Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

A case-deletion diagnostic for penalized calibration estimators and BLUP under linear mixed models in survey sampling

The penalized calibration technique in survey sampling combines usual calibration and soft calibration by introducing a penalty term. Certain relevant estimates in survey sampling can be considered as penalized calibration estimates obtained as particular cases from an optimization problem with a common basic structure. In this framework, a case deletion diagnostic is proposed for a class of penalized calibration estimators including both design-based and model-based estimators. The diagnostic compares finite population parameter estimates and can be calculated from quantities related to the full data set. The resulting diagnostic is a function of the residual and leverage, as other diagnostics in regression models, and of the calibration weight, a singular feature in survey sampling. Moreover, a particular case, which includes the basic unit level model for small area estimation, is considered. Both a real and an artificial example are included to illustrate the diagnostic proposed. The results obtained clearly show that the proposed diagnostic depends on the calibration and soft-calibration variables, on the penalization term, as well as on the parameter to estimate.     

Quantum computation based bundling optimization for combinatorial auction in freight service procurements

Combinatorial auction is a useful trade manner for transportation service procurements in e-marketplaces. To enhance the competition of combinatorial auction, a novel auction mechanism of two-round bidding with bundling optimization is proposed. As the recommended the auction mechanism, the shipper/auctioneer integrates the objects into several bundles based on the bidding results of first round auction. Then, carriers/bidders bid for the object bundles in second round. The bundling optimization is described as a multi-objective model with two criteria on price complementation and combination consistency. A Quantum Evolutionary Algorithm (QEA) with β-based rotation gate and the encoding scheme based on non-zero elements in complementary coefficient matrix is developed for the model solution. Comparing with a Contrast Genetic Algorithm, QEA can achieve better computational performances for small and middle size problems.     

埋地管道泄漏内封堵装置设计与研究

针对流体输送埋地管道泄漏问题,设计了一种利用管道机器人携带封堵气囊进行快速应急封堵修复的埋地管道泄漏内封堵装置。采用矩阵变换方法建立了牵引系统驱动轮过弯方程,利用MATLAB软件对过弯方程进行了验证,同时利用ADAMS软件仿真分析过弯路径与驱动轮转角对牵引系统行走速度的影响。研究结果表明:由两个串联封堵器组成的应急封堵系统可满足复杂工况下的管道泄漏封堵要求;牵引系统驱动轮在弯管内部行走时,单轮速度呈周期性变化,但三个驱动轮整体周期运动特性一致;驱动轮转角在25°~40°时,牵引系统行走速度与驱动轮转角成正比,且转角为30°时驱动效果最好。该内封堵装置的结构设计可为管道泄漏应急封堵领域装备的研发提供重要参考。     

埋地管道在冻土带的热力变形分析

埋地油气管道通过永冻土地带时,由于管内介质与周围的冻土发生了热力作用,使冰土解冻,在管道周围形成融化圈。文章分析了永冻土的特点,建立了永冻土地带土壤传热数学模型:融化圈内外传热方程、管道向土壤和土壤表面向大气所放热量的守恒关系、融化圈界面处土壤温度、相变界面处固相变为液相的传热条件。还建立了埋地管道模型,并以穿过永冻土地带土壤的输油管道为例,分析并计算了各种应力的最大值所在的位置和大小,提出了在永冻土地带减小管道热力变形的措施。     

细节决定采输卤的成败

通过认真学习《细节决定成败》，结合井矿盐开采的实际，分别以生产过程中的三个细节为主，讨论了在采输卤生产技术控制中的细节的重要性。     

不锈钢管道坡口加工的新方法

对于不锈钢管及合金钢管坡口的加工,采用以往的加工方法切割出来的坡口一般参差不平、比较粗糙,难于打磨平整。使用LGK空气等离子切割机和磁力管道切割机的组合加上一定的辅助手段,改善了不锈钢管及合金钢管的坡口加工效果,使加工出来的坡口像碳钢管一样平整,更加容易打磨组焊。文章介绍了这一新方法所用的设备、材料、切割原理以及操作过程。     

忠武输气管道悬索跨越设计创新及探讨

忠武输气管道有4次悬索跨越,分别是蚂蝗溪悬索跨越、木龙河悬索跨越、马水河悬索跨越、野三河悬索跨越。文章介绍了这几座悬索跨越管桥设计中采用的多项新技术,如采用热聚乙烯半平行钢丝索代替以往的钢丝绳;采用钢管塔架代替以往的角钢塔架;根据地形地貌特点变换风索型式或取消塔架等。这些新技术、新设计在工程中均取得了良好的效果。另外,文章还针对悬索跨越桥面结构型式、预拱度设置、不对称荷载的影响、风索基础的设置等问题进行了探讨。     

基于USB总线的虚拟频谱分析仪设计

文章详细介绍了基于USB总线的虚拟频谱分析仪的设计过程。该仪器使用嵌入式混合处理器MC56F8323来实现动态信号的实时采集、频谱分析和数字滤波等功能。处理结果由USB2．0接口芯片CY7C68013上传至PC机．以完成数据显示、存储、绘制图形等功能。其中，CY7C68013工作于Slave FIFO模式，以实现与MC56F8323间的数据传输。该系统可同时对两路动态信号进行实时谱分析，每路采样频率高选500kHz。