Energy conservation and carbon reduction are essential for achieving carbon peaking and neutrality, as well as advancing the green transformation of the economy and society. This research examines the energy-efficient distributed blocked flowshop scheduling problem with heterogeneous factories (EEDBFSP-HF), which aims to simultaneously minimize makespan and total energy consumption (TEC). To tackle the issue, a hybrid multi-objective variable-scale iterated greedy algorithm (HMOVIG) is developed. The algorithm incorporates several customized strategies to enhance solution quality and optimization efficiency. An elite selection strategy is employed to reserve high-quality individuals from the initial population. To promote population diversity and prevent premature convergence, a factory-aware crossover operator and a dynamic destruction intensity strategy are designed. Additionally, a problem-specific energy-saving heuristic is applied to lower TEC. A speedup-based deep local search is further integrated to refine solutions. Numerical experiments validate that HMOVIG consistently surpasses state-of-the-art algorithms in terms of scheduling effectiveness and solution quality.

With the emergence of the artificial intelligence era, all kinds of robots are traditionally used in agricultural production. However, studies concerning the robot task assignment problem in the agriculture field, which is closely related to the cost and efficiency of a smart farm, are limited. Therefore, a Multi-Weeding Robot Task Assignment (MWRTA) problem is addressed in this paper to minimize the maximum completion time and residual herbicide. A mathematical model is set up, and a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is presented to solve the problem. In the MOTLBO algorithm, a heuristic-based initialization comprising an improved Nawaz Enscore, and Ham (NEH) heuristic and maximum load-based heuristic is used to generate an initial population with a high level of quality and diversity. An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule. A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm. Finally, a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature. Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.