Behavioral TOPSIS technique based on probabilistic picture hesitant fuzzy probability splitting algorithm and novel...

Area of Science:

• Decision Sciences
• Fuzzy Set Theory
• Information Systems

Background:

• Hotel recommendation is a complex group decision-making problem with uncertain information.
• Existing methods struggle with inconsistent data lengths and aggregation defects in fuzzy environments.
• Probabilistic Picture Hesitant Fuzzy Sets (PPHFS) offer a robust framework for handling such uncertainties.

Purpose of the Study:

• To develop a novel multi-attribute group decision-making (MAGDM) technique for hotel recommendation (HR) using PPHFS.
• To address normalization issues with inconsistent hesitant degree lengths using a probability splitting algorithm.
• To introduce new aggregation operations and distance measures for PPHF elements.

Main Methods:

• Proposed a probability splitting algorithm for normalizing PPHF elements.
• Developed novel aggregation operations (PPHFWA, PPHFWG) and distance measures (PPHFDisMs) for PPHFEs.
• Extended Behavioral TOPSIS (BTOPSIS) to the PPHF environment, creating the PPHFBTOPSIS technique.

Main Results:

• The proposed probability splitting algorithm effectively normalizes PPHF elements without altering original information.
• Novel aggregation operators and distance measures demonstrate superior properties for PPHFEs.
• The PPHFBTOPSIS technique provides a robust and effective solution for hotel recommendation under uncertainty.

Conclusions:

• The developed PPHFBTOPSIS technique successfully addresses MAGDM challenges in hotel recommendation.
• The study validates the robustness and applicability of PPHFS in real-world decision-making scenarios.
• This research contributes a significant advancement in fuzzy decision-making methodologies for travel and tourism.