Abstract
Resource saving has become an integral aspect of manufacturing in industry 4.0. This paper proposes a multisystem optimization (MSO) algorithm, inspired by implicit parallelism of heuristic methods, to solve an integrated production scheduling with resource saving problem in textile printing and dyeing. First, a real-world integrated production scheduling with resource saving is formulated as a multisystem optimization problem. Then, the MSO algorithm is proposed to solve multisystem optimization problems that consist of several coupled subsystems, and each of the subsystems may contain multiple objectives and multiple constraints. The proposed MSO algorithm is composed of within-subsystem evolution and cross-subsystem migration operators, and the former is to optimize each subsystem by excellent evolution operators and the later is to complete information sharing between multiple subsystems, to accelerate the global optimization of the whole system. Performance is tested on a set of multisystem benchmark functions and compared with improved NSGA-II and multiobjective multifactorial evolutionary algorithm (MO-MFEA). Simulation results show that the MSO algorithm is better than compared algorithms for the benchmark functions studied in this paper. Finally, the MSO algorithm is successfully applied to the proposed integrated production scheduling with resource saving problem, and the results show that MSO is a promising algorithm for the studied problem.
Highlights
Industry 4.0, a new wave of industrialization, driven by smart information and communication technology, sparks a transformative view of framing process manufacturing and factory management practice [1,2,3]
It includes a production scheduling subsystem and a resource saving subsystem, and each of the subsystems contains multiple objectives. en, a multisystem optimization algorithm called the MSO algorithm, composed of within-subsystem evolution and cross-subsystem migration operators, is proposed to solve general multisystem optimization problem. e performance of the MSO algorithm is investigated on a set of benchmark functions, and the numerical simulations show that the proposed MSO algorithm is better than multiobjective multifactorial evolutionary algorithm (MO-MFEA) and NSGA-II for the most of the benchmark functions
The MSO algorithm is applied to the proposed integrated production scheduling with resource saving problem, and the results again show that the proposed MSO algorithm is a competitive multisystem optimization method
Summary
Industry 4.0, a new wave of industrialization, driven by smart information and communication technology, sparks a transformative view of framing process manufacturing and factory management practice [1,2,3]. E aim of this paper is to propose and study a multisystem optimization (MSO) algorithm, combining the multipopulation architecture of BBO/Complex with the classical NSGA-II to establish within-subsystem evolution and cross-subsystem migration operators and apply the new MSO algorithm to solving the production scheduling with resource saving problem in textile printing and dyeing plants. For the complete intersection group and no intersection group, the proposed MSO algorithm is better than NSGA-II but worse than MO-MFEA for CILS and NILS It indicates that solution variable intersection has a certain effect on optimization performance of the proposed MSO algorithm, and it is another important factor composing cross-subsystem migration in the MSO algorithm. It implies that the MSO algorithm can fully consider the inheritance of evolution information and relationship between optimization environment and performance, and migration between subsystems can effectively utilize these factors to accelerate global optimization
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