路径长度受限的随机需求VRP的两阶段模拟退火方法

路径长度受限的随机需求下的车辆路径问题(PSVRP)是一种普遍存在而求解困难的运筹学问题.在满足路径长度限制、只允许服务失败一次和不允许部分服务的策略下设计了一个两阶段模拟退火算法,根据模拟退火的理论容易知道算法求到的近似最优解是以概率1收敛的.对随机生成的中等规模和大规模问题做了数值试验,试验结果表明该算法有效.     

随机需求VRP的一个算法

研究了随机需求VRP问题,在服务仅能路由失败一次和不允许部分服务的情况下,给出了两阶段Clarke-Wright模拟退火算法。对需求为均匀分布的VRP问题做了数值试验,给出了具体的方案。数值结果表明这个算法比简单的两阶段模拟退火算法好,而且算法简单,容易实现。这个算法在第Ⅱ阶段模拟退火算法开始时给出了一个比较合理的初始解,较好地处理了大范围交叉的问题,从而使得模拟退火算法快速收敛到近似最优解。     

路径长度受限的随机需求VRP的节省算法及其策略分析

在保证每条路径长度限制,至多只能服务失败一次和不允许部分服务的策略下,定义了一个新的节省路径,给出了一个精确的节省算法,对中等规模和大规模问题进行了数值试验,数值试验表明算法有效。对所采用的策略进行了分析,得到了一些理论结果。     

塑料制件最大流长比算法研究与应用

提出了计算塑料制件最大流长比(流动长度与厚度的比值)的算法。该算法利用三角形网格模型表示复杂制件的中面模型，通过对三角单元的带权边进行细分，由细分得到的点、边和原三角网格模型上的节点构成带权图G，采用快速最短路径法(FSPA)动态计算带权图上两点的最短路径，从而得到制件上由一点到任意点的最短路径值即流长比。该算法效率高，容易实现，可通过改善网格质量和调整细分参数γ控制近似精度。给出了该算法的两个应用实例。     

自动铺带轨迹规划中测地线算法研究

本文针对复合材料自动铺放轨迹规划中自然路径求解中的测地线问题给出了求解两点之间的测地线的数值计算方法。该方法将微分方程的边值问题转化为初值问题,在初始方向的选取上较以往方法几何意义更为直观,算例证明算法的正确性。     

注塑成型Hele-Shaw流动模拟中热对流的异步长求解

在Hele-Shaw流动数值模拟中,速度是压力的后处理结果。如果是点浇口,则浇口附近速度会随单元尺寸缩小而趋于无穷大,导致能量方程作为一个整体求解时,时间步长必须非常小,否则会产生很大误差;而根据热对流物理意义分步求解,则需追踪当前物质在上一时刻位置,当单元速度很高、逆向搜索需穿透多个单元时,搜索可能会失败。鉴于此,基于分步求解法,研究提出一种变长度子时间步长方法处理对流项,确保搜索路径局限在当前单元内,并采用二分法确定子时间步数量,使算法简洁有效。算例表明,该方法在保证计算精度和求解稳定性的同时,可以明显减少计算时间,提高计算效率。     

基于VRP和3dsMax构建SDS-聚丙烯酰胺凝胶电泳虚拟实验

随着互联网和计算机技术的飞速发展,虚拟实验以其沉浸感和交互性强、不消耗实验资源等特点在许多领域得到了应用开发。实验教学是高校教学中的一个重要组成部分,虚拟实验在高校实验教学中受到了越来越多的重视。文章以构建SDS-聚丙烯酰胺虚拟实验为例,探讨了基于VRP和3dsMax技术构建该虚拟实验的方法。结果表明,利用VRP编辑器和3dsMax构建的模型软件简洁,系统操作简单,界面真实、有立体感。     

注塑成型Hele-Shaw流动模拟中热对流的异步长求解

在Hele-Shaw流动数值模拟中,速度是压力的后处理结果。如果是点浇口,则浇口附近速度会随单元尺寸缩小而趋于无穷大,导致能量方程作为一个整体求解时,时间步长必须非常小,否则会产生很大误差;而根据热对流物理意义分步求解,则需追踪当前物质在上一时刻位置,当单元速度很高、逆向搜索需穿透多个单元时,搜索可能会失败。鉴于此,基于分步求解法,研究提出一种变长度子时间步长方法处理对流项,确保搜索路径局限在当前单元内,并采用二分法确定子时间步数量,使算法简洁有效。算例表明,该方法在保证计算精度和求解稳定性的同时,可以明显减少计算时间,提高计算效率。     

基于MFDA的井下排水过程监控算法研究

针对现有泵房监控系统运行可靠性差、兼容性不强、扩展能力受限的问题,分析了排水系统特点和控制规律,总结了影响排水系统工作性能的因素,提出了基于HHT和FDA的MFDA排水过程故障监测算法,对监测到的主要过程量进行HHT变换,在多个尺度上建立FDA模型,该算法可较好地识别出故障工况,提高了故障监测准确率。     

基于纯追踪曲线跟踪模型的车辆自动导航

介绍具有仿生性的基于纯追踪曲线跟踪模型的曲线跟踪算法。该算法以简化二轮车模型为基础,通过选择合适前视距离计算车辆逼近预定义路径所需摆角达到跟踪效果。通过MATLAB/Simulink仿真,对算法中前视距离和车辆速度两个重要参数对车辆跟踪效果的影响进行了仿真探讨。仿真结果说明该算法容易实现,稳定性高,跟踪预定义路径所需时间较短,跟踪精度较高。     

AN EFFICIENT META-HEURISTIC ALGORITHM FOR ROUTING PRODUCT COLLECTING VEHICLES OF DEHYDRATION PLANTS. II. ALGORITHM PERFORMANCE AND CASE STUDIES

Routing of vehicle fleet for collecting newly cropped raw materials for multi-product dehydration plants is a component of plant production schedule of utmost significance. A meta-heuristic algorithm for efficiently solving the collecting vehicle routing problem was developed and analyzed in detail in Tarantilis and Kiranoudis (2000). Meta-heuristic algorithms are broadly characterized by a stochastic nature in producing tender solution configurations in linear search terms, which sweep the huge solution space in a guided and rational way. Algorithm performance is examined through an analysis of the impact of model parameters on solution procedure during the execution of typical routing problems. The most important model parameter examined was found to be the value of the initial threshold as well as the way that the value of this actual parameter is appropriately adjusted during the optimization process. The main characteristic of the algorithm is the way that threshold is not only lowered but also raised, or backtracked, depending on the success of the inner loop iterations to provide for an acceptable new solution that would replace an older one. An important feature of the algorithm is the fact that appearance of better configurations within a process run is distributed according to the Poisson probability distribution. The suggested algorithm is tested against typical literature benchmarks as well against real-world problem encountered in the production planning procedures of dehydration plants in Greece.     

AN EFFICIENT META-HEURISTIC ALGORITHM FOR ROUTING PRODUCT COLLECTING VEHICLES OF DEHYDRATION PLANTS. II. ALGORITHM PERFORMANCE AND CASE STUDIES

Routing of vehicle fleet for collecting newly cropped raw materials for multi-product dehydration plants is a component of plant production schedule of utmost significance. A meta-heuristic algorithm for efficiently solving the collecting vehicle routing problem was developed and analyzed in detail in Tarantilis and Kiranoudis (2000). Meta-heuristic algorithms are broadly characterized by a stochastic nature in producing tender solution configurations in linear search terms, which sweep the huge solution space in a guided and rational way. Algorithm performance is examined through an analysis of the impact of model parameters on solution procedure during the execution of typical routing problems. The most important model parameter examined was found to be the value of the initial threshold as well as the way that the value of this actual parameter is appropriately adjusted during the optimization process. The main characteristic of the algorithm is the way that threshold is not only lowered but also raised, or backtracked, depending on the success of the inner loop iterations to provide for an acceptable new solution that would replace an older one. An important feature of the algorithm is the fact that appearance of better configurations within a process run is distributed according to the Poisson probability distribution. The suggested algorithm is tested against typical literature benchmarks as well against real-world problem encountered in the production planning procedures of dehydration plants in Greece.     

The multi-echelon vehicle routing problem with cross docking in supply chain management

Multi-echelon distribution networks are quite common in supply chain and logistics. Deliveries of multiple items from factories to customers are managed by routing and consolidating shipments in warehouses carrying on long-term inventories. On the other hand, cross-docking is a logistics technique that differs from warehousing because products are no longer stored at intermediate depots. Instead, cross-dock facilities consolidate incoming shipments based on customer demands and immediately deliver them to their destinations. Hybrid strategies combining direct shipping, warehousing and cross-docking are usually applied in real-world distribution systems. This work deals with the operational management of hybrid multi-echelon multi-item distribution networks. The goal of the N-echelon vehicle routing problem with cross-docking in supply chain management (the VRPCD-SCM problem) consists of satisfying customer demands at minimum total transportation cost. A monolithic optimization framework for the VRPCD-SCM based on a mixed-integer linear mathematical formulation is presented. Computational results for several problem instances are reported.     

分级优化用于边值固定的化工动态优化问题

针对边值固定的化工动态优化问题, 提出了分级优化策略, 包括约束优先与目标优先两种方案, 它们的基本思想是将原问题转化为一系列的边值无约束问题, 采用目前成熟的优化算法加以集成即可实现. 对于控制变量受箱型约束的问题, 采用三角函数转换将其转化为控制无约束问题. 分级优化策略避免了罚函数策略的缺陷. 实例研究显示了分级优化策略能以足够的精度满足边值约束, 三角函数转换法是可行的.     

TS法的改进及其在求解化工优化问题中的应用

TS算法属于现代优化算法,是局部领域搜索法的推广,常用于求解组合优化问题.利用TS法搜索过程的有向性和能够跳离局部最优解的特点,对其进行了改造,以适应求解连续变量化工优化问题.首先,根据化工优化问题变量的特性,提出了一种简便的邻域映射方案,并改进了迭代过程中自适应因子的下降函数;进一步分析对比了禁忌步数、自适应因子和初始解等参数对于优化结果的影响.然后通过算例和换热网络优化问题的求解,表明改造后的TS法在求解连续变量化工优化问题中的有效性,及其在化工优化领域的发展价值.     

基于模腔压力的注塑过程注射速度设定曲线优化

注塑成型中,把熔前速度恒定(熔体均匀填充模具)作为设定注射速度参数的重要依据。针对熔前速度无法测量问题,提出对于一类注塑制品,用模腔压力随时间线性增加来近似熔前速度恒定。其次,利用实验数据,设计并建立了模腔压力递归神经网络(RNN)模型。在此基础上,提出一种注射速度设定曲线的分级优化策略,其中曲线分级是利用Douglas-Peucker方法自动确定速度分级数目和位置,优化过程则采取基于滑动窗口的迭代优化方法来实现。实验结果表明,该优化方法可以根据模腔复杂程度自动选择合适的注射曲线分级数目,并且其优化精度较常规等间隔分级优化方法更高。     

The ascend-II system—a flowsheeting application of a successive quadratic programming methodology

This paper describes ASCEND-II, an interactive equation based flowsheeting system capable of doing performance, design and optimization calculations with comparable ease. The optimization capability is a new, as yet unpublished, successive quadratic programming method operating in the reduced space which is the size of the degrees of freedom for the problem. Its performance is very encouraging.We spend considerable time describing the ASCEND-II system details because we believe the concepts underlying it make the system a powerful aid for design. It allows one to evolve toward the solution to a problem by allowing one to add and delete model complexity, interactively alter which variables are to be calculated and which fixed, single step through the convergence of a model, move easily from design to optimization and back, use any variable as an objective to be maximized or minimized, and to place an upper or lower bound on any variable desired.     

Determination of biogeochemical parameters in eutrophication models with simultaneous dynamic optimization approaches

This work addresses a parameter estimation problem in an ecological water quality model through a simultaneous dynamic optimization approach. The model is based on first principles and has a large number of parameters, which must be estimated based on data collected in the water body under study. Gradients of state variables are considered along the water column, rendering a partial differential equation problem, which is transformed into a differential algebraic (DAE) one by spatial discretization in several water layers. Within a simultaneous approach, the DAE constrained optimization problem is transformed into a large-scale nonlinear programming problem, with a weighted least squares objective function. Main biogeochemical parameters have been obtained, which allow a close representation of the lake dynamics, as it is shown in the numerical results.     

Solving Multi‐objective MILP Problems in Process Synthesis using the Multi‐Criteria Branch and Bound Algorithm

The paper briefly describes the problem of process synthesis in the area of chemical engineering, and suggests its formulation as a Multi‐Objective Programming problem. Process synthesis optimization is usually modeled as Mixed Integer Linear Programming (MILP) or Mixed Integer Non‐Linear Programming (MINLP) with an economic objective function. We claim that incorporating more criteria (e.g., environmental criteria) in this kind of combinatorial optimization problem offers the decision makers the opportunity to refine their final decision by examining more than one solution (a set of efficient or Pareto optimal solutions instead of one optimal solution). For solving the multi‐objective process synthesis problem, an improved version of the Multi‐Criteria Branch and Bound (MCBB) algorithm, which has been developed by the same authors, is used. MCBB is a vector maximization algorithm capable of deriving all efficient points (supported and unsupported), for small and medium sized Multi‐Objective MILP problems. The application of MCBB in two examples from process synthesis is also presented.