Permutation Flow Shop Scheduling Optimization Method Based on Cooperative Games

This paper proposes a cooperative games model for the permutation flow shop scheduling problem (PFSP) with multiple customer tasks. Here, customers create alliances through cooperation in case of delayed and early delivery penalty constraints, tasks alliance are rescheduled to seek maximum cost savings. A new $\beta $ rule allocation method is proposed considering variability delay factor different ordered Additionally, it proven that cost-saving resulting from this core PFSP games. hybrid particle swarm optimization algorithm obtain optimal each coalition. The uses as main body, maintains advantage fast convergence optimization, increases diversity among population, improves performance algorithm. Finally, effectiveness algorithm, model, verified via experiments. total all under sequence 5595.8, compared 6689.8 initial order. can be applied various enterprises better satisfy diverse needs market customers.