A novel multi-objective dynamic flexible job shop scheduling algorithm using reinforced learning based black widow spider algorithm

In today's fast-paced manufacturing environments, solving flexible job shop scheduling problem (FJSP) has become essential due to swift design-to-manufacturing-to-consumer cycle and frequent disruptive events like new job arrivals. This study proposes a novel reinforcement learning based black widow spider algorithm (BWSA-RL) to address the multi-objective dynamic flexible job shop scheduling problem (MODFJSP). The algorithm utilizes a hybrid reinforcement learning framework for dynamic adjustment of procreation and mutation rates of BWSA-RL. The switch between SARSA and Q-learning is achieved through a novel conversion operator based on sparsity of Q-tables. To enhance Pareto front diversity, a novel hybrid crowding distance metric (HCD) is introduced. Additionally, a rescheduling-heuristic is proposed to accommodate new job arrivals. A comprehensive experimental regime was applied to validate the proposed novelties against 30 benchmark instances. Mathematical model was validated with mixed integer linear programming (MILP). The conversion condition operator and the HCD metric were benchmarked against two other approaches, demonstrating their effectiveness in balancing exploration and exploitation while maintaining solution diversity. BWSA-RL was benchmarked against four state-of-the-art algorithms, outperforming them in 83.3% of the instances. BWSA-RL demonstrated its potential as a robust approach for MODFJSP, balancing energy efficiency and operational goals like makespan, due-date conformance and schedule stability.