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A particle swarm solution based on lexicographical goal programming for a multiobjective fuzzy open shop problem

机译：基于字典目标规划的粒子群算法求解多目标模糊开店问题

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摘要

In the sequel, we consider a multiobjective open shop scheduling problem with uncertain durations modelled as fuzzy numbers. Given crisp due dates, the objective is to minimise both the makespan and the maximum tardiness. We formulate the multiobjective problem as a fuzzy goal programming model based on lexicographical minimisation of expected values. The resulting problem is solved using a particle swarm optimisation approach searching in the space of possibly active schedules. To asses the performance of this algorithm, we present results of an extensive experimental study on several problem instances, including: a parametric analysis, the experimental evaluation of different priority structures compared to single-objective approaches in terms of objective values as well target achievement, an experimental analysis of the relationship between lexicographical and Pareto solutions and an empirical study based on a-posteriori semantics showing the advantages of taking into account the uncertainty along the scheduling process.
机译：在续集中，我们考虑了不确定时间的多目标开放式车间调度问题，该问题建模为模糊数。给定明确的到期日，目标是最小化制造期和最大延迟。我们将多目标问题公式化为基于期望值的字典最小化的模糊目标规划模型。使用粒子群优化方法在可能的活动计划表空间中进行搜索可以解决由此产生的问题。为了评估该算法的性能，我们提供了针对多个问题实例进行的广泛实验研究的结果，其中包括：参数分析，与单目标方法相比，在目标值和目标达成方面对不同优先级结构进行的实验评估，词典解法和帕累托解之间关系的实验分析，以及基于后验语义的实证研究，表明了在调度过程中考虑不确定性的优势。

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