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Showing 5 results for Kurniawan
Adaptive Simulated Annealing and Adaptive Large Neighborhood Search Algorithm for Solving Container Loading Problem: A Case Study
Wahyu Kurniawan, Achmad Pratama Rifai , Nur Aini Masruroh,
Volume 0, Issue 0 (IN PRESS 2025)
Abstract
Adaptive Simulated Annealing (ASA) and Adaptive Large Neighborhood Search (ALNS) are two metaheuristic algorithms widely applied to solve discrete optimization problems. This study employs both algorithms to address the Container Loading Problem (CLP), a critical challenge in the consolidation-based freight forwarding industry, where maximizing container utilization directly influences revenue and operational efficiency. The case company, a national freight forwarding enterprise operating consolidation services in Indonesia, currently achieves an average container utilization rate of 56.8%, indicating a substantial opportunity for improvement. By formulating the CLP as a discrete combinatorial optimization model, this research aims to enhance both container load utilization and revenue through algorithmic optimization. The novelty of this work lies in its comparative implementation of ASA and ALNS under adaptive parameter calibration, applied to a real-world freight forwarding context, which remains rarely explored in previous CLP studies. Experimental results show that ALNS consistently outperforms ASA in terms of both objective value and robustness across scenarios. Specifically, the ALNS method achieves 85.4% container utilization and an average revenue increase of 8.6% per container, demonstrating superior efficiency in freight consolidation optimization. Additionally, experiments conducted under equal iteration conditions further support that ALNS maintains higher stability and better solution consistency compared to ASA, particularly in terms of fitness and utilization efficiency across different iteration scenarios. Despite ALNS requiring longer computation time, it remains well within the acceptable time frame for freight forwarding operations, where up to 24 hours is available for shipment planning. These findings provide practical implications for logistics firms seeking to integrate metaheuristic-based decision support systems to improve capacity utilization, responsiveness, and profitability.

An Aggregated Newsvendor Model for Multi-Item Perishable Inventories with Uncertain Demand
Dwi Kurniawan, Aghnia Nazhiifah Ulhaq, Aditya Fadhilah Althofian, Rubby Nur Rachman,
Volume 35, Issue 4 (IJIEPR 2024)
Abstract
In industrial and commercial settings, inventory systems often involve managing multiple products with diverse demand patterns, making the direct application of the single-item newsvendor model inefficient. To address this complexity, this study proposes an adaptation of the newsvendor model through demand aggregation, where related items are grouped into a product family. By aggregating demand and financial parameters, the traditional newsvendor approach can be extended to multi-item systems, simplifying the inventory management process. This method was tested in two different case studies—a coffee roaster company and a meatball producer—demonstrating its validity and applicability. The aggregated newsvendor model was found to enhance inventory accuracy and efficiency, reducing random error and improving operational performance. This approach offers a valuable extension of the newsvendor model, with potential for broader application across various industries.

The Role of Trust in the Relationship between Past Interactions and Buying Intention in Virtual Organizations
Dwi Kurniawan, Sabila Rafa Budiyanto,
Volume 36, Issue 1 (IJIEPR 2025)
Abstract
This paper studied the impact of relationships and past positive experiences on the dimensions of trust (ability, benevolence, integrity) and the influence of these trust dimensions on customer purchase intention. The measurement instrument was developed based on the literature. The study was conducted using a questionnaire completed by two hundred customers of an Indonesian Marketplace in Bandung and its surrounding areas. The data were then processed using Structural Equation Modeling (SEM). The results showed that ability and integrity affect customer purchase intentions, while benevolence does not. Additionally, we found that relationships and positive experiences in the past significantly affect ability and integrity.

Young’s Modulus Optimisation of Triply Periodic Minimal Surfaces using Full Factorial Design
Nor Hasrul Akhmal Ngadiman, Nur Syahirah Mustafa, Izman Sudin, Denni Kurniawan,
Volume 36, Issue 1 (IJIEPR 2025)
Abstract
Bone tissue scaffolds that closely mimic the mechanical and biological properties of natural bone is critical for enhancing the outcomes in treatment of bone tissue damages. This study introduces an optimisation approach to designing bone tissue engineering scaffolds using Triply Periodic Minimal Surface (TPMS) structures, evaluated through a Full Factorial Design methodology. Finite Element Analysis was applied to simulate the TPMS scaffolds under mechanical loading. The influence of key factors of strut thickness, unit cell configuration, and TPMS type, on the scaffold’s mechanical performance, specifically targeting Young's modulus was evaluated. By employing Full Factorial Design, this study generates empirical models of Young’s modulus as a function of those key factors. Primitive and Gyroid TPMS structures emerged as optimal, achieving Young's modulus values of 4912.3 MPa and 4666.7 MPa, respectively, with configurations of 0.01 mm strut thickness in a 3-unit cell construct. These results demonstrate that optimised TPMS scaffolds can meet the mechanical demands of bone tissue while providing adequate porosity for cell proliferation and nutrient transport, essential for effective bone regeneration.

A Carbon-Constrained Production Planning Model for Sustainable Manufacturing
Dwi Kurniawan,
Volume 36, Issue 3 (IJIEPR 2025)
Abstract
While traditional production planning focused on optimizing supply-demand balance through make-to-stock/make-to-order strategies and capacity management, the new imperative of carbon neutrality introduces critical complexities. Regulatory emission caps now require manufacturers to strategically trade carbon allowances, fundamentally transforming the challenges of production optimization. This study developed an aggregate production planning model that incorporates carbon trading constraints into operational decision-making, providing industries with a systematic approach to address both economic and environmental objectives. The model optimized multi-period production plans across alternative technologies, each with distinct cost-emission profiles, while incorporating subcontracting options. It simultaneously considered government-allocated emission permits, dynamic carbon market prices, technology-specific costs and emissions, and subcontracting expenses. Through mathematical optimization of production quantities, subcontracting levels, and carbon credit transactions, the model minimized total costs while ensuring compliance. Computational experiments with nonlinear programming solved via LINGO demonstrated the model's effectiveness in identifying optimal technology deployment strategies that achieve significant cost reductions while meeting environmental targets, offering manufacturers a powerful tool for sustainable operations in carbon-constrained markets.
 
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