NexOptima

Creating Smart Optimization Solutions for Scheduling, Project Management, Production, Supply Chains, and Decision-Making

We specialize in crafting customized solutions to optimize scheduling, project management, and overall efficiency. Our services extend to transportation and supply chain management, helping businesses enhance their processes for maximum productivity. Additionally, we offer tools designed to support smarter decision-making, ensuring your operations run seamlessly and effectively, no matter the complexity.

Developing Solutions

Developing innovative solutions for scheduling, project management, production, transportation, supply chain management, and decision-making.

Delivering Custom Applications and Software

We deliver custom applications and software that solve these problems with high-quality interfaces and user-friendly designs.

Consulting

Conducting audits, providing expert advice, and offering support for short and long term challenges.

Modelling

Mathematical modelling, (non-) linear, mixed-integer mathematical programming.

Programming

Python, SQL, C, C++, With different solvers.

Algorithms

Heuristic methods, Metaheuristic approaches.

Solvers

CPLEX, Gourobi, Google OR-tools and Pyomo.

Computation

Maple, SageMath, Scientific workplace.

Management Software

Microsoft Project, Microsoft Excel.

Optimization of Planned Aircraft Maintenance Operations

Our objective was to develop an optimized schedule for assigning technicians to tasks based on their specific skills. We successfully modeled this challenge as a Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) and formulated it as a Mixed-Integer Linear Program (MILP), which we solved using exact optimization methods.

In cases where the problem's complexity rendered exact methods impractical, we employed heuristic approaches to derive high-quality solutions. The algorithms were implemented in a Python-based application, enabling efficient and streamlined management of the company’s maintenance operations.

As a result, we achieved a performance improvement of 40-50%, significantly enhancing operational efficiency.

Optimizing Delivery Routes: A Capacitated Vehicle Routing Problem Solution

Our goal was to create an efficient delivery routing system for a logistics company, ensuring timely deliveries while minimizing transportation costs. We modeled this challenge as a Capacitated Vehicle Routing Problem (CVRP) and formulated it as a Mixed-Integer Linear Program (MILP), which we solved using exact optimization techniques.

For instances where the problem scale made exact solutions infeasible, we applied metaheuristic methods to produce near-optimal routes. The solution was implemented in a Python-based tool, enabling effective management of fleet operations and significant cost savings for the company.

Optimizing Decision Processes: A Multi-Criteria Decision-Making Framework

Our objective was to support strategic decision-making by developing a robust framework for evaluating multiple competing criteria. We modeled this challenge as a Multi-Criteria Decision-Making (MCDM) problem, incorporating methods such as Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritize alternatives effectively.

In complex scenarios requiring dynamic inputs, we integrated sensitivity analysis to ensure adaptability and reliability. The framework was implemented in a user-friendly software application, empowering stakeholders to make informed, data-driven decisions with confidence.

Building a Graph Theory Education Platform for Enhanced Learning

Our aim was to create an interactive platform for teaching graph theory to university students, enabling hands-on learning and experimentation. The platform features tools for visualizing graph structures, performing algorithm simulations (such as shortest path and minimum spanning tree), and analyzing network properties.

To promote engagement, we included interactive exercises, real-world graph examples, and automated feedback mechanisms. Developed using modern web technologies, the platform provides an intuitive interface and supports seamless integration into the university's learning management system, fostering deeper understanding of graph theory concepts among students.

Solving Permutation Flow Shop Scheduling with Hybrid Grey Wolf Optimizer

Our objective was to address the complex permutation flow shop scheduling problem, aiming to minimize makespan and improve production efficiency in manufacturing settings. We formulated this challenge as an optimization problem and applied a Hybrid Grey Wolf Optimizer (HGWO) to find near-optimal scheduling solutions.

We integrated local search techniques into the Grey Wolf Optimizer to improve convergence speed and solution quality. The solution was implemented in C++, allowing for high-performance execution and enabling manufacturers to generate optimized schedules efficiently, ultimately reducing production time and operational costs.