A Multi-Objective Variable-Scale Evolutionary Strategy Method for Carbon-Efficient Scheduling of Distributed Heterogeneous Flowshop

The carbon efficiency of manufacturing industry is receiving more and more attention. An effective way to improve the carbon efficiency of manufacturing industry is to design a scheduling strategy aimed at reducing the energy cost of the production process. This paper investigates the carbon-efficient distributed heterogeneous shop scheduling problem for factories with different processing capacities, with productivity-related (makespan) and sustainability-related (total carbon emission, TCE) indicators as objectives. A multi-objective variable-scale evolutionary strategy (MOVES) is proposed to optimize two objectives simultaneously. First, a dynamic ruin intensity method is designed. Second, a carbon emission saving method based on problem knowledge is designed to optimize carbon emissions. Finally, a local search method is designed to further optimize makespan and TCE. Numerical experiments demonstrate the effectiveness of the proposed algorithm.