This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots (Bi-HFSP_CS). The objectives are to minimize the makespan and total energy consumption. First, the Bi-HFSP_CS is formalized, followed by the establishment of a mathematical model. Second, enhanced version of the artificial bee colony (ABC) algorithms is proposed for tackling the Bi-HFSP_CS. Then, fourteen local search operators are employed to search for better solutions. Two different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process. Finally, the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm, its three variants, and three effective algorithms in resolving 95 instances of 35 different problems. The experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning (QABC1) demonstrates as the top performer for solving concerned problems. This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength, offering beneficial perspectives for exploration and research in relevant domains.

Since the increasing demand for surgeries in hospitals, the surgery scheduling problems have attracted extensive attention. This study focuses on solving a surgery scheduling problem with setup time. First, a mathematical model is created to minimize the maximum completion time (makespan) of all surgeries and patient waiting time, simultaneously. The time by the fatigue effect is included in the surgery time, which is caused by doctors’ long working time. Second, four mate-heuristics are optimized to address the relevant problems. Three novel strategies are designed to improve the quality of the initial solutions. To improve the convergence of the algorithms, seven local search operators are proposed based on the characteristics of the surgery scheduling problems. Third, Q-learning is used to dynamically choose the optimal local search operator for the current state in each iteration. Finally, by comparing the experimental results of 30 instances, the Q-learning based local search strategy’s effectiveness is verified. Among all the compared algorithms, the improved artificial bee colony (ABC) with Q-learning based local search has the best competitiveness.

The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures. This study proposes a novel framework for healthcare applications that integrates 5G communication, quantum computing, and sensitive data measurement to address the challenges of measuring and securely transmitting sensitive medical data. The framework includes a quantum-inspired method for quantifying data sensitivity based on quantum superposition and entanglement principles and a delegated quantum computing protocol for secure data transmission in 5G-enabled healthcare systems, ensuring user anonymity and data confidentiality. The framework is applied to innovative healthcare scenarios, such as secure 5G voice communication, data transmission, and short message services. Experimental results demonstrate the framework’s high accuracy in sensitive data measurement and enhanced security for data transmission in 5G healthcare systems, surpassing existing approaches.