Data instances of the heterogeneous fleet electric home health care routing and scheduling problem with synchronized demand

Published: 11 September 2023| Version 1 | DOI: 10.17632/4dxm7tvmc9.1
Eda Yucel, Eşref Cebeci,


We used both synthetic data based on the literature and real-world data to generate our benchmark instances. In all instances, nurse working hours start at 8 a.m. and finish at 5 p.m. The fixed cost of a nurse is calculated between 400 and 600 based on the daily salary of a nurse according to Medisozluk (2020), and her/his competency levels. There are two competency types and there are 3 levels for each. Patient locations are generated based on the ”Address Based Population Registration System” database of TUIK (2020), which provides the population data for each province, district, and neighborhood of the city of Ankara province. Two different location selection methods are used to determine the patient locations: (1) random selection, rs in short, and (2) weighted random selection, wrs in short. In rs, patient locations are chosen randomly from the neighborhoods of Ankara. In wrs, patient locations are chosen randomly from the neighborhoods of Ankara based on their populations. The depot location of each vehicle is set as a random one of the public hospitals. Finally, the exact locations of the charging stations are determined by use of Google Maps API (ZES, 2020).The distance matrix is generated based on the Haversine formula (Movable Type Scripts, 2020).The EV fleet has three types of EVs (types a, b, and c) that differ in capacity and energy consumption. Consumption rates of EV type a, b, and c are 0.158, 0.158, and 0.165, respectively. Battery capacities of EV type a, b, and c are 26.8, 32.3, and 52, respectively. The EV speed to be used for travel times is determined as 55 km/h based on the study by (Gupta et al., 2017). In order to serve as many jobs as possible, the cost of not serving a patient job is set to 800. We generated small-, medium- and large-size instances. Small-size instances have 10 jobs, medium- size instances have 20 or 30 jobs, and large-size instances have 40, 50, or 60 jobs. There are 10 instances generated with rs method and 10 instances generated with wrs method for each job set size, up to 30 jobs. The following naming convention is used for the small- and medium-size instances, where “j” refers to the number of jobs, “n” to the number of nurses, “cs” to the number of charging stations, “s” to the number of competency types, “w” to wide time windows for jobs, “t” to narrow time windows for jobs, “sy” to the number of synchronized jobs, “R” to rs method, and “W” to wrs method.In order to analyze the effect of the problem parameters, large-size instances are divided into seven groups as G1, ..., G7.The groups differ in the location selection method (rs or wrs), charger type (super-fast or fast), the existence of competency requirement (1, corresponding to the case where there is one competency type with 3 levels, or 0, corresponding to the case where all jobs require the same competency), the percentage of synchronized jobs among all jobs (0% or 10 %), and job duration (random in [20, 60] or random in [10, 90] minutes)



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