Research on multi-agent genetic algorithm based on tabu search for the job shop scheduling problem

Peng, Chong; Wu, Guo Qing; Liao, T. Warren; Wang, Hedong

doi:10.1371/journal.pone.0223182

Cited by 20 publications

(12 citation statements)

References 27 publications

(23 reference statements)

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“…The 43 JSSP benchmark instances are selected from the OR Library [31], which contains 3 instances (FT06, FT10, FT20) designed by Fisher and Thompson [32] and 40 instances (LA01~LA40) designed by Lawrence [33]. The four comparative heuristics used for comparison are MAGATS [21], NIMGA [34], aLSGA [35] and WW [36]. Table 4 shows the results obtained by the proposed algorithm and the four comparative heuristics for the 43 instances.…”

Section: Experiments and Comparisonmentioning

confidence: 99%

“…These results include the names of the instances, the sizes of the instances represented by n×m, the best known solutions (BKS) and the best solutions obtained by the proposed algorithm and the four comparative heuristics. Results of the comparative heuristics are from the original respective publications [21,[34][35][36].…”

Section: Experiments and Comparisonmentioning

confidence: 99%

“…However, due to the premature and local convergence of GA, its performance in dealing with complex JSSP is limited [ 21 ]. The particle swarm optimization (PSO) algorithm cannot effectively search the entire solution space, and may converge to a local optimal solution prematurely, and thus cannot achieve a good exploration- exploitation balance [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

Tian¹,

Liu²,

Zhang³

et al. 2020

PLoS ONE

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A DNA (DeoxyriboNucleic Acid) algorithm is proposed to solve the job shop scheduling problem. An encoding scheme for the problem is developed and DNA computing operations are proposed for the algorithm. After an initial solution is constructed, all possible solutions are generated. DNA computing operations are then used to find an optimal schedule. The DNA algorithm is proved to have an O(n2) complexity and the length of the final strand of the optimal schedule is within appropriate range. Experiment with 58 benchmark instances show that the proposed DNA algorithm outperforms other comparative heuristics.

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Section: Experiments and Comparisonmentioning

confidence: 99%

Section: Experiments and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

Tian¹,

Liu²,

Zhang³

et al. 2020

PLoS ONE

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“…Zhou [18] fuses the social spider optimization algorithm and the differential evolutionary based mutation operator for solving the JSSP. Peng et al [19] proposed a MAGATS algorithm that combines multi-agent GA and TS to solve JSSP. Pongchairerks [20] presented a two-level meta-heuristic method for solving JSSP, where the upper-level algorithm (UPLA) was a population-based algorithm that serves as an input-parameter controller of the lower-level algorithm (LOLA).…”

Section: Introductionmentioning

confidence: 99%

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

Tian

Liu

2021

Processes

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In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

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“…Improvement heuristics such as bee colony algorithm [48,140], tabu search [180], simulated annealing [225], and genetic algorithms [46,196] have been proposed to find "good enough" solutions for solving JSS problems [3,83,193,221] in a reasonable time. However, they are often not suitable for solving the dynamic JSS problems due to their lack of ability to react in time (i.e., face with rescheduling issue, which is time-consuming).…”

Section: Existing Approachesmentioning

confidence: 99%

Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling

Zhang¹

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Dynamic flexible job shop scheduling (DFJSS) has received widespread attention from academia and industry due to its reflection in real-world scheduling applications such as order picking in the warehouse and the manufacturing industry. It requires complex routing and sequencing decisions under unpredicted dynamic events. Genetic programming, as ahyper-heuristic approach (GPHH), has been successfully applied to evolve scheduling heuristics for DFJSS automatically due to its flexible representation. Although GPHH has achieved certain success in solving the DFJSS problems, there are still some limitations for applying GPHH to DFJSS, particularly in terms of its training efficiency, large search space, search mechanism, and multitask solving ability. The overall goal of this thesis is to develop effective GPHH algorithmsto evolve scheduling heuristics for DFJSS efficiently. Different machine learning techniques, i.e., surrogate, feature selection, specialised genetic operator, and multitask learning, are incorporated in this thesis to tackle the limitations. First, this thesis develops a novel multi-fidelity based surrogate-assisted GPHH for DFJSS to improve the training efficiency of GPHH. Specifically, multi-fidelity based surrogate models are first designed by simplifying the problem to be solved. Then, an effective collaboration mechanism with knowledge transfer is proposed for utilising the advantages of the multifidelity based surrogate models to solve the problem. The results show that the proposed algorithm can dramatically reduce the computational cost of GPHH without sacrificing the performance in all the test scenarios. With the same training time, the proposed algorithm can achieve significantly better performance than its counterparts in most scenarios while no worse in others. Second, this thesis designs a novel two-stage GPHH framework with feature selection to evolve scheduling heuristics for DFJSS automatically. Based on this framework, this thesis further proposes to evolve scheduling heuristics with only the selected features by eliminating the unselected features properly. Specifically, individual adaptation strategies are proposed to generate individuals with only the selected features by utilising the information of both the selected features and the investigated individuals during the feature selection process. The results show that the proposed algorithm can successfully achieve scheduling heuristics with fewer unique features and smaller sizes, which tends to be more interpretable. In addition, the proposed algorithm can evolve comparable scheduling heuristic with that obtained by the traditional GPHH within a much shorter training time. Third, this thesis proposes a novel recombinative mechanism to provide guidance for GPHH based on the importance of subtrees to realise effective and adaptive recombination for parents to produce offspring. Two measures are proposed to measure the importance of all the subtrees of an individual. The first one is based on the frequency of features, and the second is based on the correlation between the behaviour of subtrees and the whole tree (i.e., an individual). The importance information is utilised to decide the crossover points for the parents. The proposed recombinative guidance mechanism attempts to improve the quality of offspring by preserving the promising building-blocks of one parent and incorporating good building-blocks from the other. The results show that the proposed algorithm based on the correlation importance measure performs better than the proposed algorithm based on the feature frequency importance measure. In addition, the proposed algorithm based on the correlation importance measure between the behaviour of subtrees significantly also outperforms the state-of-the-art algorithms on most tested scenarios. Last, this thesis proposes a multitask GPHH approach and a surrogate-assisted multitask GPHH approach to solving multiple DFJSS tasks simultaneously. First, an effective hyper-heuristic multitask algorithm is proposed by adapting the traditional evolutionary multitask algorithms based on the characteristics of GPHH. Second, this thesis develops a novel surrogate-assisted multitask GPHH approach to solving multiple DFJSS tasks by sharing useful knowledge between different DFJSS scheduling tasks. Specifically, the surrogate-assisted multitask GPHH algorithm employs the phenotypic characterisation technique to measure the behaviours of scheduling rules to build a surrogate for each task accordingly. The built surrogates are not only used to improve the efficiency of solving each single DFJSS task but also utilised for knowledge sharing between multiple DFJSS tasks in multitask learning. The results show that the proposed algorithm can significantly improve the quality of scheduling heuristics for all the test scenarios. The results also observe that the proposed algorithms manage to solve multiple tasks collaboratively in terms of the evolved scheduling heuristics for different tasks in a multitask scenario.

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Research on multi-agent genetic algorithm based on tabu search for the job shop scheduling problem

Cited by 20 publications

References 27 publications

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

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

Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling

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