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首页> 外文期刊>International Journal of Production Research >Ant colony optimisation with elitist ant for sequencing problem in a mixed model assembly line
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Ant colony optimisation with elitist ant for sequencing problem in a mixed model assembly line

机译：混合模型装配线中用于排序问题的精英蚂蚁蚁群优化

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Optimised sequencing in the Mixed Model Assembly Line (MMAL) is a major factor to effectively balance the rate at which raw materials are used for production. In this paper we present an Ant Colony Optimisation with Elitist Ant (ACOEA) algorithm on the basis of the basic Ant Colony Optimisation (ACO) algorithm. An ACOEA algorithm with the taboo search and elitist strategy is proposed to form an optimal sequence of multi-product models which can minimise deviation between the ideal material usage rate and the practical material usage rate. In this paper we compare applications of the ACOEA, ACO, and two other commonly applied algorithms (Genetic Algorithm and Goal Chasing Algorithm) to benchmark, stochastic problems and practical problems, and demonstrate that the use of the ACOEA algorithm minimised the deviation between the ideal material consumption rate and the practical material consumption rate under various critical parameters about multi-product models. We also demonstrate that the convergence rate for the ACOEA algorithm is significantly more than that for all the others considered.
机译：混合模型装配线（MMAL）中优化的排序是有效平衡原材料用于生产的速率的主要因素。在本文中，我们在基本蚁群优化（ACO）算法的基础上，提出了一种采用精英蚁群的蚁群优化（ACOEA）算法。提出了一种具有禁忌搜索和精英策略的ACOEA算法，以形成最优的多产品模型序列，该模型可以最大程度地减少理想材料使用率与实际材料使用率之间的偏差。在本文中，我们比较了ACOEA，ACO和其他两种常用算法（遗传算法和目标追踪算法）在基准测试，随机问题和实际问题上的应用，并证明了使用ACOEA算法可以最大程度地减少理想情况下的偏差。多产品模型的各种关键参数下的材料消耗率和实际材料消耗率。我们还证明了ACOEA算法的收敛速度明显高于所有其他考虑的收敛速度。

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