A Knowledge-Based Cooperative Algorithm for Energy-Efficient Scheduling of Distributed Flow-Shop

Wang, Jing-jing; Wang, Ling

doi:10.1109/tsmc.2017.2788879

Cited by 167 publications

(29 citation statements)

References 43 publications

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“…Gao et al [77] proposed a new hybrid search algorithm, which introduced the balance strategy into the improved particle swarm optimization algorithm, and then combined with tabu search to achieve efficient search for FSSPs. Wang and Wang [78] improved the search algorithm according to the characteristics of other search algorithms. It included the improved initialization scheme based on NEH and other properties based on knowledge-based search algorithm.…”

Section: Search Algorithmsmentioning

confidence: 99%

Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem

Jiāng¹,

Li-hong²,

Li³

et al. 2021

IJCIS

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The permutation flow shop scheduling problem (PFSSP) is a typical production scheduling problem and it has been proved to be a nondeterministic polynomial (NP-hard) problem when its scale is larger than 3. The whale optimization algorithm (WOA) is a new swarm intelligence algorithm which performs well for PFSSP. But the stability is still low, and the optimization results are not too good. On this basis, we optimize the parameters of WOA through chaos theory, and put forward a chaotic whale algorithm (CWA). Firstly, in this paper, the proposed CWA is combined with Nawaz-Ensco-Ham (NEH) and largest-rank-value (LRV) rule to initialize the population. Next, chaos theory is applied to WOA algorithm to improve its convergence speed and stability. On this basis, we also use cross operator and reversal-insertion operator to enhance the search ability of the algorithm. Finally, the improved local search algorithm is used to optimize the job sequence to find the minimum makespan. In several experiments, different benchmarks are used to investigate the performance of CWA. The experimental results show that CWA has better performance than other scheduling algorithms.

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Section: Search Algorithmsmentioning

confidence: 99%

Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem

Jiāng¹,

Li-hong²,

Li³

et al. 2021

IJCIS

View full text Add to dashboard Cite

show abstract

“…For the hybrid flow shop scheduling problem, Liu et al [17] proposed an evolutionary algorithm based on weighted sum approach to minimize both makespan and total energy consumption. For the distributed permutation flow shop scheduling problem, J. J. Wang and L. Wang [18] proposed a knowledge-based cooperative algorithm to minimize both makespan and energy consumption. For flexible job shop scheduling problem with variable processing speed, Li et al [19] proposed an improved artificial bee colony algorithm to minimize makespan, machine loading, and total carbon emission.…”

Section: Introductionmentioning

confidence: 99%

Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

Jiang

Wang

2021

Tsinghua Sci. Technol.

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This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks (EADHFSPMT) by considering two objectives simultaneously, i.e., makespan and total energy consumption. It consists of three sub-problems, i.e., job assignment between factories, job sequence in each factory, and machine allocation for each job. We present a mixed inter linear programming model and propose a Novel Multi-Objective Evolutionary Algorithm based on Decomposition (NMOEA/D). We specially design a decoding scheme according to the characteristics of the EADHFSPMT. To initialize a population with certain diversity, four different rules are utilized. Moreover, a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors. To enhance the quality of solutions, two local intensification operators are implemented according to the problem characteristics. In addition, a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence, which can adaptively modify weight vectors according to the distribution of the non-dominated front. Extensive computational experiments are carried out by using a number of benchmark instances, which demonstrate the effectiveness of the above special designs. The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.

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“…This additional dimension minimizes the maximum global makespan of Ffactories to determine the jobs that should be assigned to each factory. Therefore, from the specific situation of F = 1 in [14], DPFSP is a typical PFSP problem. For DPFSP, several integer linear programming models and two Fig.…”

Section: Introductionmentioning

confidence: 99%

A memetic discrete differential evolution algorithm for the distributed permutation flow shop scheduling problem

Zhao

2021

Complex Intell. Syst.

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The distributed manufacturing has become a prevail production mode under the economic globalization. In this article, a memetic discrete differential evolution (MDDE) algorithm is proposed to address the distributed permutation flow shop scheduling problem (DPFSP) with the minimization of the makespan. An enhanced NEH (Nawaz–Enscore–Ham) method is presented to produce potential candidate solutions and Taillard’s acceleration method is adopted to ameliorate the operational efficiency of the MDDE. A new discrete mutation strategy is introduced to promote the search efficiency of the MDDE. Four neighborhood structures, which are based on job sequence and factory assignment adjustment mechanisms, are introduced to prevent the candidates from falling the local optimum during the search process. A neighborhood search mechanism is selected adaptively through a knowledge-based strategy which focuses on the adaptive evaluation for the neighborhood selection. The optimal combinations of parameters in the MDDE algorithm are testified by the design of experiment. The computational results and comparisons demonstrated the effectiveness of the MDDE algorithm for solving the DPFSP.

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A Knowledge-Based Cooperative Algorithm for Energy-Efficient Scheduling of Distributed Flow-Shop

Cited by 167 publications

References 43 publications

Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem

Enhancing Whale Optimization Algorithm with Chaotic Theory for Permutation Flow Shop Scheduling Problem

Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

A memetic discrete differential evolution algorithm for the distributed permutation flow shop scheduling problem

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