Ride-Hailing Matching with Uncertain Travel Time: A Novel Interval-Valued Fuzzy Multi-Objective Linear Programming Approach

This study introduces an innovative approach to tackle multi-objective linear programming (MOLP) problems amidst uncertainty, employing interval-valued fuzzy numbers. The method is tailored to resolve ride-hailing matching challenges encompassing uncertain travel times. Findings reveal that managing...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 12; no. 9; p. 1355
Main Authors: Supian, Sudradjat, Subiyanto, Megantara, Tubagus Robbi, Bon, Abdul Talib
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2024
Subjects:
Algorithms
Batch processing
Car pools
Decision making
Efficiency
Fuzzy sets
interval-valued fuzzy numbers
Linear programming
Machine learning
Matching
Methods
multi-objective linear programming
Multiple objective analysis
Nonlinear programming
Parameter uncertainty
Performance evaluation
ride-hailing matching
Transportation services
Travel time
uncertainty
User satisfaction
Indonesia
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary:This study introduces an innovative approach to tackle multi-objective linear programming (MOLP) problems amidst uncertainty, employing interval-valued fuzzy numbers. The method is tailored to resolve ride-hailing matching challenges encompassing uncertain travel times. Findings reveal that managing uncertainty parameters within interval-valued fuzzy MOLP is achieved through strategic reformulations, focusing on constraint coefficients, resulting in streamlined linear programming formulations conducive to solution simplicity. The efficacy of the proposed model in efficiently handling ride-hailing matching quandaries is demonstrated. Moreover, this study delves into the prospective applications of the developed method, including its potential for generalization to address non-linear programming (NLP) issues pertinent to the ride-hailing domain. This research advances decision-making processes under uncertainty and paves the way for broader applications beyond ride-hailing.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12091355