In the ever-evolving world of Operations Research, Logistics, Planning, and Scheduling, staying ahead means embracing innovation. The introduction of GenOpt GPT, a cutting-edge tool where Constraint Programming (CP) and Combinatorial Optimization meet the prowess of ChatGPT, marks a significant leap forward. This post explores how GenOpt GPT is set to change the landscape of Operations Research by offering a versatile, adaptive approach to optimization problems.
The Challenge of Traditional Optimization Models: Traditionally, optimization in fields like Logistics, Transactions Netting, and Planning and Scheduling involves creating detailed models. These models, designed with specific rules, constraints, and objectives, require manual adjustments to accommodate new market requirements—a process both time-consuming and prone to error.
Introducing Generative Optimization as a Service: The concept of “Generative Optimization as a Service” combines Generative AI’s creativity with the precision of combinatorial optimization and mathematical modeling. GenOpt GPT exemplifies this innovative approach, offering unparalleled versatility and intelligent adaptation to meet emerging market needs.
Key Features of GenOpt GPT:
- Versatility: GenOpt GPT empowers users to describe virtually any optimization problem, specifying the necessary constraints, objectives, and rules.
- Learning from Base Optimization Models: By feeding base optimization models into GenOpt GPT, the AI learns the foundational aspects of various optimization problems.
- Intelligent Adaptation: GenOpt GPT analyzes the closest optimization model from its knowledge base, using it as a basis to adapt and tackle new challenges.
Building GenOpt GPT: A Glimpse Behind the Scenes:
- Training: The development of GenOpt GPT began with training a new GPT model, enriching it with a diverse set of optimization models.
- Infrastructure: A google cloud instance was set up to host a CP and Combinatorial Optimization Solver container, powered by MiniZinc and Google OR-Tools.
- GPT Actions: The integration of a specialized GPT action with the cloud instance enables GenOpt GPT to model and solve optimization problems efficiently.
- Prompt Creation: Crafting the perfect prompt was crucial in enabling GenOpt GPT to understand requirements, identify the nearest model from its knowledge base, and generate optimization/CP models in MiniZinc.
The Outcome: GenOpt GPT represents the potential for significant advancement in generative services, capable of evolving with market demands and user needs. Though still a prototype, its potential to streamline and enhance optimization processes is undeniable.