Genetic algorithms for planning and scheduling engineer-to-order production: a systematic review

This paper provides a systematic review of the Genetic Algorithm (GA)s proposed to solve planning and scheduling problems in Engineer-To-Order (ETO) contexts. Our review focuses on how the key characteristics of ETO projects affect both the problem studied and the GA algorithmic features. Typical ETO projects consist of one-of-a-kind products with complex structures and uncertain designs. A deep analysis of the papers published between 2000 and 2022 enables identifying 10 main characteristics of ETO projects, six activity types, 10 decision types, eight groups of constraints, and 10 optimisation objectives. Our study shows that none of the reported papers integrates all 10 ETO characteristics. The less studied ETO characteristics are incorporating design and engineering information in the problem definition and the design uncertainty. Our review also identifies 10 recurrent encoding formats and emphasises the most frequently used genetic operators. We observed that most planning and scheduling problems consider objectives and decisions related to product customisation or supply chain configuration yielding multi-objective problems. Most multi-objective GAs use a weighted sum or are based on NSGAII. Diversity maintenance methods, adaptive and parameter tunning mechanisms, or hybridisation with machine learning models are still not used in this context.

本文对为解决按订单设计（ETO）环境中的规划和调度问题而提出的遗传算法（GA）进行了系统综述。我们的综述重点关注ETO项目的关键特征如何影响所研究的问题以及遗传算法的特征。典型的ETO项目由具有复杂结构和不确定设计的独一无二的产品组成。对2000年至2022年间发表的论文进行深入分析，可以确定ETO项目的10个主要特征、6种活动类型、10种决策类型、8组约束条件和10个优化目标。我们的研究表明，所报道的论文中没有一篇综合了所有10个ETO特征。研究较少的ETO特征是在问题定义中纳入设计和工程信息以及设计的不确定性。我们的综述还确定了10种常见的编码格式，并强调了最常用的遗传算子。我们观察到，大多数规划和调度问题考虑与产品定制或供应链配置相关的目标和决策，从而产生多目标问题。大多数多目标遗传算法使用加权和或基于NSGAII。在这种情况下，仍然没有使用多样性维护方法、自适应和参数调整机制或与机器学习模型的混合。