Abstract
This paper studies a novel and practical distributed flexible assembly permutation flowshop scheduling problem with makespan criterion, which has attracted wide attention due to important applications in modern manufacturing. The problem integrates two machine environments of distributed production and flexible assembly, which can process and assemble the jobs into customized products. We first present a mixed integer linear programming model to characterize the problem essence and to solve small-size problems. Due to the NP-hard, we further propose an efficient memetic algorithm, which consists of a global exploration optimizer designed based on improved social spider optimization and two local exploitation optimizers designed based on meta-Lamarckian learning and simplex search, respectively. To implement the algorithm, a problem-specific encoding scheme is presented. Algorithmic parameters are calibrated by a design of experiments, and a comprehensive computational campaign is conducted to evaluate the performance of the mathematical model and algorithms. Statistical results show that their problem-solving abilities are effective, and especially the proposed memetic algorithm outperforms the existing algorithms significantly.
Highlights
Flowshop scheduling problem (FSP) has been extensively studied in operational research and computer science due to its theoretical significance and practical applications [1]–[5]
According to the unique design preferences and evolutionary framework of MA [13], this paper presents an efficient MA for the DFAPFSP, and its main contributions are summarized as follows: a) Propose a global exploration optimizer by refining an efficient social spider optimization, which possesses the abilities of bi-population co-evolution and cross-population interaction; b) Propose a meta-Lamarckian learning strategy-based local exploitation optimizer to refine the best solution in population; c) Propose a simplex search-based local exploitation operator to refine the diversity and the overall quality of the population; d) Formulate a novel MA to cope with the DFAPFSP based on the above global and local optimizers
For solving the DFAPFSP with different sizes, we propose a memetic algorithm that mainly consists of a global exploration optimizer devised by improving social spider optimization and two local exploitation optimizers devised separately based on meta-Lamarckian learning and simplex search
Summary
Flowshop scheduling problem (FSP) has been extensively studied in operational research and computer science due to its theoretical significance and practical applications [1]–[5]. G. Zhang et al.: MA With Meta-Lamarckian Learning and Simplex Search for DFAPFSP job-processing plus a single machine for product-assembling. This paper adopts the flexible assembly layout to replace the single machine assembly in DAPFSP, which generates a more practical distributed scheduling problem with flexible assembly, termed distributed flexible assembly permutation flowshop scheduling problem (DFAPFSP). An example of DFAPFSP can be found in the manufacturing of automobile engines Different parts such as bent axles, cylinder blocks and cylinder heads are first processed using one of multiple flowshops, and assembled into the complete engine on one of some unrelated assembly machines. A high-quality solution in optimization problem can be gained by EA mainly depending on the good balance of exploration and exploitation abilities In this sense, EA is not competent due to its insufficient local search ability.
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