Purpose

This paper considers the assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times. This problem is an extension of the (simple) SALBP-2 in which task times are worker-dependent and concurrently uncertain. Two criteria are simultaneously considered for minimization, namely, fuzzy cycle time and fuzzy smoothness index.

Design/methodology/approach

First, we show how fuzzy concepts can be used for managing uncertain task times. Then, we present a multiobjective genetic algorithm (MOGA) to solve the problem. MOGA is devoted to the search for Pareto-optimal solutions. For facilitating effective trade-off decision-making, two different MO approaches are implemented and tested within MOGA: a weighted-sum based approach and a Pareto-based approach.

Findings

Experiments over a set of fuzzified test problems show the effect of these approaches on the performance of MOGA while verifying its efficiency in terms of both solution and time quality.

Originality/value

To the author’s knowledge, no previous published work in the literature has studied the biobjective assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times.

中文翻译：

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在任务处理时间不确定的情况下平衡与异构工人一起运行的装配线

目的

本文考虑了具有模糊任务时间的类型 2 (ALWABP-2) 的装配线工人分配和平衡问题。这个问题是（简单的）SALBP-2 的扩展，其中任务时间依赖于工作人员并且同时不确定。同时考虑两个标准以进行最小化，即模糊循环时间和模糊平滑度指数。

设计/方法/方法

首先，我们展示了如何使用模糊概念来管理不确定的任务时间。然后，我们提出了一个多目标遗传算法（MOGA）来解决这个问题。MOGA 致力于寻找帕累托最优解。为了促进有效的权衡决策，在 MOGA 中实施和测试了两种不同的 MO 方法：基于加权和的方法和基于帕累托的方法。

发现

对一组模糊测试问题的实验表明，这些方法对 MOGA 性能的影响，同时验证了其在解决方案和时间质量方面的效率。

原创性/价值

据作者所知，之前发表的文献中没有研究过任务时间模糊的类型 2 (ALWABP-2) 的双目标装配线工人分配和平衡问题。