Bi-objective optimization using an improved NSGA-II for energy-efficient scheduling of a distributed assembly blocking flowshop

Niu, Wei; Li, Junqing; Jin, Hui; Qi, Rui; Sang, Hongshi

doi:10.1080/0305215x.2022.2032017

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…Owing to its low computational complexity, it is considered as a multi-objective optimization algorithm for Pareto optimal solutions. In recent years, several relevant scholars have carried out a great deal of research on the application of NSGA-II in flow shop scheduling [31]- [35]. To evaluate the effectiveness of the introduced mechanism, Basseur et al [36] progressed a dynamic mutation Pareto Genetic Algorithm (GA), a combined diversification mechanism that integrates target space sharing and decision space sharing, and a hybrid algorithm that combines Pareto genetic algorithm and local search.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Flow Shop Scheduling Problem of Preventive Maintenance and Degradation With an Improved NSGA-II Algorithm

2023

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The main objective of this research is to establish and solve a scheduling model for the degraded flow shop taking completion time and average device idle time as optimization objectives. Therefore, considering the interaction among schedule, production, maintenance and degradation, a mathematical model with completion time as well as average device idle time is constructed. Based on the NSGA-II algorithm, a local search strategy is first proposed to obtain junction points and sparse points according to the non-dominated sorted results. Subsequently, the comparative experiments are conducted utilizing NSGA-II-ALS, NSGA-II, and PSO algorithms. The optimal solution sets and Pareto diagrams of the three algorithms are obtained. The Gantt charts of the optimal solutions are drawn. Eventually, the multi-objective evaluation indexes of each algorithm are calculated and compared. Using statistical experiments and analysis, it can be found that NSGA-II-ALS exhibit excellent performance compared with other algorithms. The scheduling model proposed in this paper is able to effectively coordinate and balance the relationship among production, equipment maintenance and degradation, thus improving the efficiency of manufacturing systems, saving production costs, and realizing the optimal utilization of resources.

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Section: Introductionmentioning

confidence: 99%

An Integrated Flow Shop Scheduling Problem of Preventive Maintenance and Degradation With an Improved NSGA-II Algorithm

2023

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“…Jiang et al [15] studied the energy-aware DHFS with multiprocessor tasks with considering total energy consumption and makespan. Niu et al [16] developed an improved NSGA-II algorithm to solve an energy-efficient distributed assembly blocking flow shop problem. Qin et al [17] utilized a realistic DHFS where a novel integrated production and distribution scheduling problem is focused.…”

Section: Introductionmentioning

confidence: 99%

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

Geng¹,

Li²

2023

Computer Modeling in Engineering &Amp; Sciences

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To solve the distributed hybrid flow shop scheduling problem (DHFS) in raw glass manufacturing systems, we investigated an improved hyperplane assisted evolutionary algorithm (IhpaEA). Two objectives are simultaneously considered, namely, the maximum completion time and the total energy consumptions. Firstly, each solution is encoded by a three-dimensional vector, i.e., factory assignment, scheduling, and machine assignment. Subsequently, an efficient initialization strategy embeds two heuristics are developed, which can increase the diversity of the population. Then, to improve the global search abilities, a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions. Furthermore, a local search heuristic based on three parts encoding is embedded to enhance the searching performance. To enhance the local search abilities, the cooperation of the search operator is designed to obtain better non-dominated solutions. Finally, the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms. The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

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Energy-efficient scheduling model and method for assembly blocking permutation flow-shop in industrial robotics field

Kong,

Wu,

Zhang

et al. 2024

Artif Intell Rev

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Implementing green and sustainable development strategies has become essential for industrial robot manufacturing companies to fulfill their societal obligations. By enhancing assembly efficiency and minimizing energy consumption in workshops, these enterprises can differentiate themselves in the fiercely competitive market landscape and ultimately bolster their financial gains. Consequently, this study focuses on examining the collaborative assembly challenges associated with three crucial parts: the body, electrical cabinet, and pipeline pack, within the industrial robot manufacturing process. Considering the energy consumption during both active and idle periods of the industrial robot workshop assembly system, this paper presents a multi-stage energy-efficient scheduling model to minimize the total energy consumption. Two classes of heuristic algorithms are proposed to address this model. Our contribution is the restructuring of the existing complex mathematical programming model, based on the structural properties of scheduling sub-problems across multiple stages. This reformation not only effectively reduces the variable scale and eliminates redundant constraints, but also enables the Gurobi solver to tackle large-scale problems. Extensive experimental results indicate that compared to traditional workshop experience, the constructed green scheduling model and algorithm can provide more precise guidance for the assembly process in the workshop. Regarding total energy consumption, the assembly plans obtained through our designed model and algorithm exhibit approximately 3% lower energy consumption than conventional workshop experience-based approaches.

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Bi-objective optimization using an improved NSGA-II for energy-efficient scheduling of a distributed assembly blocking flowshop

Cited by 11 publications

References 43 publications

An Integrated Flow Shop Scheduling Problem of Preventive Maintenance and Degradation With an Improved NSGA-II Algorithm

An Integrated Flow Shop Scheduling Problem of Preventive Maintenance and Degradation With an Improved NSGA-II Algorithm

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

Energy-efficient scheduling model and method for assembly blocking permutation flow-shop in industrial robotics field

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