Abstract
The call center industry urgently needs efficient staff scheduling due to unpredictable customer service demands and challenges in human resource coordination. Appropriate scheduling is vital to enhance operational efficiency, reduce labor costs and improve customer satisfaction. This research is aim to solve the daily multi-task staff scheduling problem by integrating the innovative capabilities of QUBO (Quadratic Unconstrained Binary Optimization) model and Quantum Computing. We formulate the intricate parameters related to the number of employees, task type, and skill proficiency using a traditional mixed-integer programming model. Specifically, the study formulates scheduling constraints and objectives and then we convert this model into a QUBO model this transformation makes the problem to be suitable for Quantum Annealing (QA) and enabling subsequent integration with QA technology. This technology enables rapid and advanced exploration of extensive solution spaces and identify the optimal or near-optimal solutions. This research offers valuable insights and substantial groundwork for future explorations and developments in optimizing call center staff scheduling problem.
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Li, C., Liu, Z., Song, Y., Liu, H., Liu, H., Liu, X. (2024). Quantum Computing Approaches to Optimize Employee Scheduling in Multi-task Call Centers. In: Chien, CF., Dou, R., Luo, L. (eds) Proceedings of Industrial Engineering and Management. SMILE 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-97-0194-0_1
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DOI: https://doi.org/10.1007/978-981-97-0194-0_1
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