供应链协同的基本原理和概括供应链协同的主要优势的基础上,深入分析了客户需求对供应链协同的影响,构建了基于客户需求的供应链协同的模型,并提出基于客户需求的供应链协同实现方法。     

定置管理在供电企业标准化仓库建设中的应用

仓储管理作为供应链的核心环节之一,其管理水平直接关系着供应链的效率。有些供电企业在仓储管理中,存在物资摆放较为混乱,需求时难以找寻等现象,既增加了仓库管理的工作量,影响了其运转效率,也限制了仓库专业能力的发挥，不利于仓库实现现代化管理。定置管理作为一种科学化的管理技术，为供电企业建设标准化仓库提供了一条有效的实施途径。     

绿色运输的路径优化问题分析

在绿色供应链管理中,研究绿色运输的路径优化问题并选取恰当的配送路径,可以加快对客户需求的响应速度,提高服务质量,增强客户对供应链环节的满意度,降低服务商运作成本.本文采用遗传算法对绿色供应链中的运输问题进行分析.并利用VC较好地进行了实例验证,对企业实现科学快捷的配送调度和路径的优化具有实际意义.     

供应链库存模型联合决策的研究

本文对由一个供应商和多个客户组成的两级供应链进行了研究,建立了以平均库存成本为目标函数的供应链管理库存模型。分别采用了分散决策和联合决策对目标函数进行优化,获得了供应商的最佳生产时间间隔和客户的最佳订货次数。为了保证供应链中各成员均可获利,在联合决策模型中引入了补贴机制。数据分析和敏感性分析表明,基于补贴机制的联合决策要明显优于分散决策,在联合决策下供应链各成员平均库存成本显著降低。     

A STOCHASTIC DOMINANCE ALGORITHM USING PIECEWISE LINEAR APPROXIMATIONS

Current stochastic dominance algorithms use step functions to approximate the cumulative distributions of the alternatives, even when the underlying random variables are known to be continuous. Since stochastic dominance tests require repeated integration of the cumulative distribution functions, a compounding of errors may result from this type of approximation. This article introduces a new stochastic dominance algorithm that approximates the cumulative distribution function by piecewise linear approximations. Comparisons between the new and old algorithms are performed for normally distributed alternatives. In about 95 percent of all cases, the two algorithms produce the same result.     

基于求解TSP问题的双向扩展差额算法

旅行商(TSP)问题是典型的组合优化中的NP-hard难题.本文在最近城市搜索法和两端延伸最近城市搜索法基础上提出了双向扩展差额求解算法,并分析了算法的复杂度.采用以上三种算法求解了TSPLIB标准库中多个算例,比较结果表明本算法能够更快的找到更优的方案,具有更好的综合性能.     

Scheduling and the problem of computational complexity

Why is it that the problem of scheduling is so computationally difficult to solve? At last recent developments in modern mathematical complexity theory are providing some insights. The paper describes in essentially non-mathematical terms the computational technique known as the ‘Branch and Bound Method’. This, the best general optimising technique available for scheduling, is also shown to have its limitations. It now appears that efficient computational and optimising algorithms are unlikely ever to be found for all except special cases of the general industrial scheduling problem. It seems that heuristic (rule-of-thumb) methods leading to approximate solutions are likely to offer the only real promise for the future.     

运输网络运量分配问题的模型及算法研究

针对我国在运量分配模型及算法方面研究比较薄弱的现状,本文对此问题进行了系统深人的研究,应用运筹学、计算机科学的新的理论和方法,建立了多目标运量分配优化模型,且在模型中,将一些重要特性考虑成运输流量的函数,从而可使分配结果更符合实际情况。同时为求解该模型,本文研究设计了鲁棒性强、高效、实用的自适应搜索算法。     

MODIFIED STEPPING-STONE METHOD AS A TEACHING AID FOR CAPACITATED TRANSPORTATION PROBLEMS

This paper extends the standard stepping-stone method to the case of capacitated transportation problems. The purpose is to offer an alternative to network-oriented or simplex-based methods (such as the out-of-kilter and upper-bounding techniques) often used in handling the capacitated transportation problem but generally beyond the scope of introductory decision sciences courses. The proposed procedure is based on the simple stepping-stone method and requires only a few modifications in the cost/assignment matrix and some alterations in the pivoting rules. As such, it is much easier to understand and apply than the above-mentioned solution algorithms. It therefore can serve as an effective tool for teaching capacitated transportation problems. Two numerical examples illustrate applications of the proposed method, procedures for sensitivity analysis, and comparisons with linear programming.     

Approximate versus Exact Equilibria in Dynamic Economies

This paper develops theoretical foundations for an error analysis of approximate equilibria in dynamic stochastic general equilibrium models with heterogeneous agents and incomplete financial markets. While there are several algorithms that compute prices and allocations for which agents' first‐order conditions are approximately satisfied (“approximate equilibria”), there are few results on how to interpret the errors in these candidate solutions and how to relate the computed allocations and prices to exact equilibrium allocations and prices. We give a simple example to illustrate that approximate equilibria might be very far from exact equilibria. We then interpret approximate equilibria as equilibria for close‐by economies; that is, for economies with close‐by individual endowments and preferences. We present an error analysis for two models that are commonly used in applications, an overlapping generations (OLG) model with stochastic production and an asset pricing model with infinitely lived agents. We provide sufficient conditions that ensure that approximate equilibria are close to exact equilibria of close‐by economies. Numerical examples illustrate the analysis.     

Modeling fixed-charge problems with polynomials

In this paper we formulate fixed-charge problems with polynomials. Using polynomial formulations we show structural similarity between different kinds of linear and fixed charge formulations. We also show the benefits of applying polynomial formulation for finding an approximate solution for problems where no algorithms exist and in some cases for developing a method to provide direct solutions to those problems. The main benefit of this paper is better understanding of the fixed-charge function structure and better explanation of the local and global minima phenomena. We present a numerical example to illustrate applications of the proposed method.     

Approximation Algorithms for the Multiple Knapsack Problem with Assignment Restrictions

Motivated by a real world application, we study the multiple knapsack problem with assignment restrictions (MKAR). We are given a set of items, each with a positive real weight, and a set of knapsacks, each with a positive real capacity. In addition, for each item a set of knapsacks that can hold that item is specified. In a feasible assignment of items to knapsacks, each item is assigned to at most one knapsack, assignment restrictions are satisfied, and knapsack capacities are not exceeded. We consider the objectives of maximizing assigned weight and minimizing utilized capacity.We focus on obtaining approximate solutions in polynomial computational time. We show that simple greedy approaches yield 1/3-approximation algorithms for the objective of maximizing assigned weight. We give two different 1/2-approximation algorithms: the first one solves single knapsack problems successively and the second one is based on rounding the LP relaxation solution. For the bicriteria problem of minimizing utilized capacity subject to a minimum requirement on assigned weight, we give an (1/3,2)-approximation algorithm.     

Fair task allocation in transportation

Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial-time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost.     

大规模灾害中基于聚类的医疗物资联合运送优化

应急响应中,往往出现救援物资供应节点与需求节点距离太远、关键道路损毁导致难以及时通过车辆运送物资到灾区等情景,此时直升飞机逐渐被用来运送关键应急资源（如医疗物资及医护人员）。然而,大规模灾害中难以使用直升飞机运送医疗物资到每个医疗救助点,通常考虑灾民的聚集性选择一定数量的应急中转点,以接收直升飞机运送的医疗物资,之后采用车辆运送物资到其覆盖的医疗救助点。针对该问题,提出一种基于聚类的两阶段医疗物资联合运输方法：第一阶段根据医疗救助点分布,采用模糊C-均值算法（FCM）进行应急中转点选择和医疗救助点划分,并针对FCM划分中存在的剩余容量不均衡问题,考虑容量约束提出一种改进划分方法（FCMwCC）,构建“直升飞机-车辆”医疗物资联合运输网络结构;第二阶段建立基于聚类的运送路线优化模型,确定从应急中转点到医疗救助点的具体运送路线。数值实验验证了提出方法和算法的有效性。