Scheduling decisions in the diffusion and cleaning area can be crucial for the overall performance of a semiconductor manufacturing facility. This area includes complex production constraints and long processing durations. Consequently, we want to optimize scheduling decisions in this area while taking all relevant real-world constraints into account. An important property of machines in this work area is their batching capability: Multiple operations can be performed at the same time. We present an algorithm that includes p-batching within a job-shop environment. We are given a set of jobs to be scheduled. For each job, a fixed sequence of operations must be performed. This sequence is called the route of the job. Operations can only be performed on qualified machines and their processing durations depend on the selected machine. A capacity limit constrains the number of jobs that can be processed per batch. For each operation we are given a recipe that determines operations that are processable in a common batch. Additionally, we are given for each job a ready date and a due date. Those constraints describe a complex job-shop scheduling problem with p-batching. We aim to minimize regular objectives such as total weighted tardiness. For the described problem, we present a batch-oblivious disjunctive graph representation that allows to take adaptive batching decisions during a traversal of the graph. Underutilized batches are dynamically “filled up” by resequencing and reassigning eligible operations. We make use of this approach in a heuristic algorithm based on a GRASP meta-heuristic.
Mots clés : Complex Job-Shop Scheduling, p-batching, Disjunctive Graphs, GRASP, Semiconductor Manufacturing