Gurobi Connect Dubai
April 8, 2025 | Dubai

Optimization practitioners get excited when they have a clear puzzle in front of them. This is also the point in time where technology is usually introduced. However, success does not just depend on the right formulas and algorithms: it’s about aligning them with the right business challenges. In this session, we take a few steps back and look at identifying high-impact use case, developing a value hypothesis and scoping a proof of concept that can help validate the potential of an optimization solution.

Ronald van der Velden, Manager of Technical Account Management EMEA, Gurobi Optimization

Emirates Global Aluminium: Multi-Period, Enterprise-Scale Optimization Framework for Cost-Effective Decarbonization of Aluminium Manufacturing

Aluminum manufacturing faces significant decarbonization challenges due to its high demand for electricity, supply of which must be continuous and reliable leading to overreliance on fossil fuels. Electricity accounts for over 50% of total operational costs aside from raw materials expense, therefore, access to low-cost and reliable electricity becomes a critical enabler for economic sustainability of aluminium smelters. As industries globally strive to meet net-zero carbon emissions target with rising aluminum demand, sustainable production methods which balance cost efficiency, operational reliability, and emissions reduction will be essential for the Aluminum industry’s energy transition and growth. This study presents a multi-period ‘least-cost’ optimization framework tailored for meeting electricity demand of an aluminum smelter through a customized supply mix, integrating several renewable and conventional generation technologies, storage solutions, and carbon capture, such that a defined decarbonization target is met.

The objective function for the optimization routine is to minimize the levelized cost of electricity (LCOE) while meeting the defined decarbonization target and ensuring the electricity demand is met during all hours of the year through a mix of various generation and storage sources. To accurately estimate the LCOE, the work incorporates a robust financial model which captures the cost of various technologies in the mix such as – capital expenditure of equipment and supporting infrastructure, fixed operational expenditure to maintain and operate the energy system and variable operational expenditure such as fuel cost and efficiency corresponding to each technology. The LCOE is designed to additionally account for any electricity emissions cost from conventional sources because of carbon tax on electricity emissions in the country of focus. The optimization tool in such cases would propose a supply mix that minimizes the overall effective electricity cost, including emissions cost of any smelter. The technical and operational characteristics of the technologies (gas turbines, solar photovoltaics, wind, hydropower, hydrogen, nuclear, battery energy storage systems, long-duration energy storage, and carbon capture) have also been carefully modeled with configurable parameters to support scenario analysis.

Our early results demonstrate that a hybrid energy mix including renewables and storage can significantly reduce emissions while still maintaining economic feasibility. Analysis specific to UAE suggests solar PV with storage playing a dominant role in this energy transition for aluminium smelting due to high irradiance levels in this region.

Khuram Pervez, Director of Data & AI, Emirates Global Aluminium

In today’s dynamic business environment, optimizing demand and supply planning is crucial for achieving high efficiency and resilience.

This session explores how businesses can leverage advanced optimization techniques to enhance Integrated Business Planning (IBP). Through real-world case studies, we’ll showcase how in-and-out optimization improves resource allocation, minimizes waste, and drives agility. Learn insights into best practices for embedding optimization into IBP frameworks, ensuring better decision-making and sustainable growth.

Leonid Trotsenko, Managing Partner, Planingo

In the world of on-demand delivery, an efficient dispatcher is a key to the success of any delivery business.

As a leading delivery platform in the Middle East, at MRSOOL, the dispatch management plays a crucial role in ensuring that each order is assigned to the best courier in a way that provides the best experience for all parties involved — buyers, couriers, and businesses. Operational decision making requires speed, scale, and reliability. Greedy algorithms are fast, but not always optimal and certainly cannot consider multiple objectives.

In this session, we talk about building mathematical models to capture operational requirements, preferences, and business KPIs. We share our lessons on challenges to keep the model simple and get optimal decisions in matter of seconds. Effectiveness of any model depends on quality of data. We discuss challenges with quality of distance time matrix and food preparation time estimates etc. and ideas to improve accuracy with machine learning.

Vijay Patil, Associate Director of OR and AI, ORMAE, India

Bady Ammar, Head of Product, MRSOOL, Saudi Arabia

Finding high-quality feasible solutions quickly is a key challenge in real-world optimization applications. Gurobi is equipped with a rich set of heuristics that enable rapid solution generation and improvement to strike the balance between time-to-good-solutions and proof-of-quality. This talk will explore heuristics as an integral part of Gurobi’s solving strategy and demonstrate how certain non-default settings can drastically reduce the time-to-first-solution on certain models. Join us to discover how Gurobi makes advanced mathematical optimization a practical and effective tool for real-world decision-making.

Ronald van der Velden, Manager of Technical Account Management EMEA, Gurobi Optimization