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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753177
• 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, itultimately 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着激烈的全球竞争。为了在这种竞争环境中取得成功，制造商需要显着提高能力。以设备和自动化形式出现的技术一直是保持竞争力的手段。然而，一个同样重要且具有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。生产计划和调度已成为生存的必需品，其重要性怎么强调也不为过。这是一个随着时间的推移将有限的资源分配给任务的关键决策过程，同时满足活动和资源之间的大量时间和关系约束。事实上，它最终决定了制造系统的运行性能。这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。该领域发表了无数文章。然而，关于如何将理论研究付诸实践的文章却很少。也许研究界过分强调了数学的严谨性，而忽视了实际的实现。将调度算法转化为实际系统的一个主要问题是，大多数算法都是特定于框架的，例如作业车间或流水作业，它们很少与实际系统匹配。在这项研究中，我们将尝试开发一个可以应用于更广泛的制造业的框架和调度方法。这将使各行业能够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限制是它们大多数都是为了解决假设静态条件的数学模型而设计的。然而，制造系统在动态/随机环境中运行，其中频繁发生不可预测的实时事件，使得以前可行的计划变得不可行和过时。此类实时事件的示例包括机器故障、紧急作业到达、订单取消、截止日期更改、操作员缺勤等。为了解决这个问题，在本研究中，将开发统一的监督控制和动态调度工具。在开发作为本提案主要内容的通用调度方法的同时，还将与当地行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，该提案和研究项目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。