Affiliation:
1. Georgia Tech, USA
2. University of Michigan, USA
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
Reference44 articles.
1. Enhancing combinatorial optimization with classical and quantum generative models
2. Beaulieu, M., Roy, J., & Landry, S. (2018). Logistics outsourcing in the healthcare sector: Lessons from a Canadian experience. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l'Administration, 35(4), 635-648.
3. Bhattacharjee, D., Bustamante, F., Curley, A. & Perez, F. (2021). Navigating the labor mismatch in us logistics and supply chains. McKinsey & Company.
4. Chen, Y.H., Chen, C.A. & Chien, C.F. (2023). Logistics and supply chain management reorganisation via talent portfolio management to enhance human capital and resilience. International Journal of Logistics Research and Applications, 1-24.
5. Exploration and exploitation in evolutionary algorithms