From Subjective Guesswork to Objective Strategy: Rethinking Scheduling with EvoAPS
Scheduling is traditionally a highly subjective task. Even with a minimal dataset—just a few operations and resources—the number of possible outcomes can be staggering. In fact, for as few as 25 operations on a single resource, the total number of combinations would take over 49 million years to compute exhaustively, even at a rate of one billion schedules per second. And that’s just for a simplified case.
Given this complexity, the initial goal in building EvoAPS was not speed, but clarity. Rather than trying to generate schedules quickly, we set out to build a system that could measure the quality of a scheduling result—shifting the focus from simply producing a schedule to understanding what makes a schedule good.
But that naturally leads to a critical question:
What does “good” actually look like?
Why the Optimum Isn’t Always Practical
Customers and stakeholders often express the expectation that scheduling systems will produce the "optimum" schedule. With today’s technology buzzwords like “AI” and “machine learning” becoming ubiquitous, it’s easy to assume that any modern solution should be able to compute the perfect answer. However, the reality is quite different.
In real-world scheduling scenarios, using traditional heuristics or rule-based engines, you get one shot at finding a workable result. Exploring every possible combination to find the theoretical optimum is not only computationally unfeasible—it’s often unnecessary.
What organizations really need is a reliable, repeatable way to find a good schedule. But therein lies the challenge: “good” is highly subjective, varying between individuals, roles, and business contexts.
How EVOAPS Redefines “Good”
EvoAPS brings objectivity to a traditionally subjective problem. Instead of relying on individual planners’ experience or trial-and-error, it enables organizations to define business strategies through configurable weightings. These strategies shape the criteria by which schedule results are evaluated, aligning outcomes with broader business goals—such as cost efficiency, resource utilization, or on-time delivery.
This approach enables scheduling to become a structured, strategic process:
· Schedules are built to meet defined business strategies.
· Users can run multiple strategies and compare results objectively.
· What-if scenarios can be explored by anyone in the organization, not just planners.
By aligning scheduling logic with business intent, EvoAPS becomes more than a tool—it becomes an extension of the organization’s strategic thinking.
Why It Matters
Better schedules aren’t just about meeting targets. They reduce waste, improve efficiency, and support sustainability goals—making them better for the environment as well as the bottom line.
So, rather than asking whether a schedule is “optimum,” we ask:
Does it align with what the business values most?
Can we measure its effectiveness against clear, defined criteria?
With EvoAPS, the answer is yes.
Defining “Good” in Scheduling: Moving from Subjective Expertise to Strategic Objectivity
As discussed previously, scheduling is a deeply complex problem—often described as an “n factorial” challenge—where even small datasets can result in an astronomical number of possible outcomes. Evaluating every potential combination to identify one that meets business objectives is computationally unfeasible with traditional solver engines, which attempt to evaluate all possibilities.
Historically, planners and schedulers have relied on their experience and institutional knowledge to navigate this complexity. These individuals are recognized as domain experts within the business and typically develop viable schedules by drawing on what has worked in the past. While this often leads to strong outcomes, it is inherently a subjective process—heavily dependent on the expertise and intuition of a few highly skilled individuals.
How Does EVOAPS Help?
EvoAPS transforms this approach by introducing strategy-driven scheduling. Rather than relying solely on individual expertise, EvoAPS allows users to define and apply business strategies that reflect what “good” looks like from their organization’s perspective. Through configurable weightings, users can shape the system’s understanding of business priorities, enabling it to evaluate scheduling outcomes more objectively.
These strategies can be customized and stored within each company profile, allowing for the creation and testing of multiple approaches. Users can run concurrent “what-if” scenarios on the same dataset, empowering more stakeholders across the business to engage with the scheduling process—without requiring deep domain knowledge.
By decoupling the scheduling process from individual subjectivity and enabling consistent, strategic evaluation, EvoAPS not only democratizes access to high-quality scheduling insights but also supports scalable, repeatable decision-making. This structured approach provides a measurable framework for evaluating outcomes, helping organizations move toward more objective and business-aligned scheduling.
The Evolutionary Process: Driving Smarter Scheduling Through Measured Improvement
Every time a strategy is executed in EvoAPS, our Evolutionary Algorithm generates a schedule and evaluates its quality—referred to as its fitness—based on how well it aligns with the selected business strategy.
This process doesn’t stop at a single result. Instead, EvoAPS applies small, deliberate changes—known as mutations—to explore alternative outcomes. Each new schedule is measured against the current best-performing result. Only those that demonstrate an improvement are retained. Over thousands of iterations, the system continuously evolves, refining the schedule towards better alignment with strategic goals. This iterative process is what gives the algorithm its name: evolutionary.
Once completed, all viable results are made available in the cloud, allowing users to review, compare, and select the most appropriate schedule. Any selected schedule can then be seamlessly loaded into Opcenter APS for execution or further refinement.
Conclusion: Shifting from Subjective Decisions to Objective Strategies
This evolutionary, strategy-based approach enables faster and more effective implementation of scheduling solutions like Opcenter APS. Instead of relying on traditional, subjective methods, businesses can now evaluate thousands of schedules per minute—objectively and consistently—against clearly defined business strategies.
This not only accelerates deployment but also empowers teams to explore multiple scenarios, identify what works, and continuously improve planning outcomes with confidence.
Defining Strategies and Managing Risk in EVOAPS
One of the greatest challenges businesses face when scheduling is balancing competing priorities. For example:
· “How do we maximize machine utilization while still delivering every order on time?”
· “Can we reduce changeovers without compromising delivery performance?”
· “How far can we pull orders forward without increasing operational risk?”
These are not simple trade-offs—and there is rarely one “correct” answer. That’s why EvoAPS empowers users to define business strategies that reflect their unique goals, constraints, and risk tolerances.
Strategy Configuration Through Intuitive Weighting
Within EvoAPS, strategies are configured using a slider-based interface that allows users to assign relative weightings to multiple criteria. These criteria—such as on-time delivery, resource efficiency, changeover minimization, and more—determine how the system evaluates the fitness of each scheduling result.
By adjusting these weightings, users can simulate different business scenarios and control how EvoAPS prioritizes outcomes. This enables users to create customized, flexible strategies that reflect real-world business decisions.
Each user can create as many different schedules as required, allowing the process of ‘What if?’ scheduling to be performed over both new data and any archived schedules that are available against the profile.
New strategies can be built by simply selecting the weighting criteria value and adding it to the strategy. Then the slider is used to set the desired level.
Interpreting Result Strength and Schedule Risk
As strategies are executed, EvoAPS provides real-time feedback on the strength of each result. Strength is a measure of how closely a result aligns with the defined strategy—essentially, how “fit” that schedule is in the context of the business’s goals.
While EvoAPS does not claim to find a mathematically optimal result—something often impractical or computationally unfeasible—it continually evolves and improves its results. When a solution holds the top position through successive iterations, it becomes a strong indicator that further improvements may no longer be realistic within the defined constraints.
Quantifying Risk and Providing Insights
Each schedule is also evaluated for WIP (Work In Progress) risk, helping users understand how tightly coupled operations are across the schedule—and where potential bottlenecks or process risks may emerge.
EvoAPS also offers insight dashboards alongside results, enabling users to interpret outcomes and make informed decisions, not just based on raw data, but through a strategic lens.