A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem

Lin, Jian

doi:10.1080/00207543.2015.1094584

Cited by 30 publications

(6 citation statements)

References 46 publications

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“…Equation (4) defines the completion time of a job in the system. Equation (5) gives the completion time of a job on a machine. Equation (6) ensures that a job can be processed by the first machine only after the job arrives at the system.…”

Section: Mathematical Modelmentioning

confidence: 99%

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Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Yang

Wang

2021

Sensors

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Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.

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Section: Mathematical Modelmentioning

confidence: 99%

“…To solve the NP-hard problem, many hybrid meta-heuristics have been proposed [ 2 , 3 ]. For the PFSP, its objective is to find a good job sequence to minimize makespan [ 4 , 5 , 6 ], flow time [ 7 , 8 , 9 , 10 ], tardiness [ 11 , 12 , 13 , 14 ], multiple objective [ 15 , 16 , 17 , 18 , 19 , 20 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Yang

Wang

2021

Sensors

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“…Exact optimization algorithms (e.g., [2] , [3] , [4] , [5] ) often have very large computational times that are infeasible on even moderate-size problem instances. As for moderate- and large-size instances optimal solutions are rarely needed in practice, heuristic approximation algorithms, including constructive heuristics (e.g., [6] , [7] , [8] ) and metaheuristic evolutionary algorithms (e.g., [9] , [10] , [11] , [12] , [13] , [14] , [15] ), are more feasible to achieve a trade-off between optimality and computational costs. However, the solution fitness of constructive heuristics is often low for even moderate-size instances.…”

Section: Introductionmentioning

confidence: 99%

Real-time neural network scheduling of emergency medical mask production during COVID-19

Liao

Karataş

et al. 2020

Applied Soft Computing

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During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that must be processed within a short response time. It is of critical importance for the manufacturer to schedule and reschedule mask production tasks as efficiently as possible. However, when the number of tasks is large, most existing scheduling algorithms require very long computational time and, therefore, cannot meet the needs of emergency response. In this paper, we propose an end-to-end neural network, which takes a sequence of production tasks as inputs and produces a schedule of tasks in a real-time manner. The network is trained by reinforcement learning using the negative total tardiness as the reward signal. We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China. Computational results show that the neural network scheduler can solve problem instances with hundreds of tasks within seconds. The objective function value obtained by the neural network scheduler is significantly better than those of existing constructive heuristics, and is close to those of the state-of-the-art metaheuristics whose computational time is unaffordable in practice.

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“…Exact optimization algorithms (e.g., [2,3,4,5]) have very large computation times that are infeasible on even moderate-size problem instances. As for moderate-and large-size instances optimal solutions are rarely needed in practice, heuristic approximation algorithms, in particular evolutionary algorithms (e.g., [6,7,8,9,10,11,12]), are more feasible to achieve a trade-off between optimality and computational costs. However, the number of repeated generations and objective function evaluations for solving large-size instances still takes a relatively long time and, therefore, cannot satisfy the requirement of real-time scheduling.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Neural Network Scheduling of Emergency Medical Mask Production during COVID-19

Liao

Karataş

et al. 2020

Preprint

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During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that are beyond its capability. Therefore, it is of critical importance for the manufacturer to schedule mask production tasks as efficiently as possible. However, existing scheduling methods typically require a considerable amount of computational resources and, therefore, cannot effectively cope with the surge of orders. In this paper, we propose an end-to-end neural network for scheduling real-time production tasks. The neural network takes a sequence of production tasks as inputs to predict a distribution over different schedules, employs reinforcement learning to optimize network parameters using the negative total tardiness as the reward signal, and finally produces a high-quality solution to the scheduling problem. We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China. Computational results show that the neural network scheduler can solve problem instances with hundreds of tasks within seconds. The objective function value (i.e., the total weighted tardiness) produced by the neural network scheduler is significantly better than those of existing constructive

show abstract

A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem

Cited by 30 publications

References 46 publications

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Real-time neural network scheduling of emergency medical mask production during COVID-19

Real-Time Neural Network Scheduling of Emergency Medical Mask Production during COVID-19

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