基于多资源协同优化的港口集装箱堆存位置决策及装载作业调度

项目名称： 基于多资源协同优化的港口集装箱堆存位置决策及装载作业调度

项目编号： No.70802040

项目类型： 青年科学基金项目

立项/批准年度： 2009

项目学科： 金属学与金属工艺

项目作者： 陈璐

作者单位： 上海交通大学

项目金额： 17万元

中文摘要： 本项目基于港口内多种资源协同优化模式，研究对集装箱堆存位置决策及装载作业调度进行全局优化的流程和方法；集中研究出口箱在堆场内堆存作业特点，建立出口箱堆存位置分配两阶段决策模型；建立多设备并行考虑的出口箱装载作业调度模型，应用制造业生产调度理论对其进行建模及求解；通过对不同资源之间约束条件的定义，将这两个决策过程的相互制约性转化为各自优化模型中的决策参数，并应用于优化算法开发，改善各自计划的可行性；在此基础上利用集成化的优化算法进行统一求解，提高决策过程之间的协调性；最终形成一套完整的针对集装箱堆存位置决策及装载作业调度进行全局优化的综合理论和方法。 项目研究成果将充实和发展港口集装箱物流系统研究理论，推动港口集装箱物流系统运作管理向并行化、协同化的方向发展，同时对提高港口资源利用率，提升港口科学决策水平具有重要的现实意义。

中文关键词： 堆存决策；作业调度；建模理论；全局优化

英文摘要： Based on the synchronized optimization of various resources in a maritime terminal, this project studies two important sub-systems in a maritime terminal, namely the storage allocation problem and the container handling scheduling problem. A two-step storage location assignment method for outbound containers is developed. Using the production scheduling theory, an integrated model is developed to address the scheduling of various kinds of container handling equipment in the terminal. It is an extension of the hybrid flow shop scheduling problem. The interaction of the two sub-systems is defined as resources constraints in their respective decision model, in order to improve the feasibility of the solution. Moreover, the two sub-systems are integrated using local search in order to improve the coordination of the two decision making processes. Finally, a comprehensive methodology of global optimization is developed to address both the yard storage allocation and container handling. The results of the project will enrich the theoretical research on the logistics management in a maritime terminal; help the logistics management to move towards the direction of synchronization and cooperation. In addition, the research results of this project have an important contribution in increasing the productivity of the terminal, as well as improving the scientific level of the decision making processes in the terminal.

英文关键词： storage allocation; container handling scheduling; modelling; global optimization