The world of decision-making is teeming with choices and constraints, a complex dance that impacts everything from industrial operations to daily life. Imagine having to select the best option from a myriad of possibilities, each needing to align with certain rules and contribute towards specific goals. This is the realm of decision and optimization challenges, found in both industrial settings and our everyday decisions. Successfully resolving these issues promises substantial benefits: enhancing efficiency, cutting costs, increasing productivity, prolonging equipment life, making smarter choices, and improving customer satisfaction. Despite the vast array of methods to address these multifaceted challenges, finding software solutions is exceedingly rare. Typically, available software is proprietary, designed for very specific organizational issues, with usage rights held by its creators. More concerning, many software solutions developed in research settings remain undisclosed, eventually fading away.

In this context, COPSolver emerges as a groundbreaking innovation. Developed by Professor Tatiana Balbi Fraga from the Federal University of Pernambuco, and published in the journal Software Impacts, this freely accessible software revolutionizes how complex decision-making tasks are tackled in industrial settings. COPSolver’s growth continues with two new libraries for multicriteria ABC classification and demand pattern identification. Professor Fraga, enthusiastic about its expansion, says, “I’m excited to detail these in an upcoming article and am developing more libraries for demand forecasting and full production planning.” Plans to adapt existing software for further enhancing COPSolver’s capabilities are underway. The first library of COPSolver was designed to optimize the processing times of multiple products that are manufactured together, each with different production rates. This isn’t just a tool; it’s a significant leap forward in managing intricate industrial decisions.

At the heart of COPSolver’s first library is its method for optimizing these multi-product manufacturing processes. Professor Fraga explains, “For this reason, I have developed COPSolver to make available to everyone scientific tools that can help improve the decision management process as well as enhance the efficiency of various organizational processes”. This is especially relevant in industries where multiple products are made at the same time, each requiring different amounts of time. The challenge is to figure out the best production time for these mixed-product batches, considering various limits and requirements. By solving this challenge efficiently, COPSolver’s first library brings significant benefits to industries, leading to better stock control and increased productivity.

Delving deeper, COPSolver’s first library stands out with its low operational cost and high effectiveness. It uses Professor Fraga’s method, ensuring quick and accurate solutions to complex optimization tasks. The software architecture is strong and adaptable, written in a programming language named C++ and structured to handle a range of optimization tasks. Each problem is defined and solved within its specific section, ensuring efficient and effective problem-solving.

COPSolver’s impact is wide-ranging. In the industrial sector, it improves the efficiency of planning and producing batches of products, a vital part of production management. Professor Fraga highlights, “Determining the optimal production time of mixed-product batches is extremely important for some industries as it directly relates to stock management and meeting customer demand”. This efficiency contributes to better stock control and fulfilling customer needs, leading to cost savings and improved customer satisfaction. The software’s freely accessible nature ensures it can be widely used across various industries.

With an ambitious vision, Professor Fraga aims for COPSolver to rival established software like LINGO in effectiveness and simplicity. “Our goal is to efficiently solve any decision or optimization problem,” she notes, highlighting the software’s potential to transform decision-making with collaborative efforts. Looking forward, the team led by Professor Fraga envisions a bright future for COPSolver. Their goal is to expand its capabilities to address a broader range of challenges and methods, making it an essential tool for smaller businesses. By training these companies to use COPSolver, the team hopes to enhance their competitive potential, driving innovation and progress in the business landscape.

In conclusion, COPSolver represents a significant step forward in the field of decision-making and optimization software. Its development not only tackles a specific industrial challenge but also sets the stage for broader applications in various sectors. As industries continue to evolve and seek solutions for optimization, COPSolver stands as a testament to the power of innovative software development in enhancing efficiency and progress.

Journal Reference

Tatiana Balbi Fraga, “COPSolver: Open Source Software for Solving Combinatorial Optimization and Other Decision Problems — Library for Solving the Multi-Product p-Batch Processing Time Maximization Problem”, Software Impacts, 2023. DOI:

About the Author

Dr. Tatiana Balbi Fraga is Ms and PhD in computational modeling by the Rio de Janeiro Polytechnic Institute – Rio de Janeiro State University. Currently, she is an associate professor of Production Engineering at the Federal University of Pernambuco, and is also a founding leader of the “Group of Analysis, Modeling and Optimization of Systems” (GAMOS). Dr. Fraga develops technological, research and extension work, especially in mathematical modeling and optimization of standard problems, extracted from the scientific literature, and real problems, especially industrial problems and in the area of combinatorial optimization. Ten from her master’s degree, the teacher has worked with problems -oriented programming and recently developed and published the open source software COPSolver, which features a structure developed specifically for problems -oriented programming.