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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648911
• 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财富创造部门。然而，这一非常重要的经济领域正面临着激烈的全球竞争。为了在竞争激烈的环境中取得成功，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一种手段。然而，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。生产计划和调度已成为生存的必需品，其重要性怎么强调都不为过。这是一个关键的决策过程，在一段时间内将有限的资源分配给任务，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。它也是研究界所熟知的非常困难的优化问题之一，一直是一个活跃的研究领域。该地区发表的文章数量不计其数。然而，关于如何将理论研究付诸实践的文章却很少。也许研究界过度强调了数学上的严谨性，而忽视了真正的实现。将调度算法转换到实际系统中的一个主要问题是，大多数算法都是特定于框架的，例如作业车间或流水车间，它们很少与实际系统匹配。在这项研究中，我们将尝试开发一种框架和调度方法，可以应用于更广泛的制造行业。这将使行业采用自动化调度工具，而不需要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为求解假设静态条件的数学模型。然而，制造系统运行在动态/随机环境中，其中频繁发生不可预测的实时事件，使得先前可行的计划变得不可行和过时。这类实时事件包括机器故障、紧急任务到达、订单取消、交货期更改、操作员缺勤等。为了解决这个问题，本研究将开发一个统一的监控和动态调度工具。在致力于开发通用调度方法的同时，许多专门的算法(不同于标准研究问题)也将与当地行业合作，针对特定的调度要求进行开发。因此，这一建议和研究计划为许多研究生和本科生带来了大量的培训机会，使他们能够在复杂工业系统的高级计划、调度和控制方面进行培训。