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中最重要的创造财富部门。但是，这个非常重要的经济部门面临着激烈的全球竞争。为了在这种竞争性的气候下取得成功，制造商需要大大提高功能。以设备和自动化的形式，技术一直是竞争力的一种手段。但是，同样重要的竞争性途径是我们组织设施，计划生产和计划运营的方式。生产计划和计划已成为生存的必要性，其重要性不能被夸大。随着时间的流逝，这是将有限资源分配给任务的关键决策过程，而活动和资源之间的大量时间和关系约束也得到满足。确实，它可以确定制造系统的运营性能。它也是研究界已知的非常困难的优化问题之一，并且是一个积极的研究领域。该地区发表了无数文章。但是，关于如何将理论研究实践的文章很少。也许研究界过分强调了数学上的严格性，而付出了不足的真实实施。将调度算法转换为真实系统的一个主要问题是，大多数算法是特定于框架的框架，例如工作购物商店或流程，很少与真实系统匹配。在这项研究中，我们将尝试开发一种可以应用于更广泛的制造业的框架和调度方法。这将使行业能够采用自动调度工具，而无需开发昂贵的专业算法。在文献中调度算法的另一个主要局限性是，其中大多数旨在解决假设静态条件的数学模型。但是，制造系统在动态/随机环境中运行，在动态/随机环境中，经常发生不可预测的实时事件，以前可行的时间表不可行和过时。此类实时事件的示例包括机器故障，紧急工作的到来，取消订单，到期日变化，缺席等级等以解决此问题，在这项研究中，将开发统一的监督控制和动态时间表工具。在开发该提案的主要目的的广义调度方法方面，许多专业算法（与标准研究问题不同）也将与当地行业合作开发出许多专业算法（与标准研究问题不同）。因此，该提案和研究计划为许多研究生和本科生提供了足够的培训机会，以进行高级计划，调度和控制复杂的工业系统。