一种求解Job-Shop调度问题的新型蚁群算法

Job-Shop调度问题是一类具有很高理论研究和工程应用价值的问题。针对使用蚁群算法求解Job-Shop调度问题时较难设置合适参数的问题，提出一种动态设置参数的新型蚁群求解算法。分析了蚁群算法中参数对求解结果的影响，给出了算法求解Job-Shop调度问题的关键技术和实现过程。最后对五个基本测试问题进行了仿真实验，并与遗传算法、模拟退火算法、基本蚁群算法进行了比较。结果表明，该算法能得到较优的结果，具有一定的应用价值。

Novel ant colony optimization algorithm for Job-Shop scheduling problem

Aiming at the problem of all possible states and the inventory holding cost not completely considered in general dynamic facility location model, this paper developed a new model. Firstly, obtained the formula of inventory cost in per period with storage and traffic capacity constraints through two steps approximately method. Then, gave the formulas of opening, operation, closing and reopening cost in planning horizon, and developed a new dynamic facility location model. Finally, solved the model by genetic algorithm, clone selection algorithm, particle swarm optimization respectively, and compared the capacities of finding optimal solution, stability, counting speed and astringency between these algorithms. The results of numerical example show that the model is effective and the genetic algorithm is the most suitable for the problem.