Research of order allocation model based on cloud and hybrid genetic algorithm under ecommerce environment

For massive order allocation problem of the third party logistics (TPL) in ecommerce, this paper proposes a general order allocation model based on cloud architecture and hybrid genetic algorithm (GA), implementing cloud deployable MapReduce (MR) code to parallelize allocation process, using heuristic rule to fix illegal chromosome during encoding process and adopting mixed integer programming (MIP) as fitness function to guarantee rationality of chromosome fitness. The simulation experiment shows that in mass processing of orders, the model performance in a multi-server cluster environment is remarkable superior to that in stand-alone environment. This model can be directly applied to cloud based logistics information platform (LIP) in near future, implementing fast auto-allocation for massive concurrent orders, with great application value.     

高速氧燃料热喷涂

高速氧燃料(HVOF)粉末喷涂工艺代表了当今热喷涂金属镀层技术的最高水平。HVOF 也被称为超高速氧燃料粉末喷涂工艺、氧燃料爆燃(OFD)工艺或 D 枪工艺。     

考虑气动阻力和横风稳定的汽车车身多目标优化设计

综合考虑了气动阻力特性和横风稳定性,对车身外形参数进行了多目标自动优化设计。综合利用参数化建模技术、计算流体力学(CFD)仿真、试验设计方法、响应面模型和智能优化算法,集成Pro/Engineer参数化建模和ICEM网格划分工具以及Fluent仿真软件,在多学科优化平台modeFRONTIER上,搭建了一种自动优化设计流程。利用该流程,基于遗传算法(GA)对MIRA快背式模型车身几何外形进行了改型设计,得到了考虑车身气动阻力特性和横风稳定性的最优权衡设计解集。该结果使得气动阻力因数降低了5.2%,侧向力因数降低了5.8%。因而,实现了车身气动阻力和横风稳定性的多目标优化。     

基于GA优化模糊控制规则的工程船舶控制方法

采用模糊控制器来改进控制性能，遗传算法建立模糊控制器的控制规则，使控制器在不需要先验知识的情况下就能产生较好的控制效果；最后，建立工程船的数学模型并对系统进行了仿真实验。     

摩托车电镀零件的保护

一辆摩托车通常由大大小小上千个零件组成,电镀零件是其中的一个重要组成部分。摩托车电镀件按镀基材料可以分为金属电镀件和塑料电镀件两种,如车把、挡泥板、车圈、排气管等,属于金属镀基件:而踏板车外壳、仪表、商标、车型标识则属于塑料镀基件。如果摩托车电镀层保持完好则车辆会显得光彩照人。相反,如果镀件有锈蚀、擦痕、凹陷则整车则显得丑陋不堪,并进一步影响车手乘骑形象。像有些豪华型大排量进口摩托车,如果商标、排气管锈蚀,车子会很麻烦(因为这些零件非常难     

轴瓦电镀层孔洞原因分析及对策

常规电镀前处理工艺无法清除轴瓦毛坯中的油污，在中温电镀液中油污受热外溢，吸附于表面造成镀层孔洞。通过加强前处理、净化镀液、降低工作温度得以消除。     

浅析影响汽车连接器端子导电性能的因素

汽车连接器端子是决定连接器电力和信号传输的关键零件,影响端子导电性能的因素有很多,比如端子的材料、结构、表面状况以及端子的压接规范等。通过对以上各方面进行分析,找出影响端子导电性能的主要原因,避免在以后的设计和生产过程中出现类似的问题。     

Improved Real-Coded Genetic Algorithm Solution for Unit Commitment Problem Considering Energy Saving and Emission Reduction Demands

Unit commitment(UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm(IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.