Toward automated algorithm configuration for distributed hybrid flow shop scheduling with multiprocessor tasks

Gholami, Hadi; Sun, Hongyang

doi:10.1016/j.knosys.2023.110309

Cited by 20 publications

(3 citation statements)

References 43 publications

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“…Local search is one of the most critical steps in solving the scheduling problem, which facilitates the optimization ability of evolutionary algorithms [55] , and the domain structure based on critical factory usually has stronger optimization ability. For the EADHFSP-ST problem, this paper designs three common critical factory-based domain structures and two problemspecific block-based search operators [16] .…”

Section: Local Search Operatormentioning

confidence: 99%

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Chen,

Luo,

Gong

et al. 2024

Complex Syst. Model. Simul.

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This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population coevolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.

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Section: Local Search Operatormentioning

confidence: 99%

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Chen,

Luo,

Gong

et al. 2024

Complex Syst. Model. Simul.

View full text Add to dashboard Cite

show abstract

“…Compared with HFSPs where a single factory is considered, research on distributed HFSP scheduling has increased recently since the multifactory manufacturing strategy to enhance a company's competitiveness was introduced to production systems [1,9,10,45,46].…”

Section: Literature Reviewmentioning

confidence: 99%

An Evaluation of Mathematical Programming and Lower-Bound Methods for Hybrid Flow Shop Problems With a Makespan Criterion

2023

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This paper considers the hybrid flow shop scheduling problem, where jobs are processed in m stages with the same route of the stage. Each stage has identical parallel machines for processing jobs. Some mathematical programming formulations and lower bound calculations have been proposed in the literature for such cases. Nevertheless, there is a lack of complete comparisons of these mathematical programming formulations and lower bounds in the hybrid flow shop literature. In this paper, we propose a new mixed integer programming model and two new lower bounds based on the bin-packing concept for the considered problem. To evaluate the proposed model, two sets of small and small-to-medium problems are used to compare our model with the existing models. Moreover, two propositions are discussed for lower bounds. The experimental results show that the proposed mixed integer programming model efficiently found optimal solutions because it needs a smaller number of binary variables and a smaller number of constraints, and the proposed lower bound can also serve as a strong indicator to evaluate the distances between the solutions obtained by heuristic algorithms and the optimal solution.INDEX TERMS hybrid flow shop, makespan, mixed integer programming, lower bound

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“…This variant aims to enhance the performance and efficiency of the PDDE algorithm in tackling the given problem. A distributed FFSP with multiprocessor jobs is addressed in [23]. The studied problem is formulated as a Markov Decision Process (MDP) and subsequently solved using a hybrid Q-learning-local search algorithm.…”

Section: Ffsp Recent Publicationsmentioning

confidence: 99%

Effective Two-Phase Heuristic and Lower Bounds for Multi-Stage Flexible Flow Shop Scheduling Problem with Unloading Times

Hidri

2023

Symmetry

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This paper addresses the flexible flow shop scheduling problem with unloading operations, which commonly occurs in modern manufacturing processes like sand casting. Although only a few related works have been proposed in the literature, the significance of this problem motivates the need for efficient algorithms and the exploration of new properties. One interesting property established is the symmetry of the problem, where scheduling from the first stage to the last or vice versa yields the same optimal solution. This property enhances solution quality. Considering the problem’s theoretical complexity as strongly NP-Hard, approximate solutions are preferable, especially for medium and large-scale instances. To address this, a new two-phase heuristic is proposed, consisting of a constructive phase and an improvement phase. This heuristic builds upon an existing efficient heuristic for the parallel machine-scheduling problem and extends it to incorporate unloading times efficiently. The selection of the two-phase heuristic is justified by its ability to generate high-quality schedules at each stage. Moreover, new efficient lower bounds based on estimating minimum idle time in each stage are presented, utilizing the polynomial parallel machine-scheduling problem with flow time minimization in the previous stage. These lower bounds contribute to assessing the performance of the two-phase heuristic over the relative gap performance measure. Extensive experiments are conducted on benchmark test problems, demonstrating the effectiveness of the proposed algorithms. The results indicate an average computation time of 9.92 s and a mean relative gap of only 2.80% for several jobs up to 200 and several stages up to 10.

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Toward automated algorithm configuration for distributed hybrid flow shop scheduling with multiprocessor tasks

Cited by 20 publications

References 43 publications

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

An Evaluation of Mathematical Programming and Lower-Bound Methods for Hybrid Flow Shop Problems With a Makespan Criterion

Effective Two-Phase Heuristic and Lower Bounds for Multi-Stage Flexible Flow Shop Scheduling Problem with Unloading Times

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