Improved Multi‐Objective Evolution Algorithm for Energy‐Aware Distributed Heterogeneous Hybrid Flowshop Scheduling Problem - Li, Yingli; Liu, Haibing; Zhang, Biao (IET collaborative intelligent manufacturing, 2025)
This study investigates the distributed heterogeneous hybrid flow‐shop scheduling problem (DHHFSP) with the tardiness and energy consumption criteria. A decomposition‐based multi‐objective artificial bee colony (MOABC/D) algorithm is developed to solve the scheduling problem. In the MOABC/D algorithm, a tri‐level encoding scheme combined with domain‐specific heuristic rules are designed to enable comprehensive solution space exploration. A local search framework incorporating five novel critical path‐based neighbourhood structures to intensify subproblem investigation. An adaptive optimisation strategy integrating similarity‐based prioritisation, dynamic neighbourhood relationships, and coordinated information sharing among adjacent subproblems. A solution exchange strategy is proposed to assist the algorithm jump out of the local optimum, and continue searching for solutions in various directions. Comprehensive simulation trials validate the algorithm's ability to balance scheduling efficiency and energy conservation in the DHHFSP. It shows great promise for multi‐objective optimisation in complex distributed manufacturing systems with heterogeneous resources.