A Comprehensive Framework and Solution Procedure for Dynamic Scheduling in Manufacturing Systems

制造系统动态调度的综合框架和解决方案流程

• 批准号: RGPIN-2017-03871
• 负责人: Defersha, Fantahun
• 金额: $ 1.53万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710587
• 关键词: Comprehensive; Framework; Solution; Procedure; Dynamic

Manufacturing is the most important wealth-generating sector of the Canadian economy. However, this very important economic sector is facing intense global competitions. To be successful in this competitive climate, manufacturers require significantly improved capabilities. Technology, in the form of equipment and automation, has always been a means to competitiveness. However, an equally important and a determining avenue to competitiveness is the way we organize facilities, plan production and schedule operations. Production planning and scheduling has become a necessity for survival and its importance cannot be overstated. It is a critical decision making process of allocating limited resources to tasks over time, while a large number of time and relationship constraints among the activities and resources are being satisfied. Indeed, it ultimately determines the operational performance of a manufacturing system. It is also one of the very difficult optimization problems known to the research community and has been an active field of research. Countless number of articles are published in the area. However, very little has been written on how to bring theoretical research into practice. Perhaps the research community has over-emphasized the mathematical rigor at the cost of under-emphasizing real implementations. One major problem in translating scheduling algorithms to real systems is that most algorithms are framework specific such as job-shop or flow-shop which rarely match real systems. In this research, we will attempt to develop a framework and a scheduling methodology that can be applied to a wider range of manufacturing industries. This will enable industries adopt automated scheduling tools without the need to develop costly specialized algorithms. Another major limitation of scheduling algorithms in literature is that most of them are designed to solve mathematical models that assume static conditions. However, manufacturing systems operate in dynamic/stochastic environments where frequent unpredictable real-time events occur making a previously feasible schedule infeasible and obsolete. Examples of such real-time events include machine failures, arrival of urgent jobs, cancellation of orders, due date changes, operator absentee etc. To address this problem, in this research, a unified supervisory control and dynamic schedule tool will be developed. While working in developing generalized scheduling methodologies which are the main thrust of this proposal, many specialized algorithms (different from standard research problems) will also be developed for specific scheduling requirements in collaboration with local industries. Consequently, this proposal and the research program brings ample training opportunities for many graduate and undergraduate students in advanced planning, scheduling and controlling of complex industrial systems.

制造业是加拿大经济中最重要的创造财富的部门。然而，这个非常重要的经济部门正面临激烈的全球竞争。为了在这种竞争环境中取得成功，制造商需要显著提高能力。以设备和自动化为形式的技术一直是提高竞争力的手段。然而，一个同样重要和决定性的竞争力途径是我们组织设施、计划生产和安排运营的方式。生产计划和调度已经成为企业生存的必需品，其重要性怎么强调都不为过。它是一个关键的决策过程，分配有限的资源，随着时间的推移，任务，而大量的时间和活动和资源之间的关系约束得到满足。确实 最终决定了制造系统的运行性能。 它也是研究界已知的非常困难的优化问题之一，并且一直是一个活跃的研究领域。在该领域发表了无数文章。然而，很少有人写如何把理论研究付诸实践。也许研究界过分强调了数学的严密性，而忽视了真实的实现。在将调度算法翻译到真实的系统中的一个主要问题是，大多数算法是特定于框架的，例如作业车间或流水车间，它们很少匹配真实的系统。在这项研究中，我们将尝试开发一个框架和调度方法，可以应用到更广泛的制造业。这将使工业采用自动化调度工具，而无需开发昂贵的专门算法。文献中调度算法的另一个主要局限性是，它们中的大多数被设计用于求解假设静态条件的数学模型。然而，制造系统在动态/随机环境中运行，其中频繁发生的不可预测的实时事件使得先前可行的调度不可行和过时。这种实时事件的例子包括机器故障，紧急工作的到来，取消订单，截止日期的变化，操作员缺席等，为了解决这个问题，在这项研究中，将开发一个统一的监督控制和动态调度工具。在开发通用调度方法（这是本建议的主要重点）的同时，还将与当地工业合作开发许多专门的算法（不同于标准研究问题），以满足特定的调度要求。因此，该建议和研究计划为许多研究生和本科生在复杂工业系统的高级规划，调度和控制方面带来了充足的培训机会。