A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem

Zhao, Fuqing; Xu, Zesong; Zhu, Ningning; Xu, Tianpeng; Jonrinaldi,

doi:10.1109/tii.2022.3192881

Cited by 32 publications

(9 citation statements)

References 38 publications

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“…Li et al [23] adopted the idea of evaluating the quantity of population, combining Qlearning algorithm with multi-objective evolutionary algorithm based on decomposition (MOEA/D) to modify the number of neighborhood solutions dynamically. In order to address the FJSP with type-2 fuzzy processing time efficiently, an improved artificial immune system algorithm was presented by Li et al [24] Zhao et al [25] proposed a population-based iterated greedy algorithm to address distributed assembly nowait flow shop scheduling problem. However, many uncertainties in production mean that the current FJSP cannot meet the demand of the modern market.…”

Section: Comentioning

confidence: 99%

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

Deng,

Zhu,

et al. 2024

Complex Syst. Model. Simul.

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There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling, but most scholars neglect the connection between them, which means the purpose of both models is to simulate a more realistic factory environment. From this perspective, the solutions can be more precise and practical if both issues are considered simultaneously. Therefore, the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper, which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time. Apart from that, many other contributions can be stated as follows. A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm (RB 2 EA) is proposed, which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population. A local enhancement method which combimes multiple local search stratgies is presented. An interaction mechanism is designed to promote the convergence of the bi-population. Extensive experiments are designed to evaluate the efficacy of RB 2 EA, and the conclusion can be drew that RB 2 EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs (EFFJSPD) efficiently.

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Section: Comentioning

confidence: 99%

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

Deng,

Zhu,

et al. 2024

Complex Syst. Model. Simul.

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“…JSP has several variants that are influenced by the characteristics of enterprises due to the nature of industries. These variants include distributed JSP that requires multiple cooperation [3] , flexible JSP that considers the same type of processing machines [4] , flow shop scheduling problem that involves mass production of the same type [5] , and fuzzy JSP that deals with dynamic environments [6] . Numerous real-world application scenarios have contributed to the advancement and development of JSPs, leading to the development of several effective scheduling optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

Li,

Yan,

Huang

2024

Tsinghua Sci. Technol.

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With the advancement of the manufacturing industry, the investigation of the shop floor scheduling problem has gained increasing importance. The Job shop Scheduling Problem (JSP), as a fundamental scheduling problem, holds considerable theoretical research value. However, finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP. A co-operative-guided ant colony optimization algorithm with knowledge learning (namely KLCACO) is proposed to address this difficulty. This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge. A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions. A pheromone guidance mechanism, which is based on a collaborative machine strategy, is used to simplify information learning and the problem space by collaborating with different machine processing orders. Additionally, the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution, expanding the search space of the algorithm and accelerating its convergence. The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets, comprising 48 benchmark test cases in total. The effectiveness of the proposed algorithm in addressing JSPs is validated, demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

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“…The welding process plays a crucial role in modern manufacturing, spanning various industries such as shipbuilding, aerospace, construction machinery, automotive, and others [1][2][3]. The efficiency of welding operations significantly impacts overall production timelines, with welded structures constituting over 50% of machinery parts [4,5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

Zhang,

Li,

et al. 2024

Complex Intell. Syst.

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This research is focused on addressing the energy-aware distributed heterogeneous welding shop scheduling (EADHWS) problem. Our primary objectives are to minimize the maximum finish time and total energy consumption. To accomplish this, we introduce a learning-based cooperative and competitive multi-objective optimization method, which we refer to as LCCMO. We begin by presenting a multi-rule cooperative initialization approach to create a population that combines strong convergence and diversity. This diverse population forms the foundation for our optimization process. Next, we develop a multi-level cooperative global search strategy that explores effective genes within solutions from different angles and sub-problems. This approach enhances our search for optimal solutions. Moreover, we design a competition and cooperation strategy for different populations to expedite convergence. This strategy encourages the exchange of information and ideas among diverse populations, thereby accelerating our progress. We also introduce a multi-operator cooperative local search technique, which investigates elite solutions from various directions, leading to improved convergence and diversity. In addition, we integrate Q-learning into our competitive swarm optimizer to explore different regions of the objective space, enhancing the diversity of the elite archive. Q-learning guides the selection of operators within the small-size population, contributing to more efficient optimization. To evaluate the effectiveness of LCCMO, we conduct numerical experiments on 20 instances. The experimental results unequivocally demonstrate that LCCMO outperforms six state-of-the-art algorithms. This underscores the potential of our learning and knowledge-driven evolutionary framework in enhancing performance and autonomy when it comes to solving EADHWS.

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A Population-Based Iterated Greedy Algorithm for Distributed Assembly No-Wait Flow-Shop Scheduling Problem

Cited by 32 publications

References 38 publications

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

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