网络计划编制系统中网络图的自动生成

本文从设计角度，系统地介绍了一个计算机网络计划编制系统中如何实现计算机辅助生成网络图和时间坐标网络图的方法与步骤。并阐述了资源优化的计算机实现。     

工程问题的Petri网模型及其分析

提出一种工程问题的Petri网模型及其构造方法,并且通过该Petri网模型及其可达标识图,给出了整个工程的关键路径和合理施工方案的求解方法.     

模糊计划评审技术(F-PERT)中关键路径的规划解法

研究了模糊计划评审技术中关键路径的求解方法 .首先建立了模糊计划网络图中寻找关键路径的规划模型 ,指出该规划模型在模糊排序规则下可以转化为多目标线性规划问题 ,并给出了该类多目标线性规划问题的求解步骤 .最后的算例表明 ,文中提出的方法是切实可行的 .     

改进的随机PERT下寻找关键路径的新方法

改进了随机PERT下寻找关键路径的方法.定义了随机路径时间变量的比较原则并据此确定概率关键路径;利用关于正态分布函数的不等式能对联合概率高精度估计的特性验证了概率关键指数较小时的概率关键路径的可信度;最后,实例阐明了随机关键路径方法.     

网络图修改方法的两个创新

随着社会的不断向前发展，网络图的应用越来越广泛。比如项目管理中的网络图、电网中的控制图和交通运输中的控制图等都是常见的网络图。网络图越复杂，修改起来越麻烦，本提出的网络图修改方法的两个创新能大大提高网络图修改方法的效率。网络图修改的第一个创新是网络图节点的高效删除．第二个创新是平行工序变为顺序工序的自动实现。     

网络计划图的工序关系及其复杂性研究

本文研究了将原始的施工工序关系表转换为规范网络计划图的活动关系的算法.在理论上讨论了网络图中活动与紧前活动的关系以及与节点的关系,研究了网络图中添加虚活动的规律,进一步提出了生成网络计划图的按先行工序类生成算法的补充研究.     

基于度与路径优先连接的集聚型供应链网络演化模型

集聚型供应链供应链网络具有无标度性、高集聚性等特征.以往研究忽视了供应链网络的高集聚性,使得供应链网络模型不能够准确刻画实际的集聚型供应链网络.本文在具体分析集聚型供应链网络动态演化特征的基础上,提出了基于度与路径优先连接的集聚型供应链网络演化模型,弥补了优先连接仅依赖于节点度值的不足.最后,对集聚型供应链网络的度分布、集聚系数和平均最短路径参数进行了数值模拟,模拟结果表明,该模型不仅能够反映集聚型供应链网络的无标度性,而且能够真实刻画其高集聚性特征.     

从一个编制计划的案例说网络计划法

网络计划法是以网络图反映、表达计划安排,据以选择最优工作方案,组织协调和控制生产(项目)的进度(时间)和费用(成本),使其达到预定目标,获得更佳经济效益的一种优化决策方法.     

一种计算工作时间参数新方法

为了编制和优化施工进度计划,计算构成施工项目的各项工作最早开始时间、最迟开始时间、最早完成时间、最迟完成时间、总时差和自由时差等时间参数十分重要.提出了一种计算工作时间参数新方法.该方法以工作完成时间为决策变量,通过建立和求解线性规划模型来得到各种工作时间参数.其建模思路清晰,不需绘制网络图,能用通用办公软件EXCEL求解.模拟计算表明,用该方法与用标准网络计划技术计算出的工作时间参数完全一致.     

模糊搭接网络计划图的新算法

改进了模糊网络时间参数的运算方法,使得模糊数比较大小时不再受隶属度值的影响,且模糊数的加减法运算也避免了模糊数值与展宽的负值现象.同时提出了搭接网络的新画法,将工序间的搭接关系转化为节点之间的搭接关系,解决了单代号搭接网络中的悖论现象,从而使网络图中工序时间参数的计算更加方便、准确.改进后的网络计划图能够更加科学地科学指导工程项目的实施,具有准确性与灵活性,能够有效避免因为工程脱离计划而造成的损失.     

基于Petri Net的需求流动网组建模型及其稳定性分析

针对供应链概念不足,提出需求流动网概念,并论述了需求流动网概念模型,运用Petri网理论建立需求流动网模型,并对模型进行了分析,随后在分析需求流动网稳定性内涵的基础上,进一步运用Petri网理论分析了需求流动网稳定性,并最终得出了相应结论.     

一类基于随机着色Petri网的多级供应链可靠性模型研究

由多种要索组成的供应链系统在实际运作过程中必须符合可靠性要求。供应链系统由于内外部的不确定性，特别是外部需求不确定性而与其他可靠性系统有所不同。本文在考虑其可靠性问题的条件下，提出了一种随机着色Petri网来对供应链系统可靠性问题进行建模分析。     

Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks

This paper illustrates a dynamic model of conditional value-at-risk (CVaR) measure for risk assessment and mitigation of hazardous material transportation in supply chain networks. The well-established market risk measure, CVaR, which is commonly used by financial institutions for portfolio optimizations, is investigated. In contrast to previous works, we consider CVaR as the main objective in the optimization of hazardous material (hazmat) transportation network. In addition to CVaR minimization and route planning of a supply chain network, the time scheduling of hazmat shipments is imposed and considered in the present study. Pertaining to the general dynamic risk model, we analyzed several scenarios involving a variety of hazmats and time schedules with respect to optimal route selection and CVaR minimization. A solution algorithm is then proposed for solving the model, with verifications made using numerical examples and sensitivity analysis.     

运输线路优化问题的最小路集算法

运输是物流的基本功能,有效地协调运输时间和运输成本两者间的关系,使其保持经济性和可靠性的均衡状态,是企业选择运输方式时必须考虑的重要因素.本文针对最大流量和最短路径的可靠性问题,引入最小路集算法,为描述运输线路正常运行的可靠程度,提供一种新的计算方法.     

Decentralized Dual-Based Algorithm for Computing Optimal Flows in a General Supply Chain

This paper addresses the issue of the optimal flow allocation in general supply chains. Our basic observation is that a distribution channel involving several reselling steps for a particular product can be viewed as a route in a supply chain network. The flow of goods or services along each route is influenced by the customer's demand, described by the corresponding utility functions, and prices charged at each node. We develop an optimization algorithm based on the primal-dual framework and the Newton's step that computes optimal prices at each node (dual problem) and then computes the optimal flow allocation (primal problem) based on these prices. Our main contribution is a discovery that the Newton's step leads to a partially decentralized algorithm which is a first step toward a decentralization schema for computing optimal prices.     

AN OPTIMIZATION APPROACH TO IDENTIFYING TIMBER SUPPLY FUNCTION COEFFICIENTS

This paper examines an optimization approach to identifying short-run timber supply function coefficients when the form of the supply function is known. By definition, a short-run timber supply function is a functional relationship between the optimal harvest level in each period (e.g., each year) and the actual forest-market state in the same period. The short-run timber supply function represents the optimal harvest decision policy, and therefore, the problem of optimal harvesting can be formulated as a problem of determining this function. When the form of the supply function is known, the problem becomes one of identifying the coefficients of the supply function. If the management objective is to maximize the expected present value of net revenues from timber harvesting over an infinite time horizon, and the timber price process is, in a sense, stationary, the supply function coefficients correspond to the optimal solution to an anticipative optimization problem. In this case, the supply function coefficients can be determined by maximizing the expected present value of the net revenues from timber harvesting, where periodic harvest levels are determined using the supply function. Numerical results show that the short-run supply functions determined using this approach gives good approximations of the true supply function.     

网点选择的聚类方法

本文介绍在网点选择问题中如何利用聚类分析方法，并且以宜昌市及巴东县为实例，给出这一区域的连锁网点     

Petri net approach to disassembly process planning for products with complex AND/OR precedence relationships

We present a Petri net (PN)-based approach to automatically generate disassembly process plans (DPPs) for product recycling or remanufacturing. We define an algorithm to generate a geometrically-based disassembly precedence matrix (DPM) from a CAD drawing of the product. We then define an algorithm to automatically generate a disassembly Petri net (DPN) from the DPM; the DPN is live, bounded, and reversible. The resulting DPN can be analyzed using the reachability tree method to generate feasible DPPs, and cost functions can be used to determine the optimal DPP. Since reachability tree generation is NP-complete, we develop a heuristic to dynamically explore the v likeliest lowest cost branches of the tree, to identify optimal or near-optimal DPPs. The cost function incorporates tool changes, changes in direction of movement, and individual part characteristics (e.g., hazardous). An example is used to illustrate the procedure. This approach can be used for products containing AND, OR, and complex AND/OR disassembly precedence relationships.     

Evaluation of choice set generation algorithms for route choice models

This paper discusses choice set generation and route choice model estimation for large-scale urban networks. Evaluating the effectiveness of Advanced Traveler Information Systems (ATIS) requires accurate models of how drivers choose routes based on their awareness of the roadway network and their perceptions of travel time. Many of the route choice models presented in the literature pay little attention to empirical estimation and validation procedures. In this paper, a route choice data set collected in Boston is described and the ability of several different route generation algorithms to produce paths similar to those observed in the survey is analyzed. The paper also presents estimation results of some route choice models recently developed using the data set collected.     

A framework for solving network revenue management problems with customer choice behavior

Revenue management is the process of understanding, anticipating and influencing consumer behavior in order to maximize revenue. Network revenue management models attempt to maximize revenue when customers buy bundles of multiple resources. The dependence among the resources in such cases is created by customer demand. Network revenue management can be formulated as a stochastic dynamic programming problem whose exact solution is computationally intractable. Solutions are based on approximations of various types. Customer choice behavior modeling has been gaining increasing attention in the revenue management. A framework for solving network revenue management problems with customer choice behavior is proposed. The modeling and solving framework is composed from three inter-related network structures: basic network model, Petri net, and neural net.