A robust decision making algorithm for handling uncertainty in career planning via a circular intuitionistic fuzzy SWARA WASPAS method

Why choosing training paths is so hard

Many students finish vocational programs only to discover that landing a good job is still difficult. They must pick among courses, counseling services, and job search workshops while employers’ needs keep shifting. This paper looks at how to design and compare such programs in a way that respects the messy reality of human judgment, where experts feel unsure, change their minds, and do not always agree.

Decisions in a world of doubt

Career training decisions involve many moving parts: matching skills to real jobs, helping young people clarify their goals, teaching job search habits, and keeping people employed over time. These factors can pull in different directions, and experts such as trainers, counselors, and industry partners often hold only partial or uncertain information. Instead of forcing them to give a single neat score, the study allows their opinions to reflect hesitation and doubt, capturing how strongly they favor an option and how unsure they feel about it.

Circles that capture uncertainty

To do this, the author uses a recent idea in mathematics that describes opinions as circles rather than single points. Each circle shows how much an expert leans toward or against an option, plus a band of uncertainty around that view. This richer picture recognizes that an expert might say “this looks good, but I am not fully confident.” Building on this, the study adapts two popular ranking tools: one that decides how important each factor is, and another that scores and orders the competing training programs. Both tools are rewritten so they can work directly with these circular opinion circles instead of rigid numbers.

Weighing what matters and scoring options

The first part of the method focuses on weighing the factors that matter most, such as how well skills match jobs, how clear students feel about their careers, how ready they are to apply, how often they are placed, and how long they stay employed. Experts express their views using everyday phrases like “medium” or “very high,” which are then translated into the circular opinion form. The method gradually compares factors against one another, adjusting their weights while keeping the experts’ hesitation visible. The second part takes several competing training models, combines all expert views for each factor, and produces scores using two different ways of adding up the evidence. The scores are then blended to give a final ranking of the programs.

Testing in a realistic training scenario

To show how this works in practice, the author builds a case study of a vocational institute that wants to improve job outcomes. Five program styles are compared, ranging from simple skills training to a fully integrated model that adds structured planning, personal guidance, and links to employers. Experts from teaching, counseling, and industry rate each option, again using everyday language mapped into the circular opinion system. When the method processes this information, one option stands out: combining vocational training with basic career counseling consistently ranks first, ahead of more bare-bones training and even ahead of more complex integrated schemes.

Stable results under closer scrutiny

The study then checks how fragile these rankings are. It varies the importance given to the different factors and shifts the balance between the two scoring formulas. It also measures how similar the rankings are across methods using a standard statistical test. Across all these checks, the same program combination remains on top, and the order of the other options changes very little. This suggests that the method is not overly sensitive to small tweaks or disagreements among experts, a key requirement for use in real education and policy settings.

What the study means for students and planners

Put simply, the paper shows a way to make complex training and career guidance decisions more honest about uncertainty without becoming chaotic. By letting expert opinions be fuzzy and hesitant, yet still combining them in a structured way, the method identifies options that are both effective and stable across many tests. For a lay reader, the takeaway is that adding even modest career counseling to vocational training can make a clear, reliable difference in employment outcomes, and that careful, uncertainty-aware decision tools can help schools and agencies choose such improvements with greater confidence.

Citation: Chen, J. A robust decision making algorithm for handling uncertainty in career planning via a circular intuitionistic fuzzy SWARA WASPAS method. Sci Rep 16, 15953 (2026). https://doi.org/10.1038/s41598-026-45506-0

Keywords: career planning, vocational training, decision making, fuzzy logic, employment guidance