An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II

Delgoshaei, Aidin; Ariffin, Mohd Khairol Anuar Bin Mohd; Leman, Zulkiflle

doi:10.3390/math10234607

Cited by 3 publications

(3 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional exact methods cannot effectively solve largescale scheduling problems in an acceptable period of time [10][11][12]. In order to better meet the needs of real-world problems, metaheuristic algorithms have become an important tool for solving such problems [13]. Given the multi-flexibility and complexity of IPPS problems, it is necessary to devise an efficient algorithm for solving multi-objective IPPS problems.…”

Section: Introductionmentioning

confidence: 99%

Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling

et al. 2023

View full text Add to dashboard Cite

Integrated process planning and scheduling (IPPS) is important for modern manufacturing companies to achieve manufacturing efficiency and improve resource utilization. Meanwhile, multiple objectives need to be considered in the realistic decision-making process for manufacturing systems. Based on the above realistic manufacturing system requirements, it becomes increasingly important to develop effective methods to deal with multi-objective IPPS problems. Therefore, an improved NSGA-II (INSGA-II) algorithm is proposed in this research, which uses the fast non-dominated ranking method for multiple optimization objectives as an assignment scheme for fitness. A multi-layer integrated coding method is adopted to address the characteristics of the integrated optimization model, which involves many optimization parameters and interactions. Elite and mutation strategies are employed during the evolutionary process to enhance population diversity and the quality of solutions. An external archive is also used to store and update the Pareto solution. The experimental results on the Kim test set demonstrate the effectiveness of the proposed INSGA-II algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Aiming at the distributed scheduling problem of flow-shop and job-shop in traditional industry, many experts and scholars have carried out in-depth research from different research angles [2][3][4][5][6][7][8][9][10][11]. Zhao et al proposed a pure reactive scheduling method for updating scheduling strategies to deal with the interference of uncertainty in the arrival of new jobs on the shop floor [2].…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al proposed a pure reactive scheduling method for updating scheduling strategies to deal with the interference of uncertainty in the arrival of new jobs on the shop floor [2]. Delgoshaei et al proposed a fuzzy-weighted NSGA-II (FW-NSGA-II) to address the developed non-linear fuzzy multi-objective dual resource-constrained scheduling problem [3]. Song et al used the genetic programming hyperheuristic algorithm to solve a class of distributed assembly permutation flow shop scheduling problems related to time and sequence [4].…”

Section: Introductionmentioning

confidence: 99%

Symmetric Two-Workshop Heuristic Integrated Scheduling Algorithm Based on Process Tree Cyclic Decomposition

Zhou

Yang

et al. 2023

Electronics

View full text Add to dashboard Cite

The existing research on the two-workshop integrated scheduling problem with symmetrical resources does not consider the complex product attribute structure and the objective situation of plant equipment resources. This results in the prolongation of the product makespan and the reduction of the utilization rate of the general equipment in the workshop. To solve the above problems, a two-workshop integrated scheduling algorithm based on process tree cyclic decomposition (STHIS-PTCD) was proposed. First, a workshop scheduling scheme based on the sub-tree cyclic decomposition strategy was proposed to improve the closeness of continuous processing further. Second, an operation allocation scheme based on the principle of workshop processing balance was presented. On the basis of ensuring the advantages of parallel processing, it also effectively reduces the idle time of equipment resources and then optimizes the overall effect of the integrated scheduling of both workshops. Through the comparison and analysis of all the existing resource-symmetric two-workshop integrated scheduling algorithms, the scheduling effect of the proposed algorithm is the best.

show abstract

Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning

et al. 2023

View full text Add to dashboard Cite

This feasibility study utilized regression models to predict makespan robustness in dynamic production processes with uncertain processing times. Previous methods for robustness determination were computationally intensive (Monte Carlo experiments) or inaccurate (surrogate measures). However, calculating robustness efficiently is crucial for field-synchronous scheduling techniques. Regression models with multiple input features considering uncertain processing times on the critical path outperform traditional surrogate measures. Well-trained regression models internalize the behavior of a dynamic simulation and can quickly predict accurate robustness (correlation: r>0.98). The proposed method was successfully applied to a permutation flow shop scheduling problem, balancing makespan and robustness. Integrating regression models into a metaheuristic model, schedules could be generated that have a similar quality to using Monte Carlo experiments. These results suggest that employing machine learning techniques for robustness prediction could be a promising and efficient alternative to traditional approaches. This work is an addition to our previous extensive study about creating robust stable schedules based on deep reinforcement learning and is part of the applied research project, Predictive Scheduling.

show abstract

An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II

Cited by 3 publications

References 53 publications

Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling

Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling

Symmetric Two-Workshop Heuristic Integrated Scheduling Algorithm Based on Process Tree Cyclic Decomposition

Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning

Contact Info

Product

Resources

About