Software for dynamic reconfigurable order picking systems

This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stockkeeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families.     

矩形件排样的一种优化算法

排样问题是一种总体资源分配问题，这里根据矩形件排样的工艺要求，提出了矩形件排样的一种近似算法，将一块板材分成三部分，每部分排一种零件，使得减料方便，提高了劳动生产率。     

Ant colony optimization approach to estimate energy demand of Turkey

This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. Quadratic_ACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection.     

黑河右岸溢洪洞左导墙优化试验

黑河水利枢纽右岸溢洪洞为缩短引渠左导墙的长度,减少施工开挖量,降低施工难度,减少工程投资,进行优化试验.通过对7种不同的方案对比分析,选择了合适的体型,满足了工程要求.     

模糊规划在炼油厂生产计划优化中的应用

生产计划的编制是炼油化工企业生产经营管理中的一个关键环节，然而由于市场变化的不确定性，使得企业的长远规划制定受到限制，笔者将模糊数学应用于炼厂生产计划的编制，通过模糊规划对炼油企业生产计划进行优化，使编得的生产计划在一定程度上反映市场的变化并使企业的生产效益最优，为企业管理决策者进行长期规划给予指导，给出了实例来说明该方法的可行性。     

连铸温度场数值模拟及冷却水参数优化

由于用实验方法来研究工艺参数对铸坯温度场的影响较困难,故而提出了一种在冶金冷却条件指导下的启发式GA(遗传算法)数值模拟优化方法,为优化工艺参数提供了理论依据,并在优化基础上采用MIMO(多输入多输出)系统PID(比例积分微分)控制器实现了二冷段配水的优化控制,改善了铸坯的质量.     

Minimum spanning trees made easier via multi-objective optimization

Many real-world problems are multi-objective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multi-objective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a single-objective problem can be solved more efficiently via a generalized multi-objective model of the problem. That this is indeed the case is proved by investigating the computation of minimum spanning trees.This work was supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Center Computational Intelligence (SFB 531) and by the German-Israeli Foundation (GIF) in the project Robustness Aspects of Algorithms.     

构造性神经网络在测井岩性识别中的应用

测井岩性识别是石油勘探中十分重要的基础工作,准确的岩性识别结果可以为勘探和开发提供可靠的依据。人工神经网络方法可对复杂的高维数据进行非线性映射,在模式识别领域得到越来越广泛的应用。针对传统的BP神经网络算法存在收敛速度慢、隐层数以及隐层节点数难以确定等缺点,研究了一种构造性神经网络学习算法---级联算法（Cascade Correlation Algorithm,简称CC算法）及其在石油工程中的应用。采用该算法对甘肃镇原-泾川地区进行岩性识别研究,通过与BP神经网络的识别结果进行比较,体现出构造性神经网络的优越性。