Jointly Optimizing Parallel Batch Processing Scheduling in A Semiconductor Manufacturing Environment
DOI:
https://doi.org/10.23055/ijietap.2025.32.1.10539Abstract
The rapid growth of the semiconductor industry has led to high water and energy consumption and substantial greenhouse gas emissions. Achieving sustainability in the semiconductor industry has become an exceedingly important issue. This paper investigates a complex batch processing scheduling problem in the final testing phase of semiconductor manufacturing, where chambers and chips are modeled as batch processing machines and jobs. Machines can process multiple jobs simultaneously, with each job defined by its processing time, release time, and size. A mixed-integer linear programming model is presented, along with a constructive-based metaheuristic, the ACS-PBPMs algorithm, to optimize batch formation and scheduling decisions jointly. The algorithm uses an effective candidate list strategy to address constraints and incorporates a local search phase based on solution characteristics. Experimental results on diverse problem instances show that the ACS-PBPMs algorithm outperforms CPLEX and competitive algorithms in computational efficiency and solution quality.
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