Dynamic bi-objective Pareto-schedule generation and control in semiconductor supply chain

This work introduces the concept of Pareto-controllability of objectives in dynamic scheduling of single machine systems in a production supply chain. A convex combination approach based on compromise programming is presented in this paper to achieve the desired level of control in dynamic scheduling considering two contradicting objectives, viz., the mean cycle time and the maximum tardiness. At each decision instance during simulation, a Pareto-job is selected using the convex combination approach and loaded on the machine for processing. This approach uses the combination of the concepts of simulation and scheduling together, called as conjunctive simulated scheduling (CSS), for dynamic scheduling of jobs in production environment. We demonstrate using simulation experiments the concept of Pareto-controllability, and show how a single machine system can be controlled at specified operating conditions (scheduling objectives) dictated by the customer demands, which vary with time based on dynamic market forces.