Study on the Production Scheduling and Logistic By Advanced Evolutionary Algorithms

在这项研究中，我们对制造和物流系统中EA的使用现状进行了全面的调查。高级计划和调度(APS)是指通过优化配置原材料和生产能力以满足需求的制造管理过程。APS特别适合于简单的规划方法不能充分解决相互竞争的优先事项之间的复杂权衡的环境。在本研究中，我们分析了集成制造系统的结构，提取了先进计划与调度领域的数学模型。针对APS算法的灵活性，提出了一种基于多阶段操作的遗传算法，作为组合调度问题中柔性资源分配信息的有效表示方法。针对不同问题的特点，结合有效的局部搜索技术获得主动调度，即关键路径局部搜索、左移局部搜索，随着经济全球化的发展和全球电子营销的延伸，以全球供应链和全球物流为支撑的全球企业服务成为商业世界的必然选择。因此，在当今激烈竞争的商业环境中，如何有效地管理物流系统已成为几乎所有企业降低各种成本的关键问题，尤其是对许多跨国公司来说。在本研究中，我们关注的是使用GM的物流系统。为了解决物流问题，我们提出了不同的遗传算法，基于Prufer数的编码方法，基于优先级的编码方法，基于扩展优先级的遗传算法，以及基于随机路径的遗传算法。通过与近期相关研究的比较，考察了遗传算法在不同规模的物流问题中的有效性和效率。

In this research, we provided a comprehensive survey of the current state-of-the-art in the use of EA in manufacturing and logistics systems.Advanced planning and scheduling (APS) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. APS is especially well-suited to environments where simpler planning methods cannot adequately address complex trade-offs between competing priorities. In this research, we analyze the structure of integrated manufacturing system, and extract the mathematical model in advanced planning and scheduling field. According to the flexibility in APS, a multistage operation-based GA is developed as an effective approach for representing the information of flexible resources assignment in combinatorial scheduling problem. In terms of characters of different problem, effective local search techniques are combined to obtain active schedule, i.e. critical path local search, left shift local search.With the development of economic globalization and extension of worldwide electronic marketing, global enterprise services supported by universal supply chain and world-wide logistics become imperative for business world. How to manage logistics system efficiently thus has become a key issue for almost, all of the enterprises to reduce their various costs in today's keenly competitive environment of business, especially for many multinational companies. In this research, we concern the logistics systems using GM. lb solve the logistics problems, we proposed different GA approaches, Prufer number-based encoding method, priority-based encoding method, extended priority-based GA, and random path-based GA. The effectiveness and efficiency of GA approaches were investigated with various scales of logistics problems by comparing with recent related researches.