An artificial neural network based heuristic for flow shop scheduling problems

Ramanan, T. Radha; Sridharan, R.; Shashikant, Kulkarni Sarang; Haq, A. Noorul

doi:10.1007/s10845-009-0287-5

Cited by 32 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve the flow shop scheduling problem, Ramanan et al. [21] used a neural network trained with optimal solutions of known instances to produce quality solutions for new instances, which are then given as the initial solutions to improve other heuristics such as GA. Such methods combining machine learning and metaheuristics are still time-consuming on large problems.…”

Section: Related Workmentioning

confidence: 99%

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

Liao

Karataş

et al. 2020

Applied Soft Computing

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Liao

Karataş

et al. 2020

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

“…ANN is one among the popular CI method based on the principle of neural networks found in nervous system of living organisms [50,51]. ANN has also been applied successfully for machining problems in engineering [52,53].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Process characterisation of 3D-printed FDM components using improved evolutionary computational approach

Vijayaraghavan

Garg

Lam

et al. 2014

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Fused deposition modelling (FDM) is an additive manufacturing technique deployed to fabricate the functional components leading to shorter product development times with less human intervention. Typical characteristics such as surface roughness, mechanical strength and dimensional accuracy are found to influence the wear strength of FDM fabricated components. It would be useful to determine an explicit numerical model to describe the correlation between various output process parameters and input parameters. In this paper, we have proposed an improved approach of multi-gene genetic programming (Im-MGGP) to formulate the functional relationship between wear strength and input process variables of the FDM process. It was found that the improved approach performs better than MGGP, SVR and ANN models and is able to generalise wear strength of the FDM prototype satisfactorily. Further, sensitivity and parametric analysis is conducted to study the influence of each input variable on the wear strength of the FDM fabricated components. It was found that the input parameter, air gap, has the maximum influence on the wear strength of the FDM fabricated component.

show abstract

“…Tests with adjacent pairwise interchanges gave some indication that the solutions proposed by the neural network respond to local searches better than solutions created by the NEH algorithm. The use of neural networks to support meta-heuristics has also been investigated in [15] and [16]. In both studies, initial solutions provided by neural networks led to improved performance in genetic algorithms and some other heuristics.…”

Section: Literature Reviewmentioning

confidence: 99%

An investigation of initial solutions on the performance of an iterated local search algorithm for the permutation flowshop

El-Bouri

2012

2012 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

This paper examines the effect of initial solutions on the performance of an iterated local search (ILS) algorithm for the permutation flowshop problem with the objective of minimizing total flowtime. An ILS algorithm is applied to a set of test problems, and in each separate trial the algorithm is started from an initial solution generated by one of six different methods. Experimental results indicate that initial solutions generated by a neural network are more effective in promoting the performance of the ILS algorithm towards better solutions. A modified version of the ILS algorithm, in which an initially restricted neighborhood search is gradually expanded with each iteration, is also proposed and tested. The results from this modified ILS compare very favorably with published results from a traditional ILS approach.

show abstract

An artificial neural network based heuristic for flow shop scheduling problems

Cited by 32 publications

References 32 publications

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

Process characterisation of 3D-printed FDM components using improved evolutionary computational approach

An investigation of initial solutions on the performance of an iterated local search algorithm for the permutation flowshop

Contact Info

Product

Resources

About