Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling

Zhang, Fangfang; Mei, Yi; Nguyen, Su; Zhang, Mengjie

doi:10.1109/tevc.2021.3056143

Cited by 63 publications

(17 citation statements)

References 67 publications

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“…You can see in the table below that the computed Cronbach alpha ranges from 0.72 to 0.83, which is an excellent outcome (see Table 2). Finding the direction and magnitude of a linear relationship between two variables is done via correlation analysis (Zhang et al, 2021). The strength and relevance of their link are revealed by the degree of correlation.…”

Section: Resultsmentioning

confidence: 99%

Digital Marketing's Influence on Consumer Purchasing Decision: A Case Study in Iraq

Sukaini

2022

JAMRSSS

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Examining the effects of digital marketing on customer purchase decisions was the main goal of this study.Specific objectives guided the study, including the evaluation of digital marketing platforms in Iraq with the ability to impact purchasing decisions and the identification of product categories that customers purchased on digital media platforms. Although the corporation is not completely responsible for the purchase choice process of consumers, It is a comprehensive procedure that takes into account psychological aspects, social influence, word-of-mouth, cultural values, and financial level. A total of 250 questionnaires were delivered, with the exclusion of the unfinished surveys, and 220 usable samples were obtained, yielding an overall response rate of 88%. Multiple regressions, reliability tests, correlation tests, and descriptive analysis were all employed in this study. The findings of this study also demonstrated the major effect of digital marketing on customer purchase decisions, including social media marketing and mobile marketing. Hypothesis testing, however, showed that various well-known digital marketing platforms in Iraq had an impact on students' behavior. Through digital media platforms, students in Iraq make purchases across a wide range of product categories, and digital marketing has an impact. In the end, firms need to implement plans to harness the digital world and technology as well as raise brand recognition through online media in order to compete in today's business climate.

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Section: Resultsmentioning

confidence: 99%

Digital Marketing's Influence on Consumer Purchasing Decision: A Case Study in Iraq

Sukaini

2022

JAMRSSS

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“…Experimental results show that this method can effectively improve the quality of evolutionary rules. Zhang et al [27] proposed a recombination mechanism based on correlation coefficients, which provided guidance for super-narrow GP to generate progeny by efficient adaptive recombination in DFJSP. This helps GPHH find better scheduling heuristic by improving the generation quality and analyzing the performance of the proposed algorithm in terms of evolution rules, convergence speed, and training time efficiency.…”

Section: Dynamic Job Shop Scheduling Based On Conventional Methodsmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

Pu,

Li,

Rahimifard

2024

Sustainability

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In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks and deep reinforcement learning to complete the task of job shop scheduling. A distributed multi-agent scheduling architecture (DMASA) is constructed to maximize global rewards, modeling the intelligent manufacturing job shop scheduling problem as a sequential decision problem represented by graphs and using a Graph Embedding–Heterogeneous Graph Neural Network (GE-HetGNN) to encode state nodes and map them to the optimal scheduling strategy, including machine matching and process selection strategies. Finally, an actor–critic architecture-based multi-agent proximal policy optimization algorithm is employed to train the network and optimize the decision-making process. Experimental results demonstrate that the proposed framework exhibits generalizability, outperforms commonly used scheduling rules and RL-based scheduling methods on benchmarks, shows better stability than single-agent scheduling architectures, and breaks through the instance-size constraint, making it suitable for large-scale problems. We verified the feasibility of our proposed method in a specific experimental environment. The experimental results demonstrate that our research can achieve formal modeling and mapping with specific physical processing workshops, which aligns more closely with real-world green scheduling issues and makes it easier for subsequent researchers to integrate algorithms with actual environments.

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“…The flexible representations and search mechanisms make GP a suitable approach to designing scheduling heuristics, since the structure and size of optimal heuristics are not known in advance. A general GP based hyper-heuristic (GPHH) framework was presented in [27], and it has been successfully used in different problems such as packing [28,107], timetabling [8,198], arc routing [5,227] and JSS [106,169,170,172,241]. In the last decade, GPHH works as heuristic generation approach, has been the dominating technique to evolve scheduling heuristics for DFJSS automatically.…”

Section: Existing Approachesmentioning

confidence: 99%

Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling

Zhang¹

2021

Preprint

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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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Correlation Coefficient-Based Recombinative Guidance for Genetic Programming Hyperheuristics in Dynamic Flexible Job Shop Scheduling

Cited by 63 publications

References 67 publications

Digital Marketing's Influence on Consumer Purchasing Decision: A Case Study in Iraq

Digital Marketing's Influence on Consumer Purchasing Decision: A Case Study in Iraq

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling

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