Abstract
This chapter introduces a lightweight quantum-inspired genetic algorithm (LQIGA) to tackle the challenges of workforce scheduling in supply chain and logistics operations, with a specific focus on outsourced workforce scheduling. LQIGA employs a novel lightweight qubit encoding approach, derived from quantum-inspired evolutionary algorithms (QIEA), to effectively represent complex problem constraints while maintaining flexibility. Experimental results on benchmark instances from CSPLib demonstrate the efficacy of LQIGA in consistently achieving optimal or near-optimal solutions within reasonable timeframes. Despite its lightweight nature potentially limiting control flexibility, particularly for larger-scale problems, the promising performance of LQIGA warrants further exploration. Additionally, future research directions, including quantum-inspired parallel annealing with analog memristor crossbar arrays, are discussed, highlighting the transformative potential of quantum-inspired computation in reshaping workforce scheduling and optimization in supply chain and logistics operations
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