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Performance evaluation of ant colony optimization-based solution strategies on the mixed-model assembly line balancing problem

机译：基于蚁群优化的混合模型装配线平衡问题求解策略性能评估

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摘要

The aim of this article is to compare the performances of iterative ant colony optimization (ACO)-based solution strategies on a mixed-model assembly line balancing problem of type II (MMALBP-II) by addressing some particular features of real-world assembly line balancing problems such as parallel workstations and zoning constraints. To solve the problem, where the objective is to minimize the cycle time (i.e. maximize the production rate) for a predefined number of workstations in an existing assembly line, two ACO-based approaches which differ in the mission assigned to artificial ants are used. Furthermore, eachACO-based approach is conducted with two different pheromone release strategies: global and local pheromone updating rules. The four ACO-based approaches are used for solving 20 representative MMALBP-II to compare their performance in terms of computational time and solution quality. Detailed comparison results are presented.
机译：本文的目的是通过解决现实世界中流水线的某些特殊功能，比较基于迭代蚁群优化（ACO）的解决方案在II型混合模型流水线平衡问题（MMALBP-II）上的性能。平衡并行工作站和分区约束等问题。为了解决该问题，目标是在现有装配线中为预定数量的工作站最小化周期时间（即最大生产率），使用了两种基于ACO的方法，它们在分配给人造蚂蚁的任务方面有所不同。此外，每种基于ACO的方法都是通过两种不同的信息素释放策略进行的：全局和局部信息素更新规则。四种基于ACO的方法用于求解20个代表性的MMALBP-II，以比较它们在计算时间和解决方案质量方面的性能。给出了详细的比较结果。

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