arXiv subjects

Iwan Vanany

Publications and source records attributed to Iwan Vanany.

3 recordsLinked to original sources

arXiv2026

Coupling-Aware Sales and Operations Planning with Forced Co-Production in Polymer Production Plants

Profitability metrics, such as the single-product gross profit margin, constitute the standard toolbox to prioritize products in sales and operations planning (S&OP). These metrics leverage the premise that production lines can be committed independently, which endows them with a simple per-ton ranking that a planner can compute and use easily in practice. However, coupled plants, especially polymer plants whose parallel lines draw simultaneously on a single bulk feed, can fundamentally undermine this premise and mislead to value-destroying production plans. We propose a framework built on what we call the Augmented Gross Profit for Product Clusters (AGPPC), which converts the per-ton ranking metric into a per-coupled-hour ranking that carries a per-instance optimality certificate. We present the theory of AGPPC, which combines the co-production column concept with a fluid relaxation of the planning problem, alongside an integrated bilinear mixed integer program of the plant and a McCormick-linearized baseline that provides certified bounds for it, and we demonstrate its effectiveness on real operational data from an Indonesian polymer producer, where the coupling-aware metric alone raises operating profit by 7.6% and the full optimization by 28% over current practice.

math.OC↗

arXiv2023

A Robust and Efficient Optimization Model for Electric Vehicle Charging Stations in Developing Countries under Electricity Uncertainty

The rising demand for electric vehicles (EVs) worldwide necessitates the development of robust and accessible charging infrastructure, particularly in developing countries where electricity disruptions pose a significant challenge. Earlier charging infrastructure optimization studies do not rigorously address such service disruption characteristics, resulting in suboptimal infrastructure designs. To address this issue, we propose an efficient simulation-based optimization model that estimates candidate stations' service reliability and incorporates it into the objective function and constraints. We employ the control variates (CV) variance reduction technique to enhance simulation efficiency. Our model provides a highly robust solution that buffers against uncertain electricity disruptions, even when candidate station service reliability is subject to underestimation or overestimation. Using a dataset from Surabaya, Indonesia, our numerical experiment demonstrates that the proposed model achieves a 13% higher average objective value compared to the non-robust solution. Furthermore, the CV technique successfully reduces the simulation sample size up to 10 times compared to Monte Carlo, allowing the model to solve efficiently using a standard MIP solver. Our study provides a robust and efficient solution for designing EV charging infrastructure that can thrive even in developing countries with uncertain electricity disruptions.

math.OC↗

arXiv2022

Designing an Optimized Electric Vehicle Charging Station Infrastructure for Urban Area: A Case study from Indonesia

The rapid development of electric vehicle (EV) technologies promises cleaner air and more efficient transportation systems, especially for polluted and congested urban areas. To capitalize on this potential, the Indonesian government has appointed PLN, its largest state-owned electricity provider, to accelerate the preparation of Indonesia's EV infrastructure. With a mission of providing reliable, accessible, and cost-effective EV charging station infrastructure throughout the country, the company is prototyping a location-optimized model to simulate how well its infrastructure design reaches customers, fulfills demands, and generates revenue. In this work, we study how PLN could maximize profit by optimally placing EV charging stations in urban areas by adopting a maximal covering location model. In our experiments, we use data from Surabaya, Indonesia, and consider the two main transportation modes for the locals to charge: electric motorcycles and electric cars. Numerical experiments show that only four charging stations are needed to cover the whole city, given the charging technology that PLN has acquired. However, consumers' time-to-travel is exceptionally high (about 35 minutes), which could lead to poor consumer service and hindrance toward EV technologies. Sensitivity analysis reveals that building more charging stations could reduce the time but comes with higher costs due to extra facility installations. Adding layers of redundancy to buffer against outages or other disruptions also incurs higher costs but could be an appealing option to design a more reliable and thriving EV infrastructure. The model can provide insights to decision-makers to devise the most reliable and cost-effective infrastructure designs to support the deployment of electric vehicles and much more advanced intelligent transportation systems in the near future.

econ.GN↗