Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Tian, Xiang; Liu, Xiyu

doi:10.3390/pr9020219

Cited by 11 publications

(9 citation statements)

References 37 publications

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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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“…Determine the value of adaptive randomization parameter α from Eq. (12) for i = 1:n all n fireflies for j = 1:n all n fireflies Attractiveness varies with distance r via e [−γr2] if (X j < X i ) then (if firefly j is better) Update parameter value α from Eq. ( 12)…”

Section: Generate Initial Population Of Firefliesmentioning

confidence: 99%

“…3-5 depicts the Gantt chart of solutions. J (11), J (12), and so on represent the job number and operation in Gantt charts. Hatched lines show the machine's idle time.…”

Section: Experiments 2-du Test Instances and Rajkumar Instancementioning

confidence: 99%

“…Naifar et al [11] presented Leader Tree Guided Optimization, and they found two new optimal results for Hurink benchmark instances. Tian et al [12] incorporated different variants of the Genetic Algorithm (GA) with PSO and a local search, and the algorithm was tested on Lawrence Benchmark instances. An et al [13] solved a multi-objective problem by developing a nondominated sorting Biogeography method with a hybrid Variable neighborhood search (VNS).…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Adaptive Firefly Algorithm for Flexible Job Shop Scheduling

Devi¹,

Mishra²,

Madan³

2022

Intelligent Automation &Amp; Soft Computing

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An NP-hard problem like Flexible Job Shop Scheduling (FJSP) tends to be more complex and requires more computational effort to optimize the objectives with contradictory measures. This paper aims to address the FJSP problem with combined and contradictory objectives, like minimization of make-span, maximum workload, and total workload. This paper proposes 'Hybrid Adaptive Firefly Algorithm' (HAdFA), a new enhanced version of the classic Firefly Algorithm (FA) embedded with adaptive parameters to optimize the multi objectives concurrently. The proposed algorithm has adopted two adaptive strategies, i.e., an adaptive randomization parameter (α) and an effective heterogeneous update rule for fireflies. The adaptations proposed by this paper can help the optimization process to strike a balance between diversification and intensification. Further, an enhanced local search algorithm, Simulated Annealing (SA), is hybridized with Adaptive FA to explore the local solution space more efficiently. This paper has also attempted to solve FJSP by a rarely used integrated approach where assignment and sequencing are done simultaneously. Empirical simulations on benchmark instances demonstrate the efficacy of our proposed algorithms, thus providing a competitive edge over other nature-inspired algorithms to solve FJSP.

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“…SNSbased MIEAs are presented by using tissue-like P systems or neural-like P systems with various network topologies [68]. Various meta-heuristic algorithms, such as GA [69], DE [70] and its variants [71,72], PSO [73,74], ABC [75], and BBO [76], are usually introduced to SNS-based MIEAs as the basic evolutionary operation in the cell or neural [77][78][79][80][81]. The membrane structure in DNS-based MIEAs can be dynamically changed according to communication channel rules, and this class of MIEAs, with an extended membrane structure, has great potential for solving complex problems [82,83].…”

Section: Introductionmentioning

confidence: 99%

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

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An extended membrane system using a tissue-like P system with evolutional symport/antiport rules and a promoter/inhibitor, which is based on the evolutionary mechanism of quantum-behaved particle swarm optimization (QPSO) and improved QPSO, named CQPSO-ETP, is designed and developed in this paper. The purpose of CQPSO-ETP is to enhance the optimization performance of statistical network structure-based membrane-inspired evolutionary algorithms (SNS-based MIEAs) and the QPSO technique. In CQPSO-ETP, evolution rules with a promoter based on a standard QPSO mechanism are introduced to evolve objects, and evolution rules with an inhibitor based on an improved QPSO mechanism using self-adaptive selection, and cooperative evolutionary and logistic chaotic mapping methods, are adopted to avoid prematurity. The communication rules with a promoter/inhibitor for objects are introduced to achieve the exchange and sharing of information between different membranes. Under the control of the evolution and communication mechanism, the CQPSO-ETP can effectively improve the performance with the help of a distributed parallel computing model. The proposed CQPSO-ETP is compared with PSO, QPSO and two existing improved QPSO approaches which are conducted on eight classic numerical benchmark functions to verify the effectiveness. Furthermore, computational experiments which are made on eight tested images with three comparative clustering approaches are adopted, and the experimental results demonstrate the clustering validity of the proposed CQPSO-ETP.

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Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Cited by 11 publications

References 37 publications

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

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

A Dynamic Adaptive Firefly Algorithm for Flexible Job Shop Scheduling

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

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