Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8933
Title: The electric home health care routing and scheduling problem with time windows and fast chargers
Authors: Erdem M.
Koç Ç.
Yücel E.
Keywords: Adaptive large neighborhood search
Electric vehicles
Home health care services
Vehicle routing
Benchmarking
Charging (batteries)
Electric vehicles
Heuristic algorithms
Home health care
Optimization
Vehicle routing
Adaptive large neighborhood searches
Energy
Fast chargers
Healthcare services
Home health care service
Routing and scheduling
Routing problems
Scheduling problem
Search heuristics
Time windows
Scheduling
Issue Date: 2022
Publisher: Elsevier Ltd
Abstract: This paper introduces the electric home health care routing and scheduling problem with time windows and fast chargers. The problem aims to construct the daily routes of health care nurses so as to provide a series of services to the patients located at a scattered area. The problem minimizes the total cost, which comprises of total traveling cost of electric vehicles, total cost of uncovered jobs, and total costs of recharged energy. We develop an adaptive large neighborhood search heuristic, which contains a number of advanced efficient procedures tailored to handle specific features of the problem. The paper conducts extensive computational experiments on generated benchmark instances and assesses the competitiveness of the heuristic. Results show that the heuristic is highly effective on the problem. Our analyses quantify the advantages of considering all charger technologies, i.e., normal, fast- and super-fast. We show that the downgrading of competence levels of jobs yields an improvement in total cost. © 2022 Elsevier Ltd
URI: https://doi.org/10.1016/j.cie.2022.108580
https://hdl.handle.net/20.500.11851/8933
ISSN: 0360-8352
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.