An effective hybrid meta-heuristic for flexible flow shop scheduling with limited buffers and step-deteriorating jobs

Zheng, Qianqian; Yu, Zhongbo; Hong-wei, Tian; He, Lijun

doi:10.1016/j.engappai.2021.104503

Cited by 26 publications

(3 citation statements)

References 40 publications

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“…53 They are simplified, flexible, take advantage of no-derivative mechanisms, and avoid local optima. 54,55 The optimization of complex energy, engineering, and waste management problems has been done utilizing these methods. 56 Well-established biological or physical phenomena often inspire metaheuristic approaches.…”

Section: Methods Descriptionmentioning

confidence: 99%

“…Although the traditional approaches, like, Levenberg–Marquardt (LM), are often engaged in the training process of ANN models, metaheuristic methods have also gained high popularity recently 53 . They are simplified, flexible, take advantage of no‐derivative mechanisms, and avoid local optima 54,55 . The optimization of complex energy, engineering, and waste management problems has been done utilizing these methods 56…”

Section: Methods Descriptionmentioning

confidence: 99%

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Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Khandakar

2022

Energy Science & Engineering

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Artificial neural networks (ANNs) can understand the behavior of a given system from the historical measurements of its associated variables. Adjusting the weight and bias of the ANN model using an optimization algorithm is known as the training process. The ANN reliability is directly related to the success of the training process. Therefore, this study investigates the effect of optimization algorithms on the prediction accuracy of the multilayer perceptron neural networks (MLPNNs). The complex gas hydrate prevention unit is simulated using the MLPNN model trained by 20 different optimization algorithms. This study investigates the gradient‐based, evolutionary, and Bayesian‐based optimization algorithms. Combining statistical and ranking analyses confirms that the Levenberg–Marquardt (LM) is the most efficient optimization technique for training the MLPNN model. This training algorithm adjusts the weight and bis parameters of the MLPNN so that the highest accurate predictions have been achieved. On the other hand, the trained MLPNN by imperialist competitive algorithm shows the lowest accuracy for the considered task. The trained MLPNN by the LM algorithm predicts 239 laboratory‐measured data sets about the methanol (MeOH) loss with the absolute average relative deviation of 6.4% and regression coefficient of 0.9643. Coupling the developed MLPNN and differential evolution optimization algorithm shows that temperature = 263 K and pressure = 6.92 MPa are the optimum condition for minimizing the MeOH loss in the gas hydrate prevention unit. Economic analysis confirms that the annual cost of methanol loss for the daily processing of 100 × 106 m3 of gas is ~17 million US dollars.

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Section: Methods Descriptionmentioning

confidence: 99%

Section: Methods Descriptionmentioning

confidence: 99%

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Khandakar

2022

Energy Science & Engineering

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“…They applied NSGA-II to obtain approximate Pareto solutions. Zheng et al (2021) addressed a flexible flow shop scheduling problem considering limited buffers and step-deteriorating jobs, where there were multiple non-identical parallel machines. To handle this problem, a hybrid metaheuristic algorithm based on GA, variable neighborhood search and simulated annealing is developed to minimize the makespan and total tardiness simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times

Mousavi,

Shahnazari-Shahrezaei

2023

Management and Production Engineering Review

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The paper considers the production scheduling problem in a hybrid flow shop environment with sequence-dependent setup times and the objectives of minimizing both the makespan and the total tardiness. The multi-objective genetic algorithm is applied to solve this problem, which belongs to the non-deterministic polynomial-time (NP)-hard class. In the structure of the proposed algorithm, the initial population, neighborhood search structures and dispatching rules are studied to achieve more efficient solutions. The performance of the proposed algorithm compared to the efficient algorithm available in literature (known as NSGA-II) is expressed in terms of the data envelopment analysis method. The computational results confirm that the set of efficient solutions of the proposed algorithm is more efficient than the other algorithm.

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A cooperative adaptive genetic algorithm for reentrant hybrid flow shop scheduling with sequence-dependent setup time and limited buffers

Zheng

Hong-wei

et al. 2023

Complex Intell. Syst.

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This paper deals with a reentrant hybrid flow shop problem with sequence-dependent setup time and limited buffers where there are multiple unrelated parallel machines at each stage. A mathematical model with the minimization of total weighted completion time is constructed for this problem. Considering the complexity of the problem at hand, an effective cooperative adaptive genetic algorithm (CAGA) is proposed. In the algorithm, a dual chain coding scheme and a staged-hierarchical decoding approach are, respectively, designed to encode and decode each solution. Six dispatch heuristics and a dynamic adjustment method are introduced to define initial population. To balance the exploration and exploitation abilities, three effective operations are implemented: (1) two new crossover and mutation operators with collaborative mechanism are imposed on genetic algorithm; (2) an adaptive adjustment strategy is introduced to re-optimize better solutions after mutation operations, where ant colony search algorithm and modified greedy heuristic are intelligently switched; (3) a reset strategy with dynamic variable step strategy is embedded to re-generate some non-improved solutions. A Taguchi method of design of experiment is adopted to calibrate the parameter values in the presented algorithm. Comparison experiments are executed on test instances with different scale problems. Computational results show that the proposed CAGA is more effective and efficient than several well-known algorithms in solving the studied problem.

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An effective hybrid meta-heuristic for flexible flow shop scheduling with limited buffers and step-deteriorating jobs

Cited by 26 publications

References 40 publications

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy

Minimizing the Makespan and Total Tardiness in Hybrid Flow Shop Scheduling with Sequence-Dependent Setup Times

A cooperative adaptive genetic algorithm for reentrant hybrid flow shop scheduling with sequence-dependent setup time and limited buffers

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