Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach |
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Authors: | Derya Eren Akyol G Mirac Bayhan |
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Affiliation: | (1) Department of Industrial Engineering, Dokuz Eylul University, 35100 Bornova-Izmir, Turkey |
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Abstract: | This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates
on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural
networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient
neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which
is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient
descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise
linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to
reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in
using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation
experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network
yields an optimal solution which makes it attractive for applications of larger sized problems. |
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Keywords: | Scheduling Sequence-dependent setups Earliness and tardiness Neural networks |
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