Multi-Objective brain storm optimization for integrated scheduling of distributed flow shop and distribution with maximal processing quality and minimal total weighted earliness and tardiness

Hou, Yuhan; Wang, Hongfeng; Fu, Yaping; Gao, Kaizhou; Zhang, Hui

doi:10.1016/j.cie.2023.109217

Cited by 16 publications

(2 citation statements)

References 43 publications

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“…In MBSO, the generation strategy is selected at Moreover, this work applies statistical tests to verify whether the performance difference between MQBSO and its peers is significantly different. The Friedman and Wilcoxon signed rank tests [53][54][55] are carried out on each instance of the IGD-and HV-metrics at α = 0.05 level of significance. The average rank values of MQBSO, MOWSA, MOBSO, NSGA-II, and MOEA/D with respect to the IGD-metric are 1.3333, 2.5417, 2.7083, 3.7917, and 4.6250, respectively, and those of the five algorithms regarding the HV-metric are 1.5000, 2.1667, 3.1667, 3.7500, and 4.4167, respectively.…”

Section: Effectiveness Of the Q-learning Process In Mqbsomentioning

confidence: 99%

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

Zhang,

Xu,

Qiao

2023

Mathematics

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In recent years, integrated production and distribution scheduling (IPDS) has become an important subject in supply chain management. However, IPDS considering distributed manufacturing environments is rarely researched. Moreover, reinforcement learning is seldom combined with metaheuristics to deal with IPDS problems. In this work, an integrated distributed flow shop and distribution scheduling problem is studied, and a mathematical model is provided. Owing to the problem’s NP-hard nature, a multi-objective Q-learning-based brain storm optimization is designed to minimize makespan and total weighted earliness and tardiness. In the presented approach, a double-string representation method is utilized, and a dynamic clustering method is developed in the clustering phase. In the generating phase, a global search strategy, a local search strategy, and a simulated annealing strategy are introduced. A Q-learning process is performed to dynamically choose the generation strategy. It consists of four actions defined as the combinations of these strategies, four states described by convergence and uniformity metrics, a reward function, and an improved ε-greedy method. In the selecting phase, a newly defined selection method is adopted. To assess the effectiveness of the proposed approach, a comparison pool consisting of four prevalent metaheuristics and a CPLEX optimizer is applied to conduct numerical experiments and statistical tests. The results suggest that the designed approach outperforms its competitors in acquiring promising solutions when handling the considered problem.

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Section: Effectiveness Of the Q-learning Process In Mqbsomentioning

confidence: 99%

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

Zhang,

Xu,

Qiao

2023

Mathematics

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“…Additionally, search engineering is also important for solving MMOPs as it can greatly affect the population diversity throughout the evolution. Like other meta-heuristics algorithms, brain storm optimization (BSO) [15,16] has good global exploration ability and is suitable for solving different types of MOPs. Moreover, existing studies [17][18][19][20][21] show that the BSO is a competitive method to solve multimodal optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization

et al. 2023

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For multimodal multi-objective optimization problems (MMOPs), there are multiple equivalent Pareto optimal solutions in the decision space that are corresponding to the same objective value. Therefore, the main tasks of multimodal multi-objective optimization (MMO) are to find a high-quality PF approximation in the objective space and maintain the population diversity in the decision space. To achieve the above objectives, this article proposes a zoning search-based multimodal multi-objective brain storm optimization algorithm (ZS-MMBSO). At first, the search space segmentation method is employed to divide the search space into some sub-regions. Moreover, a novel individual generation strategy is incorporated into the multimodal multi-objective brain storm optimization algorithm, which can improve the search performance of the search engineering. The proposed algorithm is compared with five famous multimodal multi-objective evolutionary algorithms (MMOEAs) on IEEE CEC2019 MMOPs benchmark test suite. Experimental results indicate that the overall performance of the ZS-MMBSO is the best among all competitors.

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Solving multimodal optimization problems by a knowledge-driven brain storm optimization algorithm

Cheng,

Wang,

Zhang

et al. 2024

Applied Soft Computing

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Multi-Objective brain storm optimization for integrated scheduling of distributed flow shop and distribution with maximal processing quality and minimal total weighted earliness and tardiness

Cited by 16 publications

References 43 publications

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

Multi-Objective Q-Learning-Based Brain Storm Optimization for Integrated Distributed Flow Shop and Distribution Scheduling Problems

A Zoning Search-Based Multimodal Multi-Objective Brain Storm Optimization Algorithm for Multimodal Multi-Objective Optimization

Solving multimodal optimization problems by a knowledge-driven brain storm optimization algorithm

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