A Comprehensive Framework and Solution Procedure for Dynamic Scheduling in Manufacturing 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。技术，以设备和自动化的形式，一直是竞争力的手段。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生存的必需品，其重要性怎么强调都不为过。随着时间的推移，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，这是一个关键的决策制定过程。事实上,它

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.