Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.

Nowadays, green manufacturing has become one of the hot topics in both academia and industry because of global warming and the greenhouse effect. Among manufacturing systems, job-shop scheduling plays a key role due to its wide applications. In this article, we study a green job-shop scheduling problem with variable machining speeds (JSPVMS) aiming at minimizing the makespan as well as the total energy consumption (TEC). First, a new mixed-integer linear programming model is formulated for this green JSPVMS. Then, a new knowledge-based multiobjective memetic algorithm (MOMA) is developed to address this problem. In our MOMA, a novel decoding scheme based on the problem property is well designed to obtain better tradeoff solutions between makespan and TEC. Furthermore, a novel local search is proposed to search for promising nondominated solutions by discovering problem-specific knowledge. Additionally, the proposed MOMA utilizes the advantage of the genetic operator and local search to balance the exploration and exploitation. The effectiveness of each improvement component (decoding scheme and local search) in our MOMA is verified by comparing experiments among different MOMA variants. Finally, we compare our MOMA with several well-known multiobjective optimization algorithms (i.e., NSGA-II, SPEA2, and MOEA/D) on JSPVMS instances. Experimental results indicate that our MOMA significantly outperforms the other algorithms on most of the instances.

(2N+1) selective harmonic elimination pulse-width modulation (SHE-PWM) is an effective switching strategy of the modular multilevel converter in medium-voltage cases. In these cases, the number of sub-modules (SMs) is not high. Compared to the traditional (N+1) SHE-PWM, (2N+1) SHE-PWM has N more voltage levels, thus it can yield much better harmonic performance. However, the task of selective harmonic elimination also becomes much more complicated since there are more switching angles to be determined. This paper proposes a differential harmony search algorithm (DHS) with a novel harmony improvisation procedure for solving this problem. The Bayesian optimization method is applied to find the optimal parameter configuration for DHS. The performance of DHS is compared with 6 other metaheuristic algorithms including differential evolution (DE), harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), teaching and learning-based optimization (TLBO), and ant colony optimization (ACO). The comparison is conducted on a set of 100 (2N +1) SHE-PWM instances by varying the modulation index from 0.01 to 1.0 with a step of 0.01. The numerical results show that the proposed DHS outperforms other compared methods in terms of objective function values, algorithm robustness, the magnitude of fundamental harmonic, and the calculated total harmonic distortion values. The switching angles obtained by DHS are further validated by both Matlab/Simulink simulation and hardware experiment.