1) Identify your needs: what features are essential?

Before selecting an optimization solver, it’s crucial to clearly define your requirements:

• Does the software account for all the decisions you need to optimize, or do you need to predefine some of them?
• Can it take a global approach, or do you have to break the problem into sub-parts (by region, resource, or time period)?
• Are the results directly usable, or do they require manual adjustments?

If you need to bypass some limitations in the software, it’s a sign you may need a more comprehensive solution. The essential features are those that enable optimization of your problem in its entirety — not just a simplified version of it. These high-level decisions are often the most impactful in terms of operational efficiency.

2) Ensure adaptability: flexibility and scalability

A high-performing optimization solver should be adaptable both during the initial setup and in day-to-day use.

Adaptability to your business

The software should accommodate all the specificities and constraints of your problem, accouting for different decision levels (strategic, tactical, operational). Ideally, it should be an all-in-one tool capable of addressing your various challenges simultaneously (e.g., palletization + loading + routing in a single run).

Flexible configuration

A good solver should allow advanced configuration without requiring custom development. It shouldn’t just execute predefined rules and automations, it should prescribe the best possible solution based on your objectives and constraints.

3) Require performance: quality, ROI and speed

An optimization solver must be able to deliver high-quality solutions in a reasonable amount of time, whether the problem is simple or highly complex. Two aspects are key here:

Quality of solution and ROI

The software should generate plannings that are not only optimal (minimizing operational costs) but also feasible (respecting all constraints at their most detailed level). Not all tools are created equal: even on a “simple” problem, some can offer significantly higher productivity gains than others.

Computation speed

The more complex your problem (number of vehicles, deliveries, constraints, etc.), the more likely computation time will grow exponentially. Make sure the software relies on advanced algorithms capable of handling large datasets and scaling effectively.

4) Choose an intuitive, user-friendly interface

Ergonomics and ease of use play a major role in a software’s effectiveness and adoption. A good tool should be easy to use and quick to deploy (2 to 4 weeks).

Implementation should be intuitive, with support tailored to different user profiles and a responsive helpdesk that offers guidance on how to best handle your business challenges within the solver.

→ A great optimization software must combine functionality, performance, scalability, and accessibility to effectively answer your most complex operations planning challenges.

Atoptima’s optimization solvers tackle the most complex supply chain problems with unmatched performance and the most comprehensive functional spectrum on the market. With 25+ years of expertise in mathematical optimization, our decision-making AI solutions help reduce logistics costs by up to 30% through smarter resource planning.