Bilevel interactive optimisation for rebatching scheduling problem with selectivity banks in high variety flow line production

Abstract

Mass customization enables the integration of traditional flow line production with product platforms to accommodate abundant product-process varieties. These platform-based flow lines explore common process routes while highlighting rebatching scheduling with selectivity banks (RBS) to handle large process varieties across production stages at minimum setup cost. Given the inherent coupling between decision making in job diverging and retrieval quality, an interactive optimization approach is necessary for the RBS problem. This study proposes a bilevel interactive optimization (BIO) model for RBS to accommodate high variety flow line production. The model addresses the conflicting goals of lane occupancy cost, process setup cost, and job divergence and retrieval efficiency. Regarding job divergence at the leader-level, a vehicle routeing problem with precedence constraints is formulated and solved by a constructed genetic algorithm (GA). Concerning job retrieval at the follower-level and the ongoing characteristic of selectivity banks, a dispatching problem with various batch size preference and dynamic time window is established and dealt with a restricted dynamic programming (RDP) algorithm after balancing search efficiency and accuracy. Thus, to solve the BIO, a hybrid GA-RDP is developed and implemented. A practical application to an automotive painting shop illustrates the operational benefits of the BIO model for the RBS problem.