In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker’s learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

Short-reach nanophotonic interconnects are promising to solve the communication bottleneck in data centers and chip-level scenarios. However, the nanophotonic interconnects are sensitive to process and thermal variations, especially for the microring structures, resulting in significant variation of an optical link's bit error rate (BER). In this paper, we propose a power-efficient adaptive tuning approach for nanophotonic interconnects to address the variation issues. During the adaptive tuning process, each nanophotonic interconnect is adaptively allocated just enough power to meet the BER requirement. The proposed adaptive tuning approach could reduce the photonic receiver power by 8% - 34% than the worst-case based fixed design while achieving the same BER. Our evaluation results show that the adaptive tuning approach scales well with the process variation, the thermal variation and the number of communication nodes, and can accommodate different types of NoC architectures and lasers.

Considering that process planning and production scheduling are independent of each other in a hybrid flow-shop, this study categorizes the process route into parallel process-set, batch set and unordered process-set, and builds a multi-objective optimization model to minimize the maximum completion time and the minimum processing cost. An improved artificial bee colony algorithm has been developed to solve the model. A segmented decoding method based on the insertion principle and the release time of the predecessor process is proposed to effectively use the idle time of the machine. A dynamic triggering neighborhood mechanism is introduced to enhance the local searchability of the algorithm. Finally, the feasibility and effectiveness of this algorithm to solve such problems are verified via simulation experiments.