Intelligent Scheduling System for Dynamic Environment

• Main Authors: CHIANG, TE-WEI, 蔣德威
• Other Authors: SY-YEN KUO
• Format: Others
• Language: zh-TW
• Published: 1997
• Online Access: http://ndltd.ncl.edu.tw/handle/91898786350981778644

博士 === 國立臺灣大學 === 電機工程學系 === 85 === This dissertation investigates the job-shop scheduling and railway scheduling problems. The elements of a job shop scheduling problem consists of a set of machines and a collection of jobs to be scheduled. Operation precedence constraints give the order in which the operations that comprise each job must be processed. The job shop scheduling problem thus can be defined as the allocation of machines over time to perform a collection of jobs to minimize/maximize a performance measure while satisfying the operation precedence constraints, machine capacity constraints, processing time requirements, and ready time requirements. On the other hand, in railway systems, the timetable containing the arrival/ departure times and the track assignments of all trains at each station is the most essential schedule for day-to-day operations. Similar to conventional job-shop scheduling problem, the railway scheduling problem is the decision of the arrival/departure times and the assigning of tracks to all trains while minimizing a particular objective function and satisfying some specified constraints. The main differences between job-shop scheduling problems and railway scheduling problems are the constraints that must be satisfied. In this dissertation, we propose an approach for general scheduling problems based on iterative repair. This approach starts with a heuristically generated schedule which may be infeasible, then applies local search techniques to generate a good conflict- free schedule. Since cycles may exist among a sequence of repair operations during the repair process, we also propose a cycle detection and resolution scheme in this dissertation. This approach is not only suitable for static schedule generation but also for dynamic rescheduling. Furthermore, this approach is very flexible, it can be adapted to different scheduling applications. Experimental results show that the proposed system can not only generate a good feasible static schedule but also react to the dynamic and stochastic environment in an efficient manner.