【作者】 丁秀明；

【导师】 钱雪忠；

【作者基本信息】 江南大学 ， 计算机软件与理论， 2008， 硕士

【摘要】 随着市场经济的发展,信息化、智能化技术的提高,供应链管理技术也得到了飞速的发展。物流过程是供应链管理过程的子过程,物流过程中的物件分配调度是供应链调度的一部分,同时也是物流过程中最重要的组成部分、核心问题,优化物流过程中的物件分配调度对于提高企业的经济效益和社会效益具有重要意义。供应链物流过程中的物件分配调度问题是一个组合优化问题,目前解决该问题的方法比较简单,并有其各自的局限性,而新型的仿生算法——蚁群算法,具有正反馈性、鲁棒性、并行计算、协同性等特点,非常适合于解决该调度问题,并顺应算法向智能化、仿生化发展的趋势。本文研究了蚁群算法(Ant Colony optimization ,ACO)在TSP问题中的应用,深入研究分析了改进的蚁群算法。通过对物流过程的分析,建立物流过程的模型,深入分析问题的特点,将蚁群算法应用到物流调度领域中,建立适合于蚁群算法的调度模型,提出了基于蚁群优化的物流调度算法,实现供应链物流过程中物件的动态分配,并进行仿真研究以评价所提出方法的有效性。本文针对蚁群算法早熟停滞、收敛速度慢等不足,提出了针对物流调度问题的改进策略。仿真结果表明,使用改进的蚁群优化策略测试不同的订单组合,能得到一个优化解决方案,该方案能使尽可能多的定单按时交付,同时也能将订单的延迟减小,提高了算法的运算效率。最后,本文还根据物流调度问题开发了一个相应的模拟仿真系统,表明基于蚁群优化的物流调度算法具有一定的实用价值。更多还原

【Abstract】 With development of market economy and improvement of the informationization, intelligent technology, the management technique of supplied chain got the development at high speed. In supply chain management, logistics can be defined as the subprocess of the supply chain process. The scheduling of the component assignment is the key issue in logistics processes. The optimization of the scheduling logistics significantly improves economic and social benefits of enterprise. The scheduling of logistic process is a combination optimization problem. Recently, the approaches that solved this problem can be applied in simplicity, but these methods have some limitation. As a novel simulated evolutionary algorithm, Ant Colony Optimization (ACO) has many merits as positive feedback, robust, parallel compute, coordination, so it is very suitable to solve scheduling problem and accords with the tendency that the algorithm evolves into intelligent and simulated evolutionary.The paper has studied the application of the Ant Colony Optimization at the Traveling Salesman Problems and has further investigated the improved the Ant Colony Optimization. After the analysis of the logistics process, it has built the model of the logistics scheduling. By the analyzed the character of the problem, the Ant Colony Algorithm has applied in scheduling of the logistics processes. It has established the scheduling model of Ant Colony Algorithm. It has presented the scheduling algorithm of logistic scheduling based on ant colonies that has optimized the dynamic assignment of items to orders. Empirical study shows the efficiency of proposed algorithm.To overcome the default of stagnation and convergence speed slow, an improved ACO strategy is present. The simulation result shows that an optimization solution can be obtained from applying the improved optimization strategy of ant colonies to test different order combination. This solution improve the efficiency of algorithm by delivering more orders on time and reducing the delay of ordersFinally, according to the logistic scheduling, the system of simulation has been established. It shows that the logistic scheduling algorithm will be of somewhat significance.更多还原