Study on multi-objective optimization for parallel batch machine scheduling using variable neighbourhood search

Abstract

Managing multiple objectives is a crucial issue coming up with scheduling solutions in wafer fabrication. This paper presents computational results for solving Parallel Batch Machine Problems (PBMSP) with Variable Neighborhood Search (VNS), enriched with experiences from industry. Based on experiments, we present correlation factors between most common Key Performance Indicators (KPI) considered as objectives, evaluating the strength and direction of their inter-relationships. We discuss experiments for pareto objective functions and weighted objective functions, composed of important KPIs. We place great importance on the specific role of critical constraints in a scheduling system empowered by optimization, e.g. time bounds and minimum batch sizes. The pure existence of critical constraints necessarily requires multi-objective function optimization. By experiments, this paper examines hierarchical objective functions managing maximum time bounds and minimum batch sizes, discussing solution strategies and pitfalls.