In artificial intelligence (AI), the inner workings are typically a black box: complex, opaque, and understood only partially, even by the select few who created it. However, in resource scheduling, where decisions have tangible impacts on efficiency and employee satisfaction, understanding the "why" behind these decisions is a necessity. Having identified this problem over the past few years while working with clients worldwide, Solvice pioneered the Explainable AI feature in 2024, a smart development aimed at driving adoption and reducing churn for scheduling AI solutions.
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In route optimization and resource scheduling, optimization engines often behave like black boxes: they produce plans, but the reasoning behind decisions is hidden. For planners, this opacity creates a trust gap:
Explainable AI (XAI) bridges this gap. At Solvice, our solutions deliver clear, human-readable explanations of scheduling decisions—making optimization transparent, auditable, and easier to adopt.
In this post, we’ll show how our explainability features work, the underlying approach, and why it’s so valuable for organizations adopting AI-powered scheduling.
Solvice’s platform offers transparent optimization as a service, designed to make the reasoning behind decisions accessible. Unlike traditional black-box solvers, our API can return explanations alongside solutions. This means planners and developers can understand why the optimizer made specific choices.
We deliver these explanations in two complementary forms:
What if you submit an infeasible scheduling problem? Traditionally, you’d just get “no solution” back.
Solvice improves on this by offering the "Explanation" end point which returns detailed insights into constraint violations.
Our solver identifies conflicting constraints that caused failure. Developers and planners can then:
This drastically reduces trial-and-error cycles when modeling real-world planning challenges.
Read up on the Solvice Explanation End Point in our documentation.
Every decision made by the solver, such as assigning a worker to a shift or selecting a delivery route, comes with an explanation describing why it was chosen. Simply activate "Explanation" as "true" in the API call and an extensive explanation of all jobs and alternatives is generated.
When activating this feature, the solver will:
This explainable AI feature enables software developers to build explanations into the user interface which in term will:
Our explainability feature is based on a hyperlocal discovery phase, which is a detailed examination of possible alternatives after identifying the optimal solution. This phase uncovers all feasible alternative assignments for every decision rendered by the solver. So if someone wonders why a specific resource has been assigned to a job, Explainable AI will tell the user how good the schedule would be if the user were to choose one of those alternatives. Given the computational intensity of this task, it's triggered only upon request for an explanation.
In summary, this exhaustive search yields a score evaluation, which is a measure of the quality of each alternative assignment for potentially countless alternatives. This gives users the necessary insights to understand why certain constraints were violated and how minor adjustments could enhance the solution.
At Solvice, we've long recognized the role of transparency in AI Optimization. This has led us to discuss and evaluate different forms of Explainable AI for optimization with our customers. We came to three important realizations:
Consider a scenario in shift scheduling where our solver not only assigns shifts optimally but also clarifies why each shift was allocated to a particular employee. By exploring alternative shift assignments and evaluating their impact on the overall solution, users are armed with the knowledge to make informed adjustments. As a developer you get the explanations when you enable explanation when requesting a solve. This will give you all the data you need to develop interface elements like shown in the wireframe below.
As we roll out this feature, we invite developers and product managers to experience firsthand the transformative power of Explainable AI. For those constructing their own scheduling software, Solvice's latest innovation offers not just a tool but a collaborative partnership in pursuit of greater efficiency, transparency, and trust.
To explore the potential of Explainable AI in your scheduling solutions, we encourage you to dive into our API reference, which provides detailed information on integrating our Explainable AI feature into your software, and our comprehensive guides, which offer step-by-step instructions on how to use the feature. The future of resource scheduling is not only intelligent but understandable. Welcome to a new era with Solvice's Explainable AI.
