A dam-breach erosion model: I. Formulation

This paper, Part I in a series of two, develops a mathematical model for earthern dam breach erosion. This model constitutes an extension of the Breach Erosion of Earthfill Dams (BEED) model developed by Singh and Scarlatos (1987). Two aspects are emphasized: the evolution of the dam breach, and the subsequent flood and sediment routing. Simulation of dam breach evolution is based on hydrologic, geometric and geotechnic considerations. Einstein-Brown and Bagnold equations are utilized to compute the rate of erosion in the breached section. A water routing scheme, based on a modified version of the Muskingum method to simulate flow exchange between channel and floodplains, is used to route the resulting breach hydrograph. A sediment routing scheme based on the Muskingum method, modified to simulate deposition in floodplains, and deposition and scouring in the channel, is utilized to route the breach sediment graph. In Part II, the model is tested against historical dam failures, and an analysis is made to determine its sensitivity to various parameters.     

扩散波洪水演算研究进展

扩散波方程是通过忽略动力方程中的惯性项后与连续方程联立求解而得，经大量的实践证明不失为一种既具有足够精度又相对简单的洪水演算方法。近３０年来，该方法有了显的发展，中对扩散波方程的导出进行了比较全面的回顾，重点介绍了扩散波方程在各种条件下的解，对采用扩散波来近似进行洪水演算所造成的误差也作了简要的回顾，在此基础上分别进行了若干的评述。     

“92.8”高含沙洪水在黄河下游演进特性分析

“92.8”洪水位异常高的主要原因:一是前期连续几年枯水,造成河床严重淤积;二是本场洪水边滩淤积,河道过水面积减小。花园口站洪峰流量大于上站的主要原因是:主槽强烈冲刷,水位大幅度下降,漫滩水迅速回归主槽。     

Hybrid metaheuristic solutions to inventory location routing problem

This paper considers a supply chain network with multiple depots and geographically dispersed customers, each of which faces non-constant demand over a discrete planning horizon. The goal is to determine a set of depots to open, the delivery quantities to customers per period and the sequence in which they are replenished by a vehicle fleet such that the total system-wide cost is minimized. To solve it, first we construct a mixed integer program, and then propose a hybrid metaheuristic consisting of initialization, intensification and post-optimization. Results show that the proposed heuristic is considerably efficient and effective for many classical instances.     

Dynamic routing of time-sensitive air cargo using real-time information

The route planning of time-sensitive air-cargo is becoming more important with the growing air-network congestion and delays. We consider a freight forwarder’s routing of a time-sensitive air-cargo in the presence of real-time and historical information regarding flight availability, departure delays and travel times. A departure delay estimation model is developed to account for real-time information inaccuracy. A novel Markov decision model is formulated and solved with online backward induction. Through synthetic experiments and case studies, we demonstrate that dynamic routing with real-time information can improve delivery reliability and reduce expected cost.     

基于无交叉遗传算法的物流车辆路径问题研究

设计并实现了一种求解基本车辆路径问题的无交叉遗传算法,阐述了无交叉遗传算法求解物流车辆路径问题的关键技术,包括编码方案、初始种群生成方案、适应度函数、种群选择方案和变异操作等;并对比了使用目标函数倒数法和目标函数做差法构造适应度函数时的求解效果。实验结果表明,给出的无交叉遗传算法在求解车辆路径问题上具有良好的性能。     

Disaster‐Relief Logistics With Limited Fuel Supply

Disaster‐relief logistics consists of providing adequate emergency supplies rapidly to the affected people so as to minimize human suffering and death. This study empirically examines the impact that the shortage of fuel, a commonly encountered problem in many disaster situations, can have on the effectiveness of disaster‐relief logistics operations. We focus on investigating the following two issues: (1) whether the shortage of fuel is more damaging in attaining logistical goals than the equivalent‐sized shortage in emergency supplies themselves, and (2) what types of vehicles should be used when the fuel supply is limited. Results suggest that the shortage of fuel may be more damaging than the shortage of emergency supplies, and that smaller trucks may be preferred to larger trucks when the shortage of fuel becomes severe.     

SWARM INTELLIGENCE: APPLICATION OF THE ANT COLONY OPTIMIZATION ALGORITHM TO LOGISTICS‐ORIENTED VEHICLE ROUTING PROBLEMS

This research evaluates a set of logistics‐oriented vehicle routing problems (VRP) taken from the logistics and supply chain literature under the widely used Clark‐Wright Savings algorithm and the newer metaheuristic method employing a type of swarm intelligence called Ant Colony Optimization (ACO). ACO simulates the decision‐making processes of colonies of ants as they forage for food and is related to other artificial intelligence techniques such as Tabu Search, Simulated Annealing and Genetic Algorithms. Experimentation shows that ACO is successful in finding solutions near the best‐known solutions for problems with up to 20 demand locations. In addition, testing for the affect of spatial patterns suggested by the logistics literature for facility locations appears to make a difference in the quality of the solutions for the two algorithms. Finally, ACO is shown to be superior to the savings algorithm found in software packages and as a result should be tested on even larger, more complex logistics‐oriented vehicle routing problems, representative of those encountered in larger industrial and retail settings.     

从文化要素的设计视角谈 旅行社专线产品同质化现象的解决之道

此文从文化要素的角度重新审视了旅行社专线产品,在分析了旅行社专线产品的同质化现状和文化要素的内涵后,明确了注入文化要素在旅行社专线产品设计中的重要性,并面向实践针对性地提出了产品设计中文化要素的选择策略。     

Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space-time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.