A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling

Li, Rui; Gong, Wenyin; Lu, Chao

doi:10.1016/j.eswa.2022.117380

Cited by 86 publications

(16 citation statements)

References 54 publications

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“…In order to better illustrate the performance of the proposed ESPEA, the performances of other excellent multi-objective algorithms are compared with that of the proposed algorithm. These comparison algorithms include ROMA/D [24], MOEA/D [25], SPEA2 [19], NSGAII [26] and NSGAIII [27], all of which have been proven to have an excellent performance. In order to fairly compare the solving ability of different algorithms, all algorithms used the same cooperative initialization strategy and genetic operators and all algorithms were run 10 independent times.…”

Section: Performance Analysis Via An Algorithm Comparisonmentioning

confidence: 99%

Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage

Wang,

Xu,

et al. 2024

Applied Sciences

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Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.

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Section: Performance Analysis Via An Algorithm Comparisonmentioning

confidence: 99%

Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage

Wang,

Xu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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“…This algorithm integrates a new multi-objective optimization algorithm model for environment selection. Li et al [35] proposed a fully active scheduling decoding to reduce energy consumption in the flexible job scheduling problem.…”

Section: Related Workmentioning

confidence: 99%

“…However, operator selection is critical to MA performance. Existing MA frameworks always cycle through global search followed by local search, and random selection [33] , polling selection [35] , and confidence-based selection [36,37] are used for operator selection. All of these studies inherently rely on confidence levels.…”

Section: Introductionmentioning

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.

Self Cite

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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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“…The two scheduling algorithms named as Threshold based Task scheduling algorithm (TBTS) and Service level agreement-based Load Balancing (SLA-LB) algorithm [30], aiming at makespan, gain cost, and resource utilization, and supporting different configurations of VMs for task scheduling while also dynamically scheduling according to user requirements. The MOEA/D based on RL (RMORA/D) algorithm [31] uses the classical MOEA/D algorithm combined with the Q-learning algorithm in RL for adaptive selection of domain parameters T with the goal of makespan and machine load, increasing the diversity of populations.…”

Section: Related Workmentioning

confidence: 99%

An adaptive reference vector strategy with Q-learning for many-objective workflow scheduling problem in the cloud computing environment

Dong,

Fan,

Wang

et al. 2024

Preprint

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Cloud computing environment is widely used in various fields, and the scientific workflow scheduling problem in this environment is a many-objective optimization problem and has attracted much attention. Aiming at meeting the different demands of multiple users, this paper proposes an adaptive many-objective algorithm (AD\_CLIA) based on cascade clustering and reference point incremental learning algorithm (CLIA). First, this paper constructs a workflow scheduling model with four objectives: completion time (makespan), cost load, and average resource utilization (AU). Then, for improving the convergence and diversity of CLIA, a reinforcement learning method for adaptively selecting effective reference vectors is proposed. And at the same time, a double-faced mirror strategy is constructed to deal with the problem of uneven distribution of the optimal solution set. It has shown advantages in both low-dimensional DTLZ test problems and high-dimensional WFG and MaF test problems. Finally, the proposed algorithm is applied to four famous real workflow problems and the results are satisfactory.

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A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling

Cited by 86 publications

References 54 publications

Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage

Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage

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

An adaptive reference vector strategy with Q-learning for many-objective workflow scheduling problem in the cloud computing environment

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