A HYBRID EVOLUTIONARY ALGORITHM FOR INTEGRATED PRODUCTION PLANNING AND SCHEDULING PROBLEMS
A HYBRID EVOLUTIONARY ALGORITHM FOR INTEGRATED PRODUCTION PLANNING AND SCHEDULING PROBLEMS
复制标题
DOI：
--
复制
发表时间：
2012-06
影响因子：
7.9
通讯作者：
Lin Lin-Lin;X. Hao;M. Gen;K. Ohno
中科院分区：
文献类型：
--
作者：
Lin Lin-Lin;X. Hao;M. Gen;K. Ohno
集成生产计划和调度（IPPS）是指一种制造管理过程，通过该过程，原材料和生产能力得到最佳分配，以满足需求。然而，大多数的研究都针对不同的制造环境，考虑不同的假设条件，提出了不同的IPPS模型，并提出了专门的优化算法。由于数学模型的结构不同，所提算法的有效性也不同，大多数IPPS模型难以应用于实际制造系统。在本文中，我们提出了一个网络建模的方式，制定IPPS问题到一个统一的模型。此外，大多数调度模型属于NP完全问题的类，即使在简化比较实际问题的介绍。IPPS将原来的确定性模型转化为参数化模型，使问题更加复杂。为了求解这个统一的IPPS模型，我们提出了一种混合进化算法（hEA）结合遗传算法（GA）和粒子群优化（PSO）。最后，通过对几个测试问题与不同进化方法的比较，验证了所提算法的有效性。
Integrated production planning and scheduling (IPPS) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. However, most researches presented the different IPPS models with considering the different assumptions under the different manufacturing environment, and proposed the special optimization algorithms. Because the structure difference of the mathematical models, the effectiveness of the proposed algorithm is also different, most IPPS models are difficult to be applied to the practical manufacturing systems. In this paper, we propose a network modeling way to formulate the IPPS problem into a unified model. In addition, most scheduling models belong to the class of NP-complete problems even when simplifications in comparison to practical problems are introduced. The IPPS transform the original deterministic model to parametric formulations, which makes the problem more complicated. For solving this unified IPPS model, we propose a hybrid evolutionary algorithm (hEA) with combining genetic algorithm (GA) and particle swarm optimization (PSO). Finally, the experiments verify the effectiveness of proposed algorithm, by comparing with different evolutionary approaches for the several test problems.