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All Journal Jurnal Industri dan Inovasi (INVASI)
Andriansyah Hamid
Program Studi Teknik Industri Universitas Syiah Kuala
Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Transportation

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Studi Komparatif MILP dan Heuristik pada Job Shop Scheduling Problem: Minimasi Makespan dan Total Tardiness Andriansyah Hamid
Jurnal Industri dan Inovasi (INVASI) Vol 4, No 2 (2026): Vol 4, No 2 (2026): June
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/invasi.v4i2.15059

Abstract

This study presents a comprehensive analysis of the Job Shop Scheduling Problem (JSSP) with two commonly used objectives in manufacturing: minimizing makespan and minimizing total tardiness. Two approaches are directly compared: an exact model based on Mixed Integer Linear Programming (MILP) solved using Gurobi, and a fast heuristic based on dispatching rules and local search. The study is conducted on 10 random instances (10 jobs, 5 machines, 5 operations per job). The results show that the heuristic is highly competitive for the makespan objective (average gap of 9.25% with approximately 240 times faster computation than MILP). For the tardiness objective under a balanced configuration, the heuristic yields an average defined gap of 52.39% with a runtime cost about 6.0 times slower than MILP. This paper provides a reproducible baseline, data-driven analysis, and technical directions for improving both the quality and efficiency of tardiness-focused heuristics in future research.
Klasifikasi Kecacatan Produksi Manufaktur Menggunakan Ensemble Learning: Perbandingan XGBoost, LightGBM, dan Random Forest dengan SHAP Explainability Andriansyah Hamid
Jurnal Industri dan Inovasi (INVASI) Vol 4, No 2 (2026): Vol 4, No 2 (2026): June
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/invasi.v4i2.15508

Abstract

Production defects represent a critical challenge in modern manufacturing industries, directly impacting operational efficiency, production costs, and customer satisfaction. This study proposes an Ensemble Learning-based approach to classify production defect status using the Predicting Manufacturing Defects dataset from Kaggle (3,240 records, 16 features). Three state-of-the-art Ensemble Learning algorithms, XGBoost, LightGBM, and Random Forest, were comprehensively evaluated against Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) as baseline models. Significant class imbalance (84.04% High Defects vs. 15.96% Low Defects) was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Evaluation employed Accuracy, Precision, Recall, F1-Score, AUC-ROC, and 5-fold Cross-Validation. SHapley Additive exPlanations (SHAP) was applied to enhance model interpretability and identify the most influential features. Results show Random Forest achieved the highest accuracy of 94.75% with F1-Score 94.49%, while LightGBM performed best in 5-fold Cross-Validation with mean F1-Score of 95.92% ± 1.07%. SHAP analysis revealed that MaintenanceHours, DefectRate, and QualityScore are the three most dominant factors in determining production defect status.
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