基于自适应参数混合蚁群算法的双资源约束作业车间调度

Solving Dual Resource Constrained Job-Shop Scheduling Problem(DRCJSP) Based on Hybrid Ant Colony Algorithm with Self-Adaptive Parameters

文章针对以生产成本最小为目标,考虑差异性工人的双资源约束作业车间调度问题,提出参数按算法迭代结果自适应调整,基于蚂蚁流量自适应控制路径选择的混合蚁群算法,在算法前期扩大解搜索空间,后期加快算法收敛,实现算法性能的分阶段性能优化。通过对仿真实验结果的分析,该混合蚁群算法能有效求解双资源约束车间调度问题,且能够在保证得到较优调度结果的同时,具备优秀的收敛性能。

Keeping in mind the minimization of production cost and that workers differ greatly in degrees of train- ing, we propose a hybrid ant colony algorithm for solving the DRCJSP. Sections 1and 2 of the full paper explain our proposed algorithm. The core of section 1 is that we describe the DRCJSP and establish its mathematical model. The core of section 2 is that we design the self-adaptive ant colony algorithm which adjusts parameters with the iteration outcome and self-adaptively controls the route selection with the ant flow; the hybrid ant colony algorithm expands the search space at the beginning stage of iteration and accelerates its convergence at the later stage of itera- tion, thus optimizing its performance through different stages. Section 3 simulates our ant colony algorithm; the simulation results, given in Tables 2 and 3, and their analysis show preliminarily that our hybrid ant colony algorithm can effectively solve the DRCJSP and optimize the convergence performance.