Optimization Engine vs. Human Brain

Introduction. Why this comparison makes sense

At first glance, comparing an optimization model to the human brain may seem inappropriate. Human decision-making in shipping is not a formula. It is experience, intuition, market knowledge, risk awareness, and the ability to make decisions under uncertainty.

And yet this comparison comes up again and again—not because the human brain is “bad” or “not smart enough,” but because the problems solved in commercial shipping have always been complex and multi-factor. In recent years, it is not their nature that has changed, but their dynamics: input parameters shift faster, the market has become more volatile, and decisions increasingly need to be rebuilt under much tighter time constraints.

This accelerated pace triggers a chain reaction: not only does the number of factors grow, but so does the difficulty of accounting for them coherently when making decisions.

An optimization engine does not replace a human being. It expands the solution space a person can physically cover.

This is the boundary we will talk about—not an intellectual one, but a cognitive and time boundary.

What the human brain does best

The human brain is irreplaceable where what is needed is not calculation, but judgment:

• interpreting the market rather than merely computing;
• selecting relevant scenarios based on experience;
• working with incomplete and contradictory information;
• making decisions under time pressure and responsibility.

An experienced broker or operator can almost instantly:

• sense whether a vessel can realistically “work” a region or not;
• feel where a risk is not justified.

That is why, in real practice, most decisions start with human intuition rather than an algorithm. At this stage, the human performs a fundamentally important role: they set the frame of the problem—deciding which scenarios are worth considering at all, which constraints are essential and which are secondary, and where the boundary of what is acceptable lies.

In other words, human thinking defines the feasible solution space—the space within which optimization becomes meaningful.

Yet this strength has a natural limitation: a limitation of scale. It shows up not in the complexity of a single idea, but in the number of interdependent options and parameters that must be held, compared, and evaluated simultaneously over time. And this is where human thinking begins to run up against not a lack of knowledge or experience, but physical cognitive limits.

Exponential complexity and decision sensitivity

The complexity of problems in commercial shipping grows exponentially as steps, parameters, and constraints are added. Even in a basic formulation, one must simultaneously account for cargoes and ports, time windows, technical and commercial constraints, multiple performance criteria, and possible voyage evolutions.

Each additional element—one more voyage, one more mandatory condition, one more parameter such as an Exit Point—expands the feasible solution space not gradually, but in leaps. At some point, a manual approach inevitably relies on simplifications: only a few “reasonable” scenarios are evaluated, while the rest of the solution space remains out of sight.

A practical consequence of this exponential complexity is a high sensitivity of outcomes to details. Scenarios that look similar at the first level—by distance, region, or timing—may lead to different commercial results due to speed choices, fuel conditions, port waiting and turnaround realities, regulatory effects, or the cost of time.

It is precisely this combination—a rapidly expanding set of options and sensitivity to nuance—that makes selecting an optimal solution fundamentally hard to do by manual analysis. An optimization engine removes this constraint: within the feasible solution space defined by a human, it systematically compares scenarios under a single logic and makes the real trade-offs between cost, time, and risk visible.

Optimization impact: 10–15% as a systematic outcome

In a mature industry, revolutions are rare. A typical optimization impact appears as a sustained 10–15% improvement:

• in cost,
• profit,
• time,
• or an overall combined result.

This effect arises because:

• more combinations are compared;
• edge cases are not missed;
• rules are applied consistently, without exceptions.

The outcome is a reproducible, systematic benefit—not a one-off stroke of luck.

From a local choice to confidence in the decision

An intuitive decision is often accompanied by doubt: “What if there was a better option I didn’t see?”

An optimization approach does not remove responsibility, but it:

• makes the solution space visible;
• shows which options were considered and why they lost;
• enables the decision to be explained to colleagues and partners.

In this sense, an optimization engine becomes an instrument of confidence.

Where the real boundary lies

The boundary between human thinking and optimization does not lie between “smart” and “not smart,” nor between “intuition” and “algorithm.”

It lies between:

• problems where experience and local logic are sufficient,
• and problems where scale, dynamics, and the number of interdependent factors exceed cognitive limits.

There are more and more such problems in modern commercial shipping. And it is precisely here that an optimization engine—in the form implemented in Marine Solver—becomes a natural extension of professional thinking.