An Improved Sparrow Search Algorithm for Solving the Energy-Saving Flexible Job Shop Scheduling Problem

Due to emerging requirements and pressures related environmental protection, manufacturing enterprises have expressed growing concern for adopting various energy-saving strategies. However, criteria were usually not considered in traditional production scheduling problems. To overcome this deficiency, has drawn more attention from academic scholars industrial practitioners. In paper, an flexible job shop problem (EFJSP) is introduced accordance with the criterion of optimizing power consumption processing costs simultaneously. Since classical FJSP strongly NP-hard, Improved Sparrow Search Algorithm (ISSA) developed efficiently solving EFJSP. ISSA, a Hybrid (HS) method used produce initial high-quality population; Quantum Rotation Gate (QRG) Sine–Cosine (SCA) are integrated intensify ability ISSA coordinate exploration exploitation; adaptive adjustment strategy Variable Neighborhood (VNS) applied strengthen diversification move away local optima. Extensive computational experiments validate that outperforms other existing algorithms EFJSP due advantages intensification mechanisms ISSA.