A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products

Abstract

Nowadays, the production model of many enterprises is multi-variety customized production, and the makespan and total tardiness are the main metrics for enterprises to make production plans. This requires us to develop a more effective production plan promptly with limited resources. Previous research focuses on dispatching rules and algorithms, but the application of the knowledge mining method for multi-variety products is limited. In this paper, a hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness for multi-variety products is proposed. First, through offline machine learning and data mining, attributes of operations are selected to mine the initial population knowledge. Second, an addition–deletion sorting method (ADSM) is proposed to reprioritize operations and then form the rule-based initial population. Finally, the nondominated sorting genetic algorithm II (NSGA-II) hybrid with simulated annealing is used to obtain the Pareto solutions. To evaluate the effectiveness of the proposed method, three other types of initial populations were considered under different iterations and population sizes. The experimental results demonstrate that the new approach has a good performance in solving the multi-variety production planning problems, whether it is the function value or the performance metric of the acquired Pareto solutions.

Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission

“…In this section, an improved imperial competition algorithm (I-ICA) is used to solve the low-carbon resource allocation problem in a discrete manufacturing job shop. The imperial competition algorithm (ICA) is a socially inspired random optimization search algorithm, which has certain advantages in large-scale combinatorial optimization problems [9]. The initial solution generated by the ICA algorithm is, however, unevenly distributed in the solution space, and will make the final solution prone to bias towards the local optimum.…”

“…Reasonable resource allocation can continue to bring economic growth to the enterprise. However, current resource allocation research mainly considers production-efficiency-related indicators, such as maximum processing time [9,10], production delay and machine load [11,12], as optimization objectives. These studies cannot meet the actual needs of sustainable manufacturing because they do not consider processing energy consumption such as electric energy [13,14].…”

Analysis of The Development Trend of Sports Research in China and Taiwan Using Natural Language Processing

“…Therefore, systematic analysis of research abstracts accumulated over the years will enhance the understanding of the development process, research characteristics and future development trends of an academic field [19]. However, the speed of data generation and storage on various network platforms far exceeds the speed that people can analyze and digest, which also allows data mining and text mining technology to play an extremely important role in exploring the application of big data analysis [5,6,12,13,25]. Generally, data mining is mostly applied to the processing of structured data, just like a table with a fixed structure, in which each column has its own clear definition and numerical value.…”

Deep Reinforcement Learning-Based Job Shop Scheduling of燬mart燤anufacturing

“…With the common technology, different tools for optimization, such as data science, the Internet of Things (IOTs) [16], and artificial intelligence AI fields create new opportunities in production control. Many studies apply the reinforcement learning approach to model routing and scheduling optimization problems such as [17][18][19][20][21][22][23][24], which performed better than traditional algorithms on some complex combinatorial optimization problems. Researchers in [25][26][27] introduced Graph Neural Networks (GNN) and GIN to take advantage of the graph representation and solve graph-based optimization problems; they are an innovative combination of aggregative optimization and deep learning.…”