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Production scheduling plays a pivotal role in smart factories due to the development of intelligent manufacturing. As a typical scheduling problem, the blocking flow-shop scheduling problem (BFSP) has attracted enormous attention from researchers. In this paper, an ensemble discrete water wave optimization algorithm (EDWWO) is proposed with the criterion to minimize the makespan. In the proposed algorithm, a constructive heuristic is presented to suit the needs of initial solutions quality. The constructive heuristic is based on a new dispatching rule combined with the well-known NEH heuristic. The algorithmic characteristics are explored and effective technologies, such as data-driven mechanism in the propagation phase, a block-shifting operator based on the framework of the variable neighborhood search in the breaking phase, and perturbation strategy, are employed to improve the performance of the algorithm. The effectiveness of operators and parameters in EDWWO are analyzed and calibrated based on the design of experiments. To evaluate the algorithmic performance, the well-known benchmark problem is adopted for comparison with five other state-of-the-art algorithms. Meanwhile, the statistical validity of the results is investigated by introducing the Friedman-test and Wilcoxon-test. The statistical results demonstrate the effectiveness of EDWWO for solving the BFSP. [ABSTRACT FROM AUTHOR]

Water Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed. First, the random opposition-based learning (ROBL) mechanism is introduced to generate the initial population with high quality. Second, a new modified operation is designed and embedded into propagation operation to balance the global exploration and the local exploitation. Third, the covariance matrix self-adaptation evolution strategy (CMA-ES) is employed by the refraction operation to further strengthen the local exploitation. Furthermore, the diversity of the population is maintained in the evolution process by using a crossover operator. The experiment results based on CEC 2017 benchmarks indicate that the EWWO outperforms the state-of-the-art variant algorithms of the WWO and the standard WWO. [ABSTRACT FROM AUTHOR]

In this article, a water wave optimization algorithm with a single wave mechanism, called single water wave optimization (SWWO), is proposed to solve the no-wait flow-shop scheduling problem (NWFSP) with the objective of minimizing the makespan. In the proposed SWWO, an improved Nawaz–Enscore–Ham (NEH) heuristic is applied to construct a high-quality initial candidate. In the propagation operation, a self-adaptive block-shift operation is employed. In the breaking operation, a variable neighbourhood search operation is utilized to explore the local optimal solution. According to the schema theory as presented in genetic algorithms, a crossover operation is adopted as the refraction operation. Finally, the computational results based on several benchmarks and statistical performance comparisons are presented. The experimental results demonstrate the effectiveness and efficiency of the proposed SWWO for solving the NWFSP. [ABSTRACT FROM AUTHOR]

The distributed assembly blocking flow-shop scheduling problem (DABFSP), which is a promising area in modern supply chains and manufacturing systems, has attracted great attention from researchers and practitioners. However, minimizing the total tardiness in DABFSP has not captured much attention so far. For solving the DABFSP with the total tardiness criterion, a mixed integer linear programming method is utilized to model the problem, wherein the total tardiness during the production process and assembly process are optimized simultaneously. A constructive heuristic (KBNEH) and a water wave optimization algorithm with problem-specific knowledge (KWWO) are presented. KBNEH is designed by combining a new dispatching rule with an insertion-based improvement procedure to obtain solutions with high quality. In KWWO, effective technologies, such as the re-developed destruction–construction​ operator, four local search methods under the framework of the variable neighborhood search strategy (VNS), the path-relinking method are applied to improve the performance of the algorithm. Comprehensive numerical experiments based on 900 small-scale benchmark instances and 810 large-scale benchmark instances are conducted to evaluate the performance of the presented algorithm. The experimental results obtained by KWWO are 1 to 4 times better than those obtained by the other comparison algorithms, which demonstrate that the effectiveness of KWWO is superior to the compared state-of-the-art algorithms for the considered problem. • Total tardiness minimization for the distributed assembly blocking flow-shop is investigated. • A constructive heuristic is proposed to obtain competitive initial solutions. • An effective water wave optimization algorithm with problem-specific knowledge is proposed. • Experimental results show the effectiveness of the presented methods. [ABSTRACT FROM AUTHOR]

• A hybrid discrete water wave optimization algorithm is proposed to solve NIFSP. • The new initialize method is applied to generate desirable solutions. • Three different strategies are introduced to improve the performance of HWWO. • The control parameters are calibrated with extensive experiments. • The comparisons of HWWO with state-of-the-arts algorithms are presented. The no-idle flowshop has attracted enormous attention owing to its widespread application in the manufacturing industry domain. In this paper, a hybrid discrete water wave optimization algorithm, named HWWO, is presented to solve the NIFSP with total tardiness. In order to improve the quality of a population, an initialize method based on a new priority rule combined with the modified NEH method is proposed to generate a population. In the propagation phase, a self-adaption selection neighborhood search structure is introduced to amplify the search range of waves and balance the exploration and exploitation ability of the HWWO. Afterwards, a variable neighborhood search is adopted to strengthen the local search and maintain the diversity of the population in the breaking phase. In the refraction operation, a perturbation sequence is generated and combined with the local optimal solution found by the breaking operation, in order to generate a new solution, and prevent the algorithm from becoming trapped in the local optimum. Furthermore, the control parameters and time complexity are analyzed. The experimental results and comparisons with the other state-of-the-art algorithms evaluated on Taillard's and Ruiz's benchmark sets reveal that the effectiveness and efficiency of the HWWO outperformed the compared algorithms for solving the NIFSP. [ABSTRACT FROM AUTHOR]