Energy-efficient scheduling of flexible job shops with complex processes: A case study for the aerospace industry complex components in China

Jiang, Xingyu; Tian, Zhiqiang; Liu, Weijun; Suo, Yingqi; Chen, Keqiang; Xu, Xiaowen; Li, Zhiwu

doi:10.1016/j.jii.2021.100293

Cited by 24 publications

(11 citation statements)

References 46 publications

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“…Each comparative algorithm was independently run 30 times on the test set to obtain the average IGD a and ξ . Tables (4) and (5) show the average values of x and y for the 10 groups obtained by the three algorithms. The best performing metric is indicated in bold and italic.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…The results in Table (5) show that IMOEA/D algorithm obtained the best performance in 8 instances, while EATS algorithm obtained the best performance in one test case, and IH-PSO algorithm obtained the best performance in one instance as well. Compared with the EATS algorithm, IMOEA/D algorithm significantly outperformed it in 4 instances (M1, M3, M6, and M9) and showed no significant difference in the other 6 instances.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Geng et al [4] proposed an improved multi-objective ant colony optimization algorithm (IMOALO) for the reentrant hybrid flow shop scheduling problem, with the goal of minimizing both makespan and energy consumption costs. Jiang et al [5] formulated a batch division scheduling model with the objectives of minimizing total energy consumption, manufacturing span, and processing cost, based on a real case of energy-saving scheduling problem for complex aerospace components. They also proposed a novel and improved crossover artificial bee colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Strategy Dynamic Evolution-Based Improved MOEA/D Algorithm for Solving Multi-Objective Fuzzy Flexible Job Shop Scheduling Problem

Liu

Hou

et al. 2023

IEEE Access

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A scheduling model was developed to optimize the maximum completion time, total machine load, and maximum machine load for the fuzzy flexible job shop problem with uncertain processing times. To solve this problem, a multi-strategy dynamic evolution-based improved multi-objective evolutionary algorithm based on decomposition(IMOEA/D) was proposed. In order to enhance the quality of the non-dominated solution set and improve the algorithm efficiency. The algorithm firstly employs a strategy based on minimum processing time and workload, along with a non-dominated solution prioritization mechanism to generate the initial population. Secondly, three evolutionary strategies are incorporated, and their probabilities are dynamically adjusted with the increase of evolution generations. Finally, a variable neighborhood search method is introduced to improve the search performance of the algorithm. The effectiveness of the proposed algorithm was demonstrated through experimental validation.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Strategy Dynamic Evolution-Based Improved MOEA/D Algorithm for Solving Multi-Objective Fuzzy Flexible Job Shop Scheduling Problem

Liu

Hou

et al. 2023

IEEE Access

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“…Based on a comprehensive analysis of the amount of equipment in each manufacturing cell, product types and batches, and product process routes and considering the area of the manufacturing cell, safety Computational Intelligence and Neuroscience distance between equipment, and workers' operation space, the optimized cell layout of the production plant of an aerospace enterprise is drawn based on SLP, as shown in Figure 16. We note that the related information of processing time is available in our previous work [35].…”

Section: Dynamic Layout Solutionmentioning

confidence: 99%

Reconstruction Layout Optimization of Multivariety and Small Batch Workshop in Aerospace Industry

Zhao

Jiang

Tian

et al. 2022

Computational Intelligence and Neuroscience

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To cope with the problems of frequent mold changes, long production cycles and serious logistics crossings in workshop of aerospace enterprise. First, a manufacturing cell layout planning method based on the feature bit code domain method and K-Means++ is proposed to realize the accurate division of manufacturing cells. Then, a multiobjective optimization method of dynamic reconstruction layout based on improved fruit fly optimization algorithm (IFOA) is proposed to solve the reconstruction layout optimization of the production workshop problem with the optimization objectives of logistics cost, reconstruction cost, loss cost, and cell integrated area. Finally, plant simulation software is applied to simulate the workshop layout before and after optimization. The simulation results show that the logistics cost of the workshop cell layout after optimization is reduced by 8.7%, the utilization rate of the workshop area is improved by 5.2%, and the value-added rate of products is increased by 6.6%, which verifies the effectiveness and feasibility of the proposed model and method.

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“…Zheng [20] and others studied the job-shop scheduling problem with dual resource constraints, established a model with the goal of minimizing the completion time, combined a knowledge-guided search with a smell-based search, and proposed a new encoding scheme, a knowledge-guided Drosophila optimization algorithm, to solve it. Jiang et al [21] addressed an energy-efficient scheduling problem of flexible job-shops with complex processes, and they proposed a novel improved-crossover artificial bee colony algorithm. Wang et al [22] firstly performed an improved genetic algorithm to solve the energy consumption scheduling problem of a remanufacturing system with disassembly, reprocessing and reassembly, and then they provided a new idea for the integrated optimization of a remanufacturing system.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimization of Process Planning and Scheduling for Aerospace Complex Component Based on Honey-Bee Mating Algorithm

et al. 2023

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To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop scheduling for aerospace manufacturing enterprises is proposed. This paper considers the process flexibility of aerospace complex components comprehensively, and an integrated optimization model for the process planning and production scheduling of aerospace complex components is established with the optimization objectives of achieving a minimum makespan, machining time and machining cost. A honey-bee mating optimization algorithm (HBMO) combined with the greedy algorithm was proposed to solve the model. Then, it formulated a four-layer encoding method based on a feature-processing sequence, processing method, and machine tool, a tool was designed, and five worker bee cultivation strategies were designed to effectively solve the problems of infeasible solutions and local optimization when a queen bee mated to a drone. Finally, taking the complex component parts of an aerospace enterprise as an example, the integrated optimization of process planning and workshop scheduling is carried out. The results demonstrate that the proposed model and algorithm can effectively shorten the makespan and machining time, and reduce the machining cost.

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Energy-efficient scheduling of flexible job shops with complex processes: A case study for the aerospace industry complex components in China

Cited by 24 publications

References 46 publications

Multi-Strategy Dynamic Evolution-Based Improved MOEA/D Algorithm for Solving Multi-Objective Fuzzy Flexible Job Shop Scheduling Problem

Multi-Strategy Dynamic Evolution-Based Improved MOEA/D Algorithm for Solving Multi-Objective Fuzzy Flexible Job Shop Scheduling Problem

Reconstruction Layout Optimization of Multivariety and Small Batch Workshop in Aerospace Industry

Integrated Optimization of Process Planning and Scheduling for Aerospace Complex Component Based on Honey-Bee Mating Algorithm

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