A knowledge-driven constructive heuristic algorithm for the distributed assembly blocking flow shop scheduling problem

Yang, Yahong; Li, Xun

doi:10.1016/j.eswa.2022.117269

Cited by 23 publications

(2 citation statements)

References 26 publications

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“…Its importance and popularity make optimization has been implemented in many areas, such as in computing system [1], transportation [2], mobile communication [3], production and manufacturing [4], healthcare [5], power system [6], home appliance [7], and so on. In production and manufacture, some study need optimization, such as in flow shop scheduling in multi factory environment [8], nozzle assignment in printed circuit board assembly [9], and job assignment in manufacture where there are several parallel machines [10], and so on. Several objectives in the manufacturing optimization problems are minimizing the completion time [8], total assembly time [9], make-span [11], maximizing service level [12], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…In production and manufacture, some study need optimization, such as in flow shop scheduling in multi factory environment [8], nozzle assignment in printed circuit board assembly [9], and job assignment in manufacture where there are several parallel machines [10], and so on. Several objectives in the manufacturing optimization problems are minimizing the completion time [8], total assembly time [9], make-span [11], maximizing service level [12], and so on. Meanwhile, the common objectives in the optimization work in the power system is higher energy harvesting [13], minimizing power loss [14], maximizing power point [15], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

Kusuma,

Hasibuan

2024

Bulletin EEI

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This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

Kusuma,

Hasibuan

2024

Bulletin EEI

View full text Add to dashboard Cite

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BRCE: bi-roles co-evolution for energy-efficient distributed heterogeneous permutation flow shop scheduling with flexible machine speed

Huang

Gong

et al. 2023

Complex Intell. Syst.

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Distributed manufacturing is the mainstream model to accelerate production. However, the heterogeneous production environment makes engineer hard to find the optimal scheduling. This work investigates the energy-efficient distributed heterogeneous permutation flow scheduling problem with flexible machine speed (DHPFSP-FMS) with minimizing makespan and energy consumption simultaneously. In DHPFSP-FMS, the local search misleads the population falling into local optima which reduces the convergence and diversity. To solve this problem, a bi-roles co-evolutionary algorithm is proposed which contains the following improvements: First, the global search and local search is divided into two swarms producer and consumer to balance computation. Second, three heuristic rules are designed to get a high-quality initialization population. Next, five problem-based local search strategies are designed to accelerate converging. Then, an efficient energy-saving strategy is presented to save energy. Finally, to verify the performance of the proposed algorithm, 22 instances are generated based on the Taillard benchmark, and a number of numerical experiments are adopted. The experiment results state that our algorithm is superior to the state-of-arts and more efficient for DHPFSP-FMS.

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An improved fruit fly optimization algorithm with Q-learning for solving distributed permutation flow shop scheduling problems

Zhao,

Wu,

Zuo

et al. 2024

Complex Intell. Syst.

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The distributed permutation flow shop scheduling problem (DPFSP) is one of the hottest issues in the context of economic globalization. In this paper, a Q-learning enhanced fruit fly optimization algorithm (QFOA) is proposed to solve the DPFSP with the goal of minimizing the makespan. First, a hybrid strategy is used to cooperatively initialize the position of the fruit fly in the solution space and the boundary properties are used to improve the operation efficiency of QFOA. Second, the neighborhood structure based on problem knowledge is designed in the smell stage to generate neighborhood solutions, and the Q-learning method is conducive to the selection of high-quality neighborhood structures. Moreover, a local search algorithm based on key factories is designed to improve the solution accuracy by processing sequences of subjobs from key factories. Finally, the proposed QFOA is compared with the state-of-the-art algorithms for solving 720 well-known large-scale benchmark instances. The experimental results demonstrate the most outstanding performance of QFOA.

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A knowledge-driven constructive heuristic algorithm for the distributed assembly blocking flow shop scheduling problem

Cited by 23 publications

References 26 publications

Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

BRCE: bi-roles co-evolution for energy-efficient distributed heterogeneous permutation flow shop scheduling with flexible machine speed

An improved fruit fly optimization algorithm with Q-learning for solving distributed permutation flow shop scheduling problems

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