Solving flow-shop scheduling problem with a reinforcement learning algorithm that generalizes the value function with neural network

Ren, Jianfeng; Ye, Chunming; Yang, Feng

doi:10.1016/j.aej.2021.01.030

Cited by 28 publications

(13 citation statements)

References 22 publications

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“…Despite this, and considering the different contexts, there are other studies such as [82], where the aim was to improve delivery and payment dates, for which a biased random heuristic is applied, and then a metaheuristic with a variable neighborhood was used to carry it out finally a semi-heuristic through the incorporation of Monte Carlo simulations, demonstrating that the results were conclusive, exceeding a 40% improvement in the case of application and tests. In general, most of the applied techniques and algorithms manage to improve the initial makespan, highlighting studies such as that of [83,84] where techniques such as neural networks, multi-objective evolutionary algorithms with heuristic decoding, and graph theory were applied. In some cases, there are more discrete results, such as those of [85], in which artificial neural networks were used, compared to those obtained in our study.…”

Section: Results and Analysismentioning

confidence: 99%

Flow-Shop Scheduling Problem Applied to the Planning of Repair and Maintenance of Electromedical Equipment in the Hospital Industry

et al. 2022

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In the literature, several approaches have been proposed to integrate and optimize product supply and construction processes associated with demand management. However, in Industry 4.0, there needs to be more studies related to applying techniques that directly affect the programming and reprogramming process that integrates the industries at the operational level. This document proposes a flow-shop scheduling procedure to address the problem of planning the repair of medical equipment in public hospitals whose main objective is to eliminate downtime and minimize total production time. The research stems from the practical problem of responding to clinical users who make use of critical equipment, such as mechanical respirators, due to COVID-19, and the limited quantity of this equipment, which makes it necessary to have repair planning processes that seek to keep the equipment in operation for the most extended amount of time. The novelty of this study is that it was applied to a critical and real problem in the industry with a high economic and social impact, which has not been explored previously. The results show improvements in the overall planning and execution of electro-medical equipment repair. Several improvements to the applied methods were identified as future work, such as the need to consider work interruptions and psychosocial effects on workers due to the stricter planning of execution times.

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Section: Results and Analysismentioning

confidence: 99%

Flow-Shop Scheduling Problem Applied to the Planning of Repair and Maintenance of Electromedical Equipment in the Hospital Industry

et al. 2022

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“…This involves distinguishing between soft objectives and hard constraints. However, previous research predominantly focused on two-agent scheduling [10][11][12][13]. In today's integrated virtual and real world, where the number of stakeholders continues to grow and each has distinct positions, traditional two-agent scheduling may no longer adequately represent the vast array of objectives and constraints.…”

Section: Related Workmentioning

confidence: 99%

A Lower Bound for Minimizing Waiting Time in Coexisting Virtual and Physical Worlds

Chen,

Wang

2024

IEEE Access

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To balance customer satisfaction across virtual and real-world interactions, we focus on enhancing service for dine-in customers at restaurants that typically prioritize online orders, such as those on Uber Eats. Utilizing three-agent scheduling strategies that adhere to each agent's specific requirements-by utilizing whether they are hard constraints or soft objectives-we effectively manage various types of orders, including immediate individual online orders, group reservations, and oral requests from dine-in customers. This approach significantly reduces waiting times and improves overall customer satisfaction. This study introduces a branch-and-bound algorithm with a tight lower bound based on preemption. Unique in its design, the lower bound aims to prioritize agents A and B and simultaneously reduce the total waiting time for agent C, representing dine-in customers. Computational experiments reveal that this lower bound can converge quickly. Compared with existing two-agent scheduling strategies, this lower bound can also be extended to other industries that require three or more different constraints or objectives, such as in the film and television industry where real actors, virtual avatars, and expensive 3D studios have distinct requirements. Multi-agent scheduling, Branch-and-bound algorithm, Lower bound, Waiting time INDEX TERMS

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“…Similarly, in [34], the authors presented an implementation of RL for estimating the value function of Neural Networks (NN) that are used then to map jobs to machines. However, the concept is presented to deal with Pure flow shop scheduling problems that are much simpler than the HFS scheduling problems.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning Techniques For Solving Hybrid Flow Shop Scheduling Problems: Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C)

Nahhas¹,

Kharitonov²,

Turowski³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Well-studied scheduling practices are fundamental for the successful support of core business processes in any manufacturing environment. Particularly, the Hybrid Flow Shop (HFS) scheduling problems are present in many manufacturing environments. The current advances in the field of Deep Reinforcement Learning (DRL) attracted the attention of both practitioners and academics to investigate their adoption beyond synthetic game-like applications. Therefore, we present an approach that is based on DRL techniques in conjunction with a discrete event simulation model to solve a real-world four-stage HFS scheduling problem. The main narrative behind the presented concepts is to expose a DRL agent to a gamelike environment using an indirect encoding. Two types of DRL techniques namely, Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C), are evaluated for solving problems of different complexity. The computational results suggest that the DRL agents successfully learn appropriate policies for solving the investigated problem. In addition, the investigation shows that the agent can adjust their policies when we expose them to a different problem. We further evaluate the approach to solving problem instances published in the literature to establish a comparison.

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Solving flow-shop scheduling problem with a reinforcement learning algorithm that generalizes the value function with neural network

Cited by 28 publications

References 22 publications

Flow-Shop Scheduling Problem Applied to the Planning of Repair and Maintenance of Electromedical Equipment in the Hospital Industry

Flow-Shop Scheduling Problem Applied to the Planning of Repair and Maintenance of Electromedical Equipment in the Hospital Industry

A Lower Bound for Minimizing Waiting Time in Coexisting Virtual and Physical Worlds

Deep Reinforcement Learning Techniques For Solving Hybrid Flow Shop Scheduling Problems: Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C)

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