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Metaheuristics for single and multiple objectives production scheduling for the capital goods industry

机译:用于资本品行业的单目标和多目标生产调度的元启发式

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摘要

In the capital goods industry, companies produce plant and machinery that is used to produce consumer products or commodities such as electricity or gas. Typical products produced in these companies include steam turbines, large boilers and oil rigs. Scheduling of these products is difficult due to the complexity of the product structure, which involves many levels of assembly and long complex routings of many operations which are operated in multiple machines. There are also many scheduling constraints such as machine capacity as well as operation and assembly precedence relationships. Products manufactured in the capital goods industry are usually highly customised in order to meet specific customer requirements. Delivery performance is a particularly important aspect of customer service and it is common for contracts to include severe penalties for late deliveries. Holding costs are incurred if items are completed before the due date. Effective planning and inventory control are important to ensure that products are delivered on time and that inventory costs are minimised. Capital goods companies also give priority to resource utilisation to ensure production efficiency. In practice there are tradeoffs between achieving on time delivery, minimising inventory costs whilst simultaneously maximising resource utilisation. Most production scheduling research has focused on job-shops or flow-shops which ignored assembly relationships. There is a limited literature that has focused on assembly production. However, production scheduling in capital goods industry is a combination of component manufacturing (using jobbing, batch and flow processes), assembly and construction. Some components have complex operations and routings. The product structures for major products are usually complex and deep. A practical scheduling tool not only needs to solve some extremely large scheduling problems, but also needs to solve these problems within a realistic time. Multiple objectives are usually encountered in production scheduling in the capital goods industry. Most literature has focused on minimisation of total flow time, or makespan and earliness and tardiness of jobs. In the capital goods industry, inventory costs, delivery performance and machine utilisation are crucial competitive. This research develops a scheduling tool that can successfully optimise these criteria simultaneously within a realistic time. ii The aim of this research was firstly to develop the Enhanced Single-Objective Genetic Algorithm Scheduling Tool (ESOGAST) to make it suitable for solving very large production scheduling problems in capital goods industry within a realistic time. This tool aimed to minimise the combination of earliness and lateness penalties caused by early or late completion of items. The tool was compared with previous approaches in literature and was proved superior in terms of the solution quality and the computational time. Secondly, this research developed a Multi-Objective Genetic Algorithm Scheduling Tool (MOGAST) that was based upon the development of ESOGAST but was able to solve scheduling problems with multiple objectives. The objectives of this tool were to optimise delivery performance, minimise inventory costs, and maximise resource utilisation simultaneously. Thirdly, this research developed an Artificial Immune System Scheduling Tool (AISST) that achieved the same objective of the ESOGAST. The performances of both tools were compared and analysed. Results showed that AISST performs better than ESOGAST on relatively small scheduling problems, but the computation time required by the AISST was several times longer. However ESOGAST performed better than the AISST for larger problems. Optimum configurations were identified in a series of experiments that conducted for each tool. The most efficient configuration was also successfully applied for each tool to solve the full size problem and all three tools achieved satisfactory results.
机译:在资本货物行业中,公司生产用于生产消费品或电力或天然气等商品的工厂和机械。这些公司生产的典型产品包括蒸汽轮机,大型锅炉和石油钻机。由于产品结构的复杂性,这些产品的调度很困难,这涉及许多级别的组装以及在多台机器中进行的许多操作的漫长而复杂的工艺路线。还存在许多调度约束,例如机器容量以及操作和装配优先级关系。在资本货物行业中制造的产品通常高度定制,以满足特定的客户要求。交付绩效是客户服务的一个特别重要的方面,对于合同而言,通常包括对延迟交付的严厉处罚。如果项目在到期日之前完成,则会产生持有成本。有效的计划和库存控制对于确保按时交付产品并最大程度降低库存成本非常重要。资本货物公司还优先考虑资源利用,以确保生产效率。实际上,要在按时交付,最小化库存成本和最大程度地利用资源之间进行权衡。大多数生产计划研究都集中在忽略装配关系的作业车间或流程车间。有限的文献集中在装配生产上。但是,资本货物行业的生产计划是组件制造(使用工作,批生产和流水处理),组装和构造的组合。一些组件具有复杂的操作和路由。主要产品的产品结构通常很复杂而且很深。实用的调度工具不仅需要解决一些非常大的调度问题,而且还需要在现实时间内解决这些问题。在资本货物行业的生产计划中通常会遇到多个目标。大多数文献都集中在最小化总流动时间上,或尽量缩短工期和早熟程度。在固定资产行业中,库存成本,交付性能和机器利用率至关重要。这项研究开发了一种调度工具,可以在实际时间内同时成功地优化这些条件。 ii本研究的目的是首先开发增强型单目标遗传算法调度工具(ESOGAST),使其适合在现实时间内解决资本货物行业中非常大的生产调度问题。该工具旨在最大程度地减少因项目的早期或延迟完成而导致的早期和延迟处罚的组合。将该工具与文献中的先前方法进行了比较,并在解决方案质量和计算时间方面被证明是优越的。其次,本研究开发了一种基于ESOGAST的多目标遗传算法调度工具(MOGAST),但该工具能够解决具有多个目标的调度问题。该工具的目标是优化交付性能,最小化库存成本并同时最大程度地利用资源。第三,这项研究开发了一种人工免疫系统计划工具(AISST),该工具达到了ESOGAST的相同目的。对两种工具的性能进行了比较和分析。结果表明,在相对较小的调度问题上,AISST的性能比ESOGAST更好,但是AISST所需的计算时间却长了几倍。但是,对于较大的问题,ESOGAST的性能优于AISST。在针对每种工具进行的一系列实验中确定了最佳配置。最有效的配置也成功地应用于每种工具,以解决全尺寸问题,所有三个工具均取得了令人满意的结果。

著录项

  • 作者

    Xie Wenbin;

  • 作者单位
  • 年度 2011
  • 总页数
  • 原文格式 PDF
  • 正文语种 English
  • 中图分类

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