Production scheduling refers to the task of allocating resources over time to the requests in the production system so as to satisfy the concerned performance criteria. In a production system, the resources typically include machines, transportation vehicles, auxiliary tools, operators, and so on. The requests are mainly the products demanded by customers. Scheduling is critical to a production system since it affects time-to-market (the time when the products can be sent to customers), throughput (quantity of finished products per time unit), inventory cost, ability to respond to market demand, etc. On the other hand, scheduling in a production system is very challenging due to complicated factors like a variety of products, uncertain events (machine failure and customer order changes), resources with diverse characteristics, and so forth.

With practical values and challenging problem complexity, production scheduling keeps attracting attention of researchers from the academia and the industry. The goal of our group is not only to do research works with high scientific contribution but also to realize our solutions in the real-world manufacturing facilities. We publish papers in flagship journals, and we are active in relevant international conferences. Our research topics include system modeling, simulation, performance analysis, machine scheduling, vehicle routing, and so on. We execute research projects from National Science Council and also help leading manufacturing companies like TSMC to build customized systems to increase their competitiveness.

The main purpose of our research is to help production managers and engineers to construct high quality schedules effectively and efficiently. The following table summarizes our interested topics and skilled techniques.

The first step to do scheduling in a production system is to understand the detailed behaviors of the target system. Building a model is helpful for researchers to precisely and deeply go into the system and is also convenient for us to discuss with the engineers and managers in the plant. Petri nets (PN) are a promising tool popularly used for modeling production systems. Common phenomena in a production system like conflicts, concurrency, and synchronization can be easily presented by PN. With the introduction of color and time attributes, the so called colored timed PN (CTPN) is able to modeling in a more systematic and well organized way.

We proposed several general and modularized CTPN models for wafer fabrication facilities (fab) and probe centers. We also propose a queueing colored PN (QCPN) model which integrates CTPN and queueing theory to make the execution of the model more efficiently. The following is a model for a machine in the wafer fab. For more details, see Chen et al. (2001, IEEE t-RA), Chiang et al. (2006, IEEE t-ASE), and Chan et al. (2007, ICAT).

A schedule for a machine is actually a set of allocation of exclusive periods to the jobs. To construct a schedule for the plant, we need to take into account a lot of constraints about machine capacity, process capability, setup, availability and specific restrictions resulted from production strategies. A schedule is typically represented by a Gantt chart. The following figure is an example of a schedule for a plant with four machines and four jobs. In this plant, each job must be processed by each machine exactly once, following a predetermined processing route. Besides, each machine is restricted to process only one job at a time.

With thousands of jobs and hundreds of machines, dynamic arrival of customer orders, and uncertain events like machine failure, it is very difficult to construct the schedule through manipulating the Gantt chart directly. In the real-world plants, the schedule is usually constructed by making decisions in real time. Generally, there are six kinds of decisions to be made during the execution of a production system. Three of them are related to machine scheduling, and the other three deal with vehicle scheduling.

There are many production rules proposed in the literature. Each rule is often useful in a specific condition. Therefore, we need to determine a set of rules according to the state of the production system. This problem can be taken as searching a combination of rules in a huge solution space of all possible combinations. The genetic algorithm is a well-known global search algorithm which imitates the evolutionary process of nature. To apply this algorithm, we need to encode the solutions to be chromosomes and then define how to evaluate the fitness of chromosomes and how to execute the evolutionary process. Designing a genetic algorithm is an interesting work, visitors are welcome to read our publications to see more details.

Yung-Hsiang Chan aki (2008- ) akihisa@gamil.com

Hung-We Wen Wen (1998-2000)
An-Chih Huang chih (1999-2001)
Shun-Yu Lin chih (1999-2001)
Yi-Shiuan Shen shiuan (2001-2003)
Hsiang-Hung Cheng littlestar (2002-2004)
Da-Wei Zhan BigTail (2004-2006)
How-Wei Huang Howei (2005-2007)
Tsung-Che Chiang YaKai (2001-2008)
Jia-Wei Yang gawi (2006-2008)
Hsueh-Chien Cheng (2007-2009)

[1] Tsung-Che Chiang and Li-Chen Fu, “Using a family of critical ratio-based approaches to minimize the number of tardy jobs in the job shop with sequence dependent setup times,” European Journal of Operational Research, accepted, 2008.

[2] Tsung-Che Chiang and Li-Chen Fu, “A rule-centric memetic algorithm to minimize the number of tardy jobs in the job shop,” International Journal of Production Research, accepted, 2006.

[3] Tsung-Che Chiang, Yi-Shiuan Shen, and Li-Chen Fu, “A new paradigm for rule-based scheduling in the wafer probe center,” International Journal of Production Research, vol. 46, no. 15, pp. 4111 - 4133, 2008.

[4] Tsung-Che Chiang and Li-Chen Fu, “Using dispatching rules for job shop scheduling with due date-based objectives,” International Journal of Production Research, vol. 45, Issue 14, pp. 3245 - 3262, 2007.

[5] Tsung-Che Chiang, An-Chih Huang, and Li-Chen Fu, “Modeling, scheduling, and performance evaluation for wafer fabrication: a queueing colored Petri-net and GA-based approach,” IEEE Trans. on Automation Science and Engineering, vol. 3, issue 3, pp. 330 - 337, 2006.

[6] Jyh-Horng Chen, Li-Chen Fu, Ming-Hung Lin, and An-Chih Huang, “Petri-Net and GA-Based Approach to Modeling, Scheduling, and Performance Evaluation for Wafer Fabrication,” IEEE TRANS. ON ROBOTICS AND AUTOMATION, vol. 17, NO. 5, 2001.

[7] Ming-Hung Lin, and Li-Chen Fu, “A virtual factory based approach to on-line simulation and scheduling for an FMS and a case study,” Journal of Intelligent Manufacturing , vol. 12, NO. 3 , 2001.

[8] Ming-Hung Lin, and Li-Chen Fu, “Modelling, control and simulation of an IC wafer fabrication system: a generalized stochastic coloured timed Petri Net approach,” International Journal of Production Research , vol. 38, NO. 14, 2000.

[1]Tsung-Che Chiang, Hsueh-Chien Cheng, and Li-Chen Fu, “An efficient heuristic search for minimizing maximum lateness on parallel batch machines,” International Conference on Intelligent Systems Design and Applications, 2008.

[2]Jia-Wei Yang, Hsueh-Chien Cheng, Tsung-Che Chiang, and Li-Chen Fu, “Multiobjective lot scheduling and dynamic OHT routing in a 300-mm wafer fab,” IEEE International Conference on Systems, Man, and Cybernetics, October 2008.

[3]Hsueh-Chien Cheng, Tsung-Che Chiang, and Li-Chen Fu, “A memetic algorithm for parallel batch machine scheduling with incompatible job families and dynamic job arrivals,” IEEE International Conference on Systems, Man, and Cybernetics, October 2008.

[4]Hsueh-Chien Cheng, Tsung-Che Chiang, and Li-Chen Fu, “Multiobjective permutation flowshop scheduling by an adaptive genetic local search algorithm,” IEEE Congress on Evolutionary Computation, June 2008.

[5]How-Wei Huang, Ching-Hu Lu and Li-Chen Fu, Fellow, IEEE, “Lot Dispatching and Scheduling Integrating OHT Traffic Information in the 300mm Wafer Fab,” IEEE Conference on Automation Science and Engineering, September 2007

[6]Da-Wei Chan, How-Wei Huang and Li-Chen Fu, “Optimized Dispatching and Scheduling for OHTs in the 300mm Wafer Fab,” The Ninth International Conference on Automation Technology, June 2007

[7]Tsung-Che Chiang and Li-Chen Fu, “A simulation study on dispatching rules in semiconductor wafer fabrication facilities with due date-based objectives,” Proc. of IEEE International Conference on Systems, Man, and Cybernetics, pp. 4660 - 4665, October, 2006

[8]Tsung-Che Chiang and Li-Chen Fu, “Multiobjective job shop scheduling using genetic algorithm with cyclic fitness assignment,” Proc. of IEEE World Congress on Computational Intelligence, pp. 11035 - 11042, July, 2006.

[9]Tsung-Che Chiang and Li-Chen Fu, “Multiobjective job shop scheduling using rule-coded genetic local search,” Proc. of International Conference on Computers and Industrial Engineering, pp. 1764 – 1775, May, 2006.

[10]Tsung-Che Chiang and Li-Chen Fu, “Using dispatching rules for job shop scheduling with due date-based objectives,” Proc. of IEEE International Conference on Robotics and Automation, in press, May, 2006.

[11]Tsung-Che Chiang and Li-Chen Fu, “A rule-centric local search approach for due-dates job shop scheduling,” Proc. of Asian Pacific Industrial Engineering and Management Conference, December, 2005.

[12]Tsung-Che Chiang and Li-Chen Fu, “An iterative refining mechanism for general job shop scheduling problems,” Proc. of IEEE International Conference on Automation Science and Engineering, pp. 203 - 208, August 2005.

[13]Tsung-Che Chiang and Li-Chen Fu, “Exploiting paradigms for rule-based scheduling in flexible manufacturing systems,” Proc. of Asian Conference on Industrial Automation and Robotics, 2005.

[14]Hsing-Hung Cheng, Tsung-Che Chiang and Li-Chen Fu, “Petri net modeling and GA-based scheduling for the assembly industry,” Proc. of International Conference on Automation Technology, pp. 581 – 586, 2005.

[15]Tsung-Che Chiang and Li-Chen Fu, “A virtual preemption paradigm for using priority rules to solve job shop scheduling problems,” Proc. of IEEE International Conference on Robotics, Automation, pp. 3714 – 3719, 2005.

[16]Tsung-Che Chiang and Li-Chen Fu, “Parameter tuning of production scheduling rules by an ant system-embedded genetic algorithm,” Proc. of IEEE International Conference on Robotics, Automation and Mechatronics, pp. 1089 – 1094, 2004.

[17]Tsung-Che Chiang, Li-Chen Fu, “Solving the FMS scheduling problem by critical ratio-based heuristics and the genetic algorithm,” Proc.IEEE Conf. Robotics and Automation, vol. 3, 3131 - 3136, 2004

[18]Tsung-Che Chiang, Yi-Shiuan Shen, Li-Chen Fu, “Adaptive lot/equipment matching strategy and ga based approach for optimized dispatching and scheduling in a wafer probe center,” Proc.IEEE Conf. Robotics and Automation, vol. 3, 3125 - 3130, 2004

[19]Shun-Yu Lin, Li-Chen Fu, Tsung-Che Chiang, Yi-Shiaun Shen , “Colored timed Petri-net and GA based approach to modeling and scheduling for wafer probe center,” Proc.IEEE Conf. Robotics and Automation, vol. 1, 1434 - 1439, 2003

[20]An-Chih Huang, Li-Chen Fu, Ming-Hung Lin, Shun-Yu Lin, “Modeling, scheduling, and prediction for wafer fabrication: queueing colored Petri-net and GA based approach,” Proc.IEEE Conf. Robotics and Automation, vol. 3, 3187 - 3192, 2002

[21]Ming-Hung Lin, Li-Chen Fu, “An effective search strategy for wafer fabrication scheduling with uncertain process requirements,” Proc.IEEE Conf. Robotics and Automation, vol. 1, 547 - 552, 2001

[22]Hung-We Wen, Li-Chen Fu, Shih-Shinh Huang, “Modeling, scheduling, and prediction in wafer fabrication systems using queueing Petri net and genetic algorithm,” Proc.IEEE Conf. Robotics and Automation, vol. 4, 3559 - 3564, 2001

[23]Jyh-Horng Chen, Li-Chen Fu, Ming-Hung Lin, “Petri-net and GA based approach to modeling, scheduling, and performance evaluation for wafer fabrication,” Proc.IEEE Conf. Robotics and Automation, vol. 4, 3403 - 3408, 2000

[24]Ming-Hung Lin, Li-Chen Fu, “A new generation of evaluation tool for online design and scheduling in an advanced manufacturing system,” Proc.IEEE Conf. Robotics and Automation, vol. 1, 459 - 464

[25]Ming-Hung Lin, Li-Chen Fu, “Modeling, analysis, simulation and control of semiconductor manufacturing systems: a generalized stochastic colored timed Petri net approach,” Proc.IEEE Conf. Systems, Man, and Cybernetics, vol. 3, 12 - 15, 1999

[26]Ming-Hung Lin, Li-Chen Fu, “New approach combining numerical technique and simulation for analysis of large discrete event systems based on Petri nets,” Proc. IEEE Conf. Systems, Man, and Cybernetics, Oct. 1999

[27]Ming-Hung Lin, Li-Chen Fu, “Modeling of priority queueing service in discrete event systems using hybrid Petri nets,” Proc. IEEE Conf. Systems, Man, and Cybernetics, Oct. 1999

[28]Ming-Hung Lin, Li-Chen Fu and Teng-Jei Shih, “Virtual factory-a novel testbed for an advanced flexible manufacturing system,” Proc. IEEE Conf. Robotics and Automation, Vol. 3, 10-15 ,May 1999

[29]Ming-Hung Lin, Li-Chen Fu, “Systematic creation and application of virtual factory with object oriented concept,” Proc. IEEE Conf. Robotics and Automation,Vol 1, 16-20 ,May 1998

[30]Yung-Yu Chen, Li-Chen Fu, Yu-Chien Chen, “Multi-agent based dynamic scheduling for a flexible assembly system,” Proc.IEEE Conf. Robotics and Automation, vol. 3, 2122 - 2127, 1998

[31]Yung-Yi Chung, Li-Chen Fu, Ming-Wei Lin, “Petri net based modeling and GA based scheduling for a flexible manufacturing system,” Proc.IEEE Conf. Decision and Control, vol. 4, 4346 - 4347, 1998

[32]Yung-Feng Chiu, Li-Chen Fu, “A GA embedded dynamic search algorithm over a Petri net model for an FMS scheduling,” Proc.IEEE Conf. Robotics and Automation, vol. 1, 513 - 518, 1997

[33]Chu-Hui Lin, Li-Chen Fu, “Petri net based dynamic scheduling of an elevator system,” Proc.IEEE Conf. Robotics and Automation, vol. 1, 192 - 199, 1996

[34]Ham-Huah Hsu; Li-Chen Fu, “Fully automated robotic assembly cell: scheduling and simulation,” Proc.IEEE Conf. Robotics and Automation, vol. 1, 208 - 214, 1995

[34]Ham-Huah Hsu; Li-Chen Fu, “Fully automated robotic assembly cell: scheduling and simulation,” Proc.IEEE Conf. Robotics and Automation, vol. 1, 208 - 214, 1995

[36]Li-Chen Fu, Pei-Sen Liu, “Hierarchical Dynamic Scheduling For A Flexible Manufacturing System,” Proc.IEEE Conf. Computer Integrated Manufacturing, 393 - 402, 1992

[37]Pei-Sen Liu, Li-Chen Fu, “Planning and scheduling in a flexible manufacturing system using a dynamic routing method for automated guided vehicles,” Proc. IEEE Conf. Robotics and Automation, vol. 3, 1584 - 1589, 1989