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With the rise of globalization and environmental concerns, distributed scheduling and energy-efficient scheduling have become crucial topics in the informational manufacturing system. Additionally, the growing consideration about realistic constraints, such as transportation time and finite transportation resources, has made the scheduling problem increasingly complex. Facing these challenges, special mechanisms are required to improve the efficiency of solving algorithms. In this paper, a bi-learning evolutionary algorithm (BLEA) is proposed to solve the distributed energy-efficient flexible job shop problem with transportation constraints (DEFJSP-T). Firstly, we integrate statistical learning (SL) and evolutionary learning (EL) in the framework, while decomposition and Pareto dominance methods are employed in different stages to handle conflicting objectives. During the SL stage, probability models are …