OPTIMIZING PATIENT TRANSPORT UNDER INFECTION CONTROL CONSTRAINTS: A MILP-BASED DIAL-A-RIDE APPROACH

<br />With aging populations and rising healthcare demands, efficient patient transportation has become a critical challenge, particularly in the context of infection control. In this paper is presented an extended mixed-integer linear programming (MILP) model for optimizing patient transport in urban environments, with a focus on the separate transportation of infectious and non-infectious individuals. The model incorporates time windows, maximum allowable ride durations, and mandatory vehicle disinfection requirements. Experimental results obtained using CPLEX demonstrate that incorporating infection control measures significantly influences both route planning and computational complexity. The proposed approach provides a scalable foundation for future multi-vehicle extensions and cost-based optimization strategies.

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Publication Details

Journal
Communications - Scientific letters of the University of Zilina
Published
2026-01-16
DOI
https://doi.org/10.26552/com.c.2026.008
Primary Topic
Vehicle Routing Optimization Methods
Type
article
Field-Weighted Citation Impact
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article

OPTIMIZING PATIENT TRANSPORT UNDER INFECTION CONTROL CONSTRAINTS: A MILP-BASED DIAL-A-RIDE APPROACH

Tamás Hajba, Adrian Horváth, Martin Tóth, Tamás Hajba et al.
Communications - Scientific letters of the University of Zilina
Vehicle Routing Optimization Methods
article

OPTIMIZING PATIENT TRANSPORT UNDER INFECTION CONTROL CONSTRAINTS: A MILP-BASED DIAL-A-RIDE APPROACH

Tamás Hajba, Adrian Horváth, Martin Tóth, Tamás Hajba, Adrián Horváth, Martin Tóth
article en

Abstract

<br />With aging populations and rising healthcare demands, efficient patient transportation has become a critical challenge, particularly in the context of infection control. In this paper is presented an extended mixed-integer linear programming (MILP) model for optimizing patient transport in urban environments, with a focus on the separate transportation of infectious and non-infectious individuals. The model incorporates time windows, maximum allowable ride durations, and mandatory vehicle disinfection requirements. Experimental results obtained using CPLEX demonstrate that incorporating infection control measures significantly influences both route planning and computational complexity. The proposed approach provides a scalable foundation for future multi-vehicle extensions and cost-based optimization strategies.

Communications - Scientific letters of the University of ZilinaVol. 28(1)
Sustainable cities and communities
Openalex Percentile: Top 88%
Vehicle Routing Optimization Methods
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