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All Journal Pro Food SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Pengabdian Magister Pendidikan IPA Mandalika Mathematics and Educations Journal Griya Journal of Mathematics Education and Application Rengganis Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Aquaculture Indonesia Milang Journal of Mathematics and Its Applications Jurnal Pepadu MILANG Journal of Mathematics and Its Applications
Dilla Afriansyah
Universitas Mataram
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Optimization of Coffee Inventory and Replenishment Planning under Demand Uncertainty: A Linear Programming Approach Lingga Gita Dwikasari; Dilla Afriansyah
Mandalika Mathematics and Educations Journal Vol 8 No 2 (2026): Edisi Juni
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v8i2.12437

Abstract

This study develops a multi-period linear programming model to optimize coffee inventory and replenishment planning under demand uncertainty. The model integrates inventory balance, replenishment capacity, storage capacity, and safety stock constraints to determine cost-efficient replenishment quantities and ending inventory levels for three coffee products: Robusta, Arabica, and Blend. Simulated data over six planning periods were analyzed under low, medium, and high demand scenarios using PuLP in Python. The results show that optimal solutions were obtained under low and medium demand conditions, with total inventory costs of Rp 286,836,000 and Rp 480,466,000, respectively. Under low demand, inventory was maintained exactly at safety stock levels, reflecting a just-in-time strategy. Under medium demand, the model temporarily increased Robusta inventory to anticipate future demand. However, the high-demand scenario was infeasible, indicating insufficient replenishment capacity. The model provides a practical decision support tool for cost-efficient and resilient coffee inventory management.
Optimization of Coffee Production and Distribution under Multi-Demand Scenarios: A Linear Programming and Dual Analysis Approach Dilla Afriansyah; Lingga Gita Dwikasari
Griya Journal of Mathematics Education and Application Vol. 6 No. 2 (2026): Juni 2026
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v6i2.1209

Abstract

Efficient production and distribution planning is essential for improving profitability and operational performance in coffee supply chains. This study develops a linear programming model to optimize coffee production and distribution decisions under multiple demand scenarios. The model considers three coffee products, namely Robusta, Arabica, and Blend coffee, distributed to four regional markets. The objective is to maximize total net profit while satisfying production capacity, demand, distribution capacity, service-level, and minimum production constraints. Three demand scenarios—low, medium, and high demand—were evaluated to examine the impact of varying market conditions on optimal production and distribution strategies. The results indicate that the optimal profit increased from Rp 56.585 million under the low-demand scenario to Rp 80.375 million under the medium-demand scenario and Rp 94.350 million under the high-demand scenario. The optimization model consistently prioritized Arabica and Blend products because of their higher profitability, while Robusta was utilized primarily to satisfy capacity and demand requirements under higher-demand conditions. Shadow price analysis identified Arabica production capacity and regional distribution capacities as the most critical resources affecting profitability. In addition, reduced cost analysis revealed distribution routes that were not economically competitive under current operating conditions. The findings demonstrate that the proposed linear programming framework provides an effective decision-support tool for optimizing coffee production and distribution planning. The integration of scenario analysis and dual analysis offers valuable managerial insights for improving resource allocation, operational efficiency, and profitability in coffee-based food enterprises.
Comparative Mathematical Modeling of Coffee Shelf-Life Using Linear Regression and Ensemble Learning under Simulated Storage Conditions Lingga Gita Dwikasari; Dilla Afriansyah
Griya Journal of Mathematics Education and Application Vol. 6 No. 2 (2026): Juni 2026
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v6i2.1276

Abstract

management. However, comparative studies evaluating interpretable statistical models and ensemble learning algorithms for coffee shelf-life prediction remain limited, particularly using simulation-based datasets. This study compared the predictive performance of Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Gradient Boosting Regression (GBR) using a simulation-based dataset of 400 observations representing realistic storage conditions. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). MLR achieved the best performance with the lowest MAE (10.01 days), the lowest RMSE (12.55 days), and the highest R² (0.8649), outperforming both ensemble learning models. Feature importance analysis consistently identified storage temperature as the most influential predictor of coffee shelf-life. These findings demonstrate that increased model complexity does not necessarily improve predictive accuracy and support the use of simulation-based datasets for developing predictive models prior to validation with experimental data.
Co-Authors Adriani, Ika Reskiana Anggraini, Ines Marisya Dwi Arsyi, Elmia Kharisma Eka Partiwi Firman Fajar Perdhana Gita Dwikasari, Lingga Hadi Sumarno Hadi Sumarno Hasanah, Nadia Permata Hidayatullah, Rizkiya Azzahra I Wayan Mangku Ines Marisya Dwi Anggraini Isnain, Fuad Sauqi Lingga Gita Dwikasari Lingga Gita Dwikasari, Lingga Gita Made Gendis Putri Pertiwi Made Gendis Putri Pertiwi Ni Kadek Ayu Astiti Kirtiyani Novia Rahayu Novia Rahayu Pertiwi, Made Gendis Putri Qabul Dinanta Utama Rahayu, Olivia Rini Nofrida Rini Nofrida Ruhaeni, Ruhaeni Sukma, Dinda Galuh Wahyuni Ekasasmita Widyasari, Rucitra Yesica Marcelina Romauli Sinaga Yusuf, Aulia Islamiati Zahra, Khoiruz Zainuri Zainuri Zainuri Zainuri Zainuri, Zainuri