Process scheduling is a classical problem in the field of production planning and control; in particular, effective job shop scheduling remains an essential component in today's highly dynamic and agile production environment. This paper presents unified framework for solving generic job shop scheduling problems based on the formulation of a job shop into three main classes of problem, namely, static, semi-dynamic and dynamic scheduling problems. Algorithms based on artificial immune systems, an engineering analogy of the human immune system, are developed to solve the respective classes of job shop scheduling problems. A high level decision support model is presented for the effective deployment of the scheduling strategies whereby a unified approach to solving real job shop problems is achieved.
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