Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Zhao, Ziyan; Liu, Shixin; Zhou, MengChu; Abusorrah, Abdullah

doi:10.1109/jas.2020.1003539

Cited by 161 publications

(27 citation statements)

References 64 publications

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“…In [45], a scoring and dynamic hierarchybased NSGA-II is designed for workflow scheduling, where a scoring criterion is considered as the DM preference. Besides, [46]- [51] also introduce some DM preference-based MOEAs. However, these methods are developed for specific problems and not suitable for train speed trajectory optimization problem elaborated earlier.…”

Section: Romentioning

confidence: 99%

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Xing

Zhang

et al. 2023

IEEE Trans. Transp. Electrific.

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Section: Romentioning

confidence: 99%

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Xing

Zhang

et al. 2023

IEEE Trans. Transp. Electrific.

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“…Meta-heuristic algorithm has excellent global search ability, strong adaptability and robustness. It has been successfully applied to many fields, such as optimization computing [23][24][25][26][27][28], artificial intelligence [29,30], pattern recognition [31,32], image processing [33,34], constrained optimization [35][36][37], engineering [38,39] and biology [40,41].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy

Tang

Tao

Wang

et al. 2022

Int J Comput Intell Syst

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Wind driven optimization (WDO) is a meta-heuristic algorithm based on swarm intelligence. The original selection method makes it easy to converge prematurely and trap in local optima. Maintaining population diversity can solve this problem well. Therefore, we introduce a new fitness-distance balance-based selection strategy to replace the original selection method, and add chaotic local search with selecting chaotic map based on memory to further improve the search performance of the algorithm. A chaotic wind driven optimization with fitness-distance balance strategy is proposed, called CFDBWDO. In the experimental section, we find the optimal parameter settings for the proposed algorithm. To verify the effect of the algorithm, we conduct comparative experiments on the CEC 2017 benchmark functions. The experimental results denote that the proposed algorithm has superior performance. Compared with WDO, CFDBWDO can gradually converge in function optimization. We further verify the practicality of the proposed algorithm with six real-world optimization problems, and the obtained results are all better than other algorithms.

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“…Zhao et al. [13] applied the memetic algorithm that integrated a population‐based non‐dominated sorting genetic algorithm II and two single‐solution‐based improvement methods to solve a bi‐objective serial‐batch group scheduling problem considering the constraints of sequence‐dependent setup time, release time, and due time. Also, Zhao [14] summarised iterated greedy algorithm (IGA) variants and hybrid algorithms with IGA integrated for solving FSPs according to their scheduling scenarios, objective functions, and constraints.…”

Section: Introductionmentioning

confidence: 99%

A deep reinforcement learning based approach for dynamic distributed blocking flowshop scheduling with job insertions

Sun

Vogel‐Heuser

et al. 2022

IET Collab Intel Manufact

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The distributed blocking flowshop scheduling problem (DBFSP) with new job insertions is studied. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. A deep reinforcement learning (DRL) algorithm is proposed to optimise the job selection model, and local modifications are made on the basis of the original scheduling plan when new jobs arrive. The objective is to minimise the total completion time deviation of all products so that all jobs can be finished on time to reduce the cost of storage. First, according to the definitions of the dynamic DBFSP problem, a DRL framework based on multi-agent deep deterministic policy gradient (MADDPG) is proposed. In this framework, a full schedule is generated by the variable neighbourhood descent algorithm before a dynamic event occurs. Meanwhile, all newly added jobs are reordered before the agents make decisions to select the one that needs to be scheduled most urgently. This study defines the observations, actions and reward calculation methods and applies centralised training and distributed execution in MADDPG. Finally, a comprehensive computational experiment is carried out to compare the proposed method with the closely related and well-performing methods. The results indicate that the proposed method can solve the dynamic DBFSP effectively and efficiently. K E Y W O R D S deep reinforcement learning, distributed blocking flowshop scheduling problem, dynamic scheduling, job insertions, multi-agent deep deterministic policy gradientThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

Cited by 161 publications

References 64 publications

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Chaotic Wind Driven Optimization with Fitness Distance Balance Strategy

A deep reinforcement learning based approach for dynamic distributed blocking flowshop scheduling with job insertions

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