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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Vydáno v:	Mathematics (Basel) Ročník 12; číslo 9; s. 1355
Hlavní autoři:	Supian, Sudradjat, Subiyanto, Megantara, Tubagus Robbi, Bon, Abdul Talib
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.05.2024
Témata:	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
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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Supian, Sudradjat Bon, Abdul Talib Megantara, Tubagus Robbi Subiyanto
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SubjectTerms	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
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Title	Ride-Hailing Matching with Uncertain Travel Time: A Novel Interval-Valued Fuzzy Multi-Objective Linear Programming Approach
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