The permutation flow shop batch scheduling problem: an improved population-based iterative greedy algorithm with self-adaption and self-viewing

To address production scheduling difficulties, efforts in academia and industry focus on achieving a balance of economic, environmental, and societal growth through green manufacturing scheduling. In this paper, the multi-objective permutation flow shop batch scheduling problem (MOPFSBSP) is optimized by taking makespan and machine emission noise into account. As a result of evaluating other NEH-based algorithms, the enhanced NEH_PR SQ algorithm yields a favorable initial solution after evaluating other NEH-based algorithms. The improved population-based iterative greedy algorithm (IPBIG) is then given a self-adaptation and self-viewing strategies to make it better at exploring. Then, a local search algorithm is suggested to apply mutation and replacement to sub-batches and sub-lots of each product to achieve the best solution. The algorithms presented in this research are tested on the Car, Rec, and Hel standard database instances and compared with traditional and innovative algorithms. The experimental data shows that the IPBIG algorithm outperforms other algorithms in optimizing over 74.19% of instances, particularly medium- and large-scale instances. Undoubtedly, the IPBIG algorithm offers a superior solution to the MOPFSBSP problem. It also significantly diminishes production noise, enhances operational efficiency for enterprises, and provides a novel trajectory for the sustained advancement of manufacturing firms.