Machine Learning Applications for Production Scheduling Optimization - Abstrak, Pendahuluan, dan Tinjauan Tantangan

Aug 3, 2026

Upload by Eden

Production scheduling represents a critical function within manufacturing and industrial operations, directly affecting productivity, operational efficiency, and overall cost management. Traditional rule-based, heuristic, and mathematical optimization approaches often struggle under the dynamic, uncertain, and stochastic conditions of modern production environments. This paper investigates Machine Learning techniques—including reinforcement learning, neural networks, and genetic algorithms—to model complexity, adapt to real-time disruptions, and improve decision-making. It also reviews key industrial applications, evaluates performance versus conventional methods, and discusses challenges such as data availability, interpretability, and legacy system integration.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Limitations of Traditional Scheduling

Drivers for Intelligent Adaptive Scheduling

Role of Machine Learning in Scheduling

E-ISSN: 3031-8521 P-ISSN: 3032-1867 Halaman 63-79 Volume 2 Nomor 4 Tahun 2025 Machine Learning Applications for Production Scheduling Optimization Aguh Patrick Sunday1, Udu Chukwudi Emeka2, Emmanuel Okechukwu Chukwumuanya3, Okpala Charles Chikwendu4 Department Industrial and Production Engineering, Nnamdi Azikiwe University, P. M. B. 5025 Awka, Anambra State – Nigeria [eo.chukwumuanya@unizik.edu.ng](eo.chukwumuanya@unizik.edu.ng) Abstrak Penjadwalan produksi merupakan fungsi yang sangat krusial dalam operasi manufaktur dan industri, dengan pengaruh langsung terhadap produktivitas, efisiensi operasional, dan pengelolaan biaya secara keseluruhan. Meskipun metode penjadwalan tradisional menjadi dasar dalam praktik industri, metode tersebut sering kali menunjukkan keterbatasan ketika dihadapkan pada kompleksitas, variabilitas, dan tuntutan dinamis dalam lingkungan produksi modern. Sebagai respons terhadap tantangan tersebut, makalah ini menyelidiki potensi teknik Machine Learning (ML) untuk meningkatkan hasil penjadwalan produksi. Secara khusus, makalah ini mengeksplorasi kemampuan reinforcement learning, jaringan saraf tiruan (neural networks), dan algoritma genetikadalam memodelkan sistem yang kompleks, beradaptasi terhadap gangguan secarawaktu nyata, serta mendukung proses pengambilan keputusan yang lebih efektif. Makalah ini juga meninjau berbagai penerapan industri yang signifikan dari teknik-teknik tersebut, dengan melakukan evaluasi kritis terhadap kinerjanya dibandingkan dengan metode konvensional. Selain itu, dibahas pula tantangan yang melekat dalampenerapan ML pada penjadwalan produksi, termasuk ketersediaan data, interpretabilitasalgoritma, dan integrasi dengan sistem lama (legacy systems) . Akhirnya, studi ini menguraikan arah penelitian di masa depan, dengan menekankan pentingnya pengembangan solusi penjadwalan berbasis ML yang lebih tangguh, dapat diskalakan, dan mudah diinterpretasikan untuk memenuhi kebutuhan industri modern yang terus berkembang. Kata kunci: penjadwalan produksi, pembelajaran mesin, interoperabilitas, optimisasi, produktivitas, aplikasi industri, efisiensi manufaktur. Abstract Production scheduling represents a critical function within manufacturing and industrial operations, exerting a direct influence on productivity, operational efficiency, and overall cost management. Traditional scheduling methodologies, while foundational, often E-ISSN: 3031-8521 P-ISSN: 3032-1867 Halaman 63-79 Volume 2 Nomor 4 Tahun 2025 exhibit limitations when confronted with the complexity, variability, and dynamic demands of contemporary production environments. In response, this paper investigates the potential of Machine Learning (ML) techniques for the enhancement of production scheduling outcomes. Specifically, it examines the capabilities of reinforcement learning, neural networks, and genetic algorithms to model complex systems, adapt to real-time disruptions, and support more effective decision-making processes. The paper further reviews notable industrial applications of these techniques, critically evaluating their performance relative to conventional methods. In addition, it addresses the inherent challenges associated with the deployment of ML in production scheduling, including data availability, algorithmic interpretability, and integration with legacy systems. Finally, the study outlines future research directions, emphasizing the need for more robust, scalable, and interpretable ML-based scheduling solutions to meet the evolving demands of modern industry. Keywords : production scheduling, machine learning, interoperability, optimization, productivity, industrial applications, manufacturing efficiency. INTRODUCTION Efficient production scheduling remains fundamental to achieving operational excellence in manufacturing and industrial processes. Scheduling governs the allocation of resources, sequencing of tasks, and synchronization of activities, all of which critical

FAQs

Why is production scheduling considered crucial in manufacturing operations?

Production scheduling directly affects productivity, operational efficiency, and overall cost management by governing resource allocation, task sequencing, and activity synchronization.

What limitations do traditional scheduling methods face in modern production environments?

Conventional rule-based, heuristic, and mathematical optimization approaches are often rigid and rely on static models, making them less effective under dynamic, uncertain, and stochastic conditions.

Which Machine Learning techniques are highlighted for production scheduling optimization?

The document focuses on reinforcement learning, neural networks, and genetic algorithms to model complex systems, adapt to real-time disruptions, and support better decision-making.

Aug 3, 2026

Upload by Eden