Integrated Process Planning and Active Scheduling in a Supply Chain-A Learnable Architecture Approach

Abstract

Through the lens of supply chain management, integrating process planning decisions and scheduling plans becomes an issue of great challenge and importance. Dealing with the problem paves the way to devising operation schedules with minimum makespan; considering the flexible process sequences, it can be viewed as a fundamental tool for achieving the scheme, too. To deal with this integration, the modeling approach to problem with MIP structure is common in the literature. These models take precedence constraints into consideration to select machines and to determine sequences. In order to obtain viable sequences, we employed a proposed transformation matrix (TM). We also took advantage of an evolutionary search, called Learnable genetic Architecture (LEGA). Based on LEGA, we developed an integrated process planning and scheduling learnable genetic algorithm (IPPSLEGA). Our approach was evaluated with problems with various sizes. The experimental results show that our proposed architecture outperforms prior approaches, or it performs, at least, as efficiently as they do.