Graduation Year


Document Type




Degree Granting Department

Industrial Engineering

Major Professor

Michael X. Weng, Ph.D.

Committee Member

Tapas K. Das, Ph.D.

Committee Member

Grisselle Centeno, Ph.D.

Committee Member

Sudeep Sarkar, Ph.D.

Committee Member

Lihua Li, Ph.D.


Due date, Multi-agent method, Make-to-order, Production planning and control, Manufacturing systems


In the make-to-order (MTO) industry, offering competitive due dates and on-time delivery for customer orders is important to the survival of MTO companies. Workload control is a production planning and control approach designed to meet the need of the MTO companies. In this dissertation, a multi-agent workload control methodology that simultaneously deals with due date setting, job release and scheduling is proposed to discourage job early or tardy completions. The earliness and tardiness objectives are consistent with the just-in-time production philosophy which has attracted significant attention in both industry and academic community. This methodology consists of the order entry agent, job release agent, job routing and sequencing agent, and information feedback agent.

Two new due date setting rules are developed to establish job due dates based on two existing rules. A feedback mechanism to dynamically adjust due date setting is introduced. Both new rules are nonparametric and easy to be implemented in practice. A job release mechanism is applied to reduce job flowtimes (up to 20.3%) and work-in-process inventory (up to 33.1%), without worsening earliness and tardiness, and lead time performances. Flexible job shop scheduling problems are an important extension of the classical job shop scheduling problems and present additional complexity. A multi-agent scheduling method with job earliness and tardiness objectives in a flexible job shop environment is proposed. A new job routing and sequencing mechanism is developed. In this mechanism, different criteria for two kinds of jobs are proposed to route these jobs. Two sequencing algorithms based on existing methods are developed to deal with these two kinds of jobs.

The proposed methodology is implemented in a flexible job shop environment. The computational results indicate that the proposed methodology is extremely fast. In particular, it takes less than 1.5 minutes of simulation time on a 1.6GHz PC to find a complete schedule with over 2000 jobs on 10 machines. Such computational efficiency makes the proposed method applicable in real time. Therefore, the proposed workload control methodology is very effective for the production planning and control in MTO companies.