A Pareto-Based Discrete Jaya Algorithm for Multiobjective Carbon-Efficient Distributed Blocking Flow Shop Scheduling Problem

Carbon peaking and carbon neutrality, which are significant strategies for national sustainable development, have attracted enormous attention from researchers in the manufacturing domain. A Pareto-based discrete Jaya algorithm (PDJaya) is proposed to solve the carbon-efficient distributed blocking flow shop scheduling problem (CEDBFSP) with the criteria of total tardiness and total carbon emission in this article. The mixed-integer linear programming model is presented for the CEDBFSP. An effective constructive heuristic is produced to generate the initial population. The new individual is generated by the update mechanism of PDJaya. The self-adaptive operator local search strategy is designed to enhance the exploitation capability of PDJaya. A critical-path-based carbon saving strategy is introduced to further reduce carbon emissions. The effectiveness of each strategy in the PDJaya is verified and compared with the state-of-the-art algorithms in the benchmark suite. The numerical results demonstrate that the PDJaya is the efficient optimizer for solving the CEDBFSP.