Wang JinFeng, Fan XiaoLiang, Wan Shuting
School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China.
ScientificWorldJournal. 2014;2014:271895. doi: 10.1155/2014/271895. Epub 2014 Jun 3.
The complex process planning problem is modeled as a combinatorial optimization problem with constraints in this paper. An ant colony optimization (ACO) approach has been developed to deal with process planning problem by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the optimal process plan. A weighted directed graph is conducted to describe the operations, precedence constraints between operations, and the possible visited path between operation nodes. A representation of process plan is described based on the weighted directed graph. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPC). Two cases have been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been conducted to demonstrate the feasibility and efficiency of the proposed approach.
本文将复杂的工艺规划问题建模为一个带有约束的组合优化问题。已开发出一种蚁群优化（ACO）方法来处理工艺规划问题，该方法通过同时考虑诸如操作排序、选择制造资源以及确定设置计划等活动，以实现最优工艺规划。构建了一个加权有向图来描述操作、操作之间的优先约束以及操作节点之间可能的访问路径。基于加权有向图描述了工艺规划的一种表示形式。蚁群遍历图上的必要节点，以实现总成本（TPC）最小化的目标从而获得最优解。进行了两种情况的研究以考察ACO各种参数对系统性能的影响。开展了大量对比实验以证明所提方法的可行性和有效性。
