A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times

Zadeh, Melissa Shahgholi; Katebi, Yalda; Doniavi, Ali

doi:10.1080/00207543.2018.1524165

Cited by 54 publications

(8 citation statements)

References 35 publications

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“…Gong et al [22] considered the impact and potential of human factors on improving productivity and reducing production costs in real production systems and proposed a hybrid ABC to solve flexible job shop scheduling problems (FJSP) with worker flexibility. Zadeh et al [23] proposed a heuristic model based on an ABC for the dynamic FJSP. Lei et al [24] studied the distributed unrelated parallel machine scheduling problem with preventive maintenance (DUPMSP) and proposed an ABC with division to minimize MS. Xie et al [25] proposed an improved ABC considering machining structure evaluation to solve the flexible integrated scheduling problem of networked equipment, which is an extension of job shop scheduling.…”

Section: Related Workmentioning

confidence: 99%

“…The high-quality individuals then guide the poor individuals to self-renewal when the employed bees search around solutions. The high-quality individuals guided the poor individuals to different degrees, and (23) gives the degree to which each individual i guided the poor individuals. It is worth noting that the high-quality individuals only guide the poorer individuals in their neighborhood.…”

Section: Employed Beesmentioning

confidence: 99%

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Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Wang

2021

Mathematics

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In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

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Section: Related Workmentioning

confidence: 99%

Section: Employed Beesmentioning

confidence: 99%

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Wang

2021

Mathematics

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“…In recent years, the adaptive production scheduling problem with an uncertain environment has been an active research area [27][28][29][30][31][32]. Yang and Wang [33] presented a new adaptive neural network and heuristics hybrid approach for job-shop scheduling.…”

Section: A Adaptive Schedulingmentioning

confidence: 99%

Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN

2020

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“…With the popularization of automated manufacturing plants in the future, scheduling algorithm will be applied more widely. At the present stage, how to optimize the algorithm, improve the calculation speed of the algorithm and improve the optimization ability of the algorithm is the main goal [11,12,13].In this paper, the automatic processing of exible shop is taken as the background, and the assignment of tasks is solved by scheduling algorithm. In order to reduce the complexity of problem solving, the assignment of processing tasks and handling tasks is solved successively, and the accurate allocation of processing machines and handling AGVs is realized.In the process of solving the optimal solution, an improved genetic algorithm is proposed to solve the problem that the traditional genetic algorithm is easy to fall into the local optimal solution and the convergence speed is slow.…”

Section: Introductionmentioning

confidence: 99%

Research on Flexible Workshop Scheduling Algorithm Based on Improved Genetic Algorithm

Feng

Peng

Qin

2023

Preprint

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With the proposal of "Made in China 2025", the manufacturing industry is rapidly integrating with information technology, accelerating the pace of construction of smart factories, and reducing labor costs by establishing factories with few people and no one.Scheduling algorithm of flexible shop is the key technology to realize automatic processing of production line. The limited resources of shop can be allocated rationally through scheduling algorithm to complete automatic production.According to the requirements of automatic production line, this chapter realizes the assignment of processing task and handling task in turn.The classical genetic algorithm is easy to fall into local optimization and slow convergence.An improved genetic algorithm is adopted to introduce dynamic crossover and mutation probability. In addition, when the algorithm performs crossover operation, the individuals are first sorted according to their fitness values, and then cross operation is carried out successively.Through the simulation experiment, it is found that the task completion time obtained by the improved algorithm is 146 units of time, compared with the 198 units of time obtained before the improvement, the task completion time is reduced by 26%, and the improved algorithm has better convergence speed, so the improved algorithm has better optimization performance.

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A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times

Cited by 54 publications

References 35 publications

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN

Research on Flexible Workshop Scheduling Algorithm Based on Improved Genetic Algorithm

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