基于第三方物流的家具配送开放式车辆路径问题

为了降低家具配送成本,提高物流效率,基于第三方物流配送模式,构建了以总行驶距离最短和车辆数最少为最优目标的开放式车辆路径问题(open vehicle routing problem,OVRP)数学模型,并设计了一个改进的两阶段禁忌搜索算法进行求解,第1阶段求解包含所有客户的TSP(traveling salesman problem)路径来作为第2阶段划分OVRP路径的基础.设计了一个随机动态禁忌表,并将"邻域算子编号"和"邻域交换点对"同时作为禁忌对象,避免了过度禁忌的情况.另外,对5个邻域算子进行了测试,表明采用由点交换、分序点插入、点逆序和前点前向插入这4个算子组成的多邻域结构体效果最佳.经算例测试和文献对比,验证了设计算法的有效性,采用第三方物流配送比自营物流配送更节省成本.     

节点度估计和静态博弈转发策略的Ad Hoc网络路由协议

针对Ad Hoc网络路由发现过程中广播路由请求分组导致的广播风暴问题，提出了一种基于节点度估计和静态博弈转发策略的Ad Hoc网络路由协议NGRP.NGRP考虑边界影响，采用分段函数的思想将网络场景分为中心、边和角区域，分别估算网络中节点在不同区域的节点度，避免了周期性广播Hello消息获取节点度导致的开销；NGRP路由请求分组的转发采用静态博弈转发策略，利用节点度估算参与转发路由请求分组的节点数量，将转发和不转发作为策略集合，设计效益函数，通过纳什均衡获得节点转发路由请求分组的转发概率，从而减少了路由请求分组广播过程中产生的大量的冗余、竞争和冲突，提高了路由发现过程中路由请求分组的广播效率.运用NS-2对协议的性能进行大量的仿真，结果表明：NGRP的分组投递率、路由开销、MAC层路由开销和吞吐率这4项指标明显优于AODV+FDG，AODV with Hello和AODV without Hello协议.     

Deep reinforcement learning for a color-batching resequencing problem

In automotive paint shops, changes of colors between consecutive production orders cause costs for cleaning the painting robots. It is a significant task to re-sequence orders and group orders with identical color as a color batch to minimize the color changeover costs. In this paper, a Color-batching Resequencing Problem (CRP) with mix bank buffer systems is considered. We propose a Color-Histogram (CH) model to describe the CRP as a Markov decision process and a Deep Q-Network (DQN) algorithm to solve the CRP integrated with the virtual car resequencing technique. The CH model significantly reduces the number of possible actions of the DQN agent, so that the DQN algorithm can be applied to the CRP at a practical scale. A DQN agent is trained in a deep reinforcement learning environment to minimize the costs of color changeovers for the CRP. Two experiments with different assumptions on the order attribute distributions and cost metrics were conducted and evaluated. Experimental results show that the proposed approach outperformed conventional algorithms under both conditions. The proposed agent can run in real time on a regular personal computer with a GPU. Hence, the proposed approach can be readily applied in the production control of automotive paint shops to resolve order-resequencing problems.     

融媒体下新闻采编者的业务素养

随着新兴媒体的兴起与发展,传统的新闻媒体面临前所未有的机遇与挑战。融媒体时代的到来,实现了传统媒体与新兴媒体的融合发展,同时也对传统新闻媒体提出了更高要求。新闻采编者必须切实提升自身业务素养,方能适应新形势下的要求。着重论述了融媒体下新闻采编者业务素养存在的问题及应对措施。     

里程计辅助的高精度车载GNSS/INS组合导航系统

针对城市隧道、偏远山区等复杂路况下,卫星导航系统信号被遮挡较严重或无卫星导航系统信号的场景中,车载GNSS/INS组合导航系统精度下降的问题,提出一种里程计辅助的高精度车载GNSS/INS组合导航方法。该方法中的组合滤波模式可根据载车环境变化在GNSS/INS组合模式和DR/INS组合模式间实现自适应切换,该组合导航方法将三维里程计航位推算位置误差作为状态量扩充到常规组合导航滤波器中,里程计的标度因数误差、安装角误差可通过里程计误差标定方法离线精确得到,后续使用只需将里程计误差参数装订到组合导航系统中即可。车载试验表明,7 km的信号遮挡场景下组合导航系统单个方向上的位置误差最大值也不大于8 m,整个跑车过程中位置误差在3 m以内,进一步保证了车载GNSS/INS组合导航系统复杂路况下的高精度定位。     

喷丸奥氏体耐热钢抗蒸汽氧化性的研究与使用现状

奥氏体钢内壁喷丸处理作为一种新的提高材料抗蒸汽氧化性的技术，在超超临界火电机组受热面管的使用越来越广泛和成熟。概述了传统奥氏体钢在蒸汽氧化过程中氧化皮的形成结构，材料临界Cr含量 对富Cr层形成的影响，以及氧化皮结构与临界Cr含量 的关系。奥氏体钢内壁喷丸处理提高抗蒸汽氧化性的机理为：喷丸在管子内表面形成一层微米级厚度的喷丸层，喷丸层中晶粒碎化、晶格畸变、位错增多，在蒸汽氧化过程中，为基体内的Cr原子向表面扩散提供短路通道，使喷丸层表面Cr浓度提高，以形成富Cr层，减小氧化速率，提高抗蒸汽氧化性。归纳得出国内外关于喷丸奥氏体钢与传统钢管在600~750 ℃温度范围内，抗蒸汽氧化性的顺序依次为：600~700 ℃温度下，喷丸SUPER304(S30432)>喷丸TP347H≈喷丸TP321H≈TP310HCbN(HR3C)>SUPER304≈TP347HFG>TP304≈TP347H>TP321H；700~750 ℃下，TP310HCbN(HR3C)>喷丸TP347H>喷丸TP321H。同时归纳了喷丸工艺中，各因素对喷丸效果的影响，喷丸层质量的金相和硬度评价方法，以及目前喷丸奥氏体钢的实际应用现状。最后分析了喷丸奥氏体钢关于加工、焊接以及使用寿命等仍需研究解决的问题，展望了喷丸工艺向马氏体钢的推广使用。     

A油区清垢剂与缓蚀剂的优选分析

随着油田开发进程的深入,注水驱油早已成为最为常见的开采方式之一。实践表明,不论是从水体的配伍性还是经济性的角度来看,经过多重技术处理的采出污水都是回注水体的最佳选择。但是由于作业废水和废置液包含化学成分较多,常会因其间的相互反应而腐蚀堵塞管线等设备。因此,本文以A油区为例,基于其实际的生产状况,根据采出污水结垢趋势和腐蚀原因,进行清垢剂与缓蚀剂的优选,以期为实际生产提供一点理论参考。     

壳牌气化炉激冷口盘管损坏原因分析及改进

简要介绍壳牌气化炉结构及工艺流程、激冷口结构及存在问题;分析激冷口盘管损坏原因及如何避免;检修优化及取得的效果。     

Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, problem domain knowledge of NESs is investigated in this study, where we propose the incorporation of a variable reduction strategy (VRS) into EAs to solve NESs. The VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through variable relationships that exist in the equation systems. It enables the reduction of partial variables and equations and shrinks the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper mainly integrates the VRS into two existing state-of-the-art EA methods (i.e., MONES and DR-JADE) according to the integration framework of the VRS and EA, respectively. Experimental results show that, with the assistance of the VRS, the EA methods can produce better results than the original methods and other compared methods. Furthermore, extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.      

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.