Modified Multi-Crossover Operator NSGA-III for Solving Low Carbon Flexible Job Shop Scheduling Problem

Sun, Xingping; Wang, Ye; Kang, Hongwei; Shen, Yong; Chen, Qingyi; Da, Wang

doi:10.3390/pr9010062

Cited by 26 publications

(12 citation statements)

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“…In the early stage, the constructive approach, widely known as NEH [10], is probably the one mostly refined and modified by other authors [11]. On the other hand, many meta-heuristics and their modified approaches have been applied to solve FSSP, such as particle swarm optimization [12,13], simulated annealing [14], genetic algorithm [13,15,16], ant colony optimization [7], and firefly algorithm [17].…”

Section: Introductionmentioning

confidence: 99%

Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA

et al. 2021

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In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.

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Section: Introductionmentioning

confidence: 99%

Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA

et al. 2021

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“…These systems can be as simple as spreadsheets with basic functionalities (e.g., Excel) or more complex, allowing advanced users with more experience to obtain graphical representations, visualize performance metrics and have access to effective optimization methods. Some modern example of successful approaches are presented in [7][8][9][10].…”

Section: Scheduling Problemsmentioning

confidence: 99%

“…Of these two approaches, the one that stands out is the one that uses approximation algorithms, given its efficiency and possibility of being used in production environments of real industries. Some examples of successful approaches are presented in [9,20,21].…”

Section: Job Shop Problemmentioning

confidence: 99%

Intelligent Scheduling with Reinforcement Learning

et al. 2021

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In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides a solution quickly, or use classic optimization approaches (e.g., metaheuristics) that take more time but will output better solutions, closer to their optimal value. In this work, we aim to create a novel architecture that incorporates reinforcement learning into scheduling systems in order to improve their overall performance and overcome the limitations that current approaches present. It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. The reported experimental results and the conducted statistical analysis conclude about the benefits of using an intelligent agent created with reinforcement learning techniques. The main contribution of this work is proving that reinforcement learning has the potential to become the standard method whenever a solution is necessary quickly, since it solves any problem in very few seconds with high quality, approximate to the optimal methods.

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“…Considering carbon emissions, Sun et al [14] establish a multiobjective flexible job shop scheduling problem (MO-FJSP) mathematical model with minimum completion time, carbon emission, and machine load. To solve this problem, they study six variants of the non-dominated sorting genetic algorithm-III (NSGA-III) and from the experimental results, it can be concluded that the NSGAIII-COE has significant advantages in solving the low carbon MO-FJSP.…”

Section: Scheduling and Planning Issuesmentioning

confidence: 99%