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AI-Based Discrete-Event Simulations for Manufacturing Schedule Optimization

Published: 25 November 2020 Publication History

Abstract

Optimization of manufacturing schedules is of great theoretical and practical significance, especially when high-tech products are produced in large manufacturing networks with global supply chains. In this paper, a novel approach is described how discrete-event simulations shall be enhanced by Artificial Intelligence (AI) to optimize manufacturing schedules for complex job shop manufacturing networks in the high-tech energy industry.
This work attempts to close the gap between the growing expectations on AI-based discrete-event simulations for manufacturing schedule optimization on the one hand and the current limitations of data quality and Information Technology (IT) capabilities in existing Enterprise Resource Planning (ERP) systems on the other hand. In order to deliver the expected benefits, business targets of schedule optimization need to be understood, the solution approach has to be defined and verified in a complex manufacturing environment. Performance and reliability of the implemented solution are validated with two real data sets derived from ongoing business planning activities.
An actor-critic architecture is proposed which uses a multi-stage schedule compression algorithm for modifying the start dates of production orders according to defined business targets and a genetic algorithm which selects production orders for automated make or buy decisions. The first research result is the provision of a manufacturing schedule for 375 product types, more than 180 manufacturing resources, 367 process variants (routings), and a total of 1.293 production orders at about 80% average capacity utilization of bottleneck machinery in a 12 months planning horizon, which is optimized according to lead times of customer products. The second research result is the generation of a manufacturing schedule for 3.657 production orders in a 24 months planning horizon, where the optimal outsourcing portion is calculated according to defined extensions of standard lead times within the same supply and manufacturing network.

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Cited By

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  • (2023)Linking Discrete-Event Simulation with Artificial Intelligence: A Literature-Based Analysis of Existing Approaches in the Context of Manufacturing Planning and Control2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)10.1109/IEEM58616.2023.10406374(1194-1198)Online publication date: 18-Dec-2023
  • (2022)Discrete-Event Simulation and Machine Learning for Prototype Composites Manufacture Lead Time PredictionsProceedings of the Winter Simulation Conference10.5555/3586210.3586350(1695-1706)Online publication date: 11-Dec-2022
  • (2022)Real-time pipe system installation schedule generation and optimization using artificial intelligence and heuristic techniquesJournal of Information Technology in Construction10.36680/j.itcon.2022.00927(173-190)Online publication date: 23-Feb-2022
  • Show More Cited By

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    cover image ACM Other conferences
    ICACS '20: Proceedings of the 4th International Conference on Algorithms, Computing and Systems
    January 2020
    109 pages
    ISBN:9781450377324
    DOI:10.1145/3423390
    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 ACM 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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    • University of Thessaly: University of Thessaly, Volos, Greece

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    Published: 25 November 2020

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    Author Tags

    1. Actor-Critic Architecture, Multi-Objective Optimization
    2. Artificial Intelligence
    3. Discrete-Event Simulation
    4. Genetic Algorithm
    5. Manufacturing Planning
    6. Schedule Optimization

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    View all
    • (2023)Linking Discrete-Event Simulation with Artificial Intelligence: A Literature-Based Analysis of Existing Approaches in the Context of Manufacturing Planning and Control2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)10.1109/IEEM58616.2023.10406374(1194-1198)Online publication date: 18-Dec-2023
    • (2022)Discrete-Event Simulation and Machine Learning for Prototype Composites Manufacture Lead Time PredictionsProceedings of the Winter Simulation Conference10.5555/3586210.3586350(1695-1706)Online publication date: 11-Dec-2022
    • (2022)Real-time pipe system installation schedule generation and optimization using artificial intelligence and heuristic techniquesJournal of Information Technology in Construction10.36680/j.itcon.2022.00927(173-190)Online publication date: 23-Feb-2022
    • (2022)Discrete-Event Simulation and Machine Learning for Prototype Composites Manufacture Lead Time Predictions2022 Winter Simulation Conference (WSC)10.1109/WSC57314.2022.10015322(1695-1706)Online publication date: 11-Dec-2022

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