Managing Home Healthcare System Using Capacitated Vehicle Routing Problem with Time Windows: A Case Study in Chiang Mai, Thailand

Sirilak Phonin, Chulin Likasiri, Radom Pongvuthithum () and Kornphong Chonsiripong ()
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Sirilak Phonin: Department of Mathematics, Faculty of Science and Agricultural Technology, Rajamangala University of Technology Lanna Tak, Tak 63000, Thailand
Chulin Likasiri: Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
Radom Pongvuthithum: Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
Kornphong Chonsiripong: Faculty of Science at Sriracha, Kasetsart University, Sriracha Campus, Chonburi 20230, Thailand

Logistics, 2025, vol. 9, issue 3, 1-23

Abstract: Background : The Vehicle Routing Problem with Time Windows (VRPTW) has been extensively researched due to its applicability across various real-world domains, including logistics, healthcare, and distribution systems. With the global elderly population projected to continue increasing, the demand for home healthcare (HHC) services is also on the rise. This work focuses on a specific application within an HHC system, aiming to minimize the total completion time for a fleet of vehicles delivering healthcare services to patients at home. Methods : We propose a mathematical model for the VRPTW, targeting a reduction in both customer and server waiting times on each route and seeking to decrease the total completion time. Our proposed algorithm employs a tabu search to narrow the search space, leveraging a greedy algorithm to establish the tabu list. Results : Our experimental results, based on Solomon’s benchmark datasets, demonstrate that the proposed algorithms achieve optimal solutions, particularly in minimizing total completion time compared to traditional methods, in a case study involving 400 customers where vehicles’ hours are restricted to align with caregivers’ average daily working hours. Conclusions : Our algorithm resulted in a 59% reduction in the number of vehicles required compared to the most recent algorithms, which combine k-mean clustering and local search.

Keywords: vehicle routing problem; time windows; total completion time; home healthcare system (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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