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Neural Network-Based Evaluation for the Multi-Objective Optimization in Flow Shops with Random Failures

Published: 21 December 2023 Publication History

Abstract

Nowadays, managers in manufacturing plants are faced with increasingly complex real-world scenarios. It requires minimizing manufacturing lead time while addressing the instability caused by random machine failures. Moreover, saving on energy cost under a time-of-use electricity tariff is crucial to enhance the price competitiveness of products. This study takes a flow shop with consideration of random machine failures as the object. We propose a multi-objective mathematical model with the objective of makespan, system robustness, and energy cost. For an uncertain manufacturing environment, a multi-task neural network is designed to evaluate the feasible solutions, which is trained by the data obtained from Monte Carlo simulation. The numerical experiments validate the effectiveness and generalization of the designed neural network.

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  1. Neural Network-Based Evaluation for the Multi-Objective Optimization in Flow Shops with Random Failures

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    CSAE '23: Proceedings of the 7th International Conference on Computer Science and Application Engineering
    October 2023
    358 pages
    ISBN:9798400700590
    DOI:10.1145/3627915
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 21 December 2023

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    Author Tags

    1. Equipment maintenance
    2. Flowshop
    3. Multi-objective optimization
    4. Neural network

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    Overall Acceptance Rate 368 of 770 submissions, 48%

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