A Two-Stage Solution Approach for the Large-Scale Home Healthcare Routeing and Scheduling Problem

Abstract

The purpose of this study is to introduce a two-stage solution approach for a large-scale home healthcare routeing and scheduling problem (HHCRSP). In the first part of the two-stage solution approach, a cluster-assign algorithm is employed, based on the home location and the time to obtain feasible clusters. In the second stage, using these clusters, route construction heuristics start to create schedules and routes, taking the side constraints of the model into consideration. Using the novelty of this two-stage solution approach, higher diversification is achieved with a series of newly-developed cross movement strategies. The computational results show that our solution approach offers certain advantages, such as an increase in the efficient use of human resources, and a decrease in the working time of nurses. OPSOMMINGDie doel van hierdie studie is om ʼn tweeledige oplossingsbenadering vir grootskaalse tuissorg roetebeplanning en skedulering voor te stel vir gesondheidsorg. In die eerste deel van die benadering word ʼn groepering-algoritme, gebaseer op die ligging van die tuiste en die tyd om vatbare groepe te vorm, toegepas. Tydens die tweede stap van die benadering word die groepe gebruik saam met roetebeplanning-heuristiek om skedules en roetes te skep terwyl die randvoor-waardes van die model in ag geneem word. Die tweeledige benadering bewerkstellig hoër diversifikasie met ʼn reeks nuut ontwikkelde kruisbeweging strategieë. Die gesimuleerde resultate toon dat die voorgestelde oplossingsbenadering sekere voordele inhou, onder andere die ʼn verhoging in die effektiewe aanwending van menslikehulpbronne en ʼn afname in die werktyd van verpleegsters.

Home care vehicle routing problem with chargeable overtime and strict and soft preference matching

“…Caregivers have different skills and qualifications, and clients can specify the desire to be assisted by caregivers who meet a specific set of skills. In addition, assignments of caregivers to clients can also be influenced by a number of other characteristics [7][8][9]. For example, [7] dealt with the penalization of assigning a caregiver with specific skills to a client who does not require them, which is seen as a waste of qualifications.…”

“…In the most general case, clients define a time window in which the visit must be performed; within this window, they may also indicate a preferred starting time for the visit. Then, deviations from required time window and preferred start time are usually penalized in the objective function [8,9].…”

“…The total number of worked hours was considered to compute the overtime, neglecting the possibility of clients paying for it. Finally, [9] minimized total time, uncovered visits, overtime work and deviations from the preferred time of jobs.…”

Operational research applied to decisions in home health care: A systematic literature review

“…The problem of service definition and access has similarities to investment portfolio selection: choosing the set of home health care services to offer and identifying the corresponding target patients can be OR approach (number of papers) Solution method (reference) 2017; Shi et al, 2017a) Combination of clustering and variable neighbourhood search (Erdem & Bulkan, 2017) Repeated matching (Eveborn et al, 2006;Eveborn et al, 2009) Tabu search (Liu et al, 2013;Liu et al, 2014;Rest & Hirsch, 2015) Discrete event driven metaheuristics Record-to-record travel algorithm (Hewitt et al, 2016) Harmony search (Lin et al, 2018) Branch and price (Rasmussen et al, 2012;Manerba & Mansini, 2016;Liu et al, 2017) Fuzzy-based particle swarm optimisation algorithm Variable neighbourhood search Nasir & Dang, 2018) Combination of dynamic programming and tabu search (Rest & Hirsch, 2016) Other heuristic algorithm (An et al, 2012;Bard et al, 2013;Bard, Shao, Qi, et al, 2014;Mankowska et al, 2014;Maya Duque et al, 2015;Decerle et al, 2016;Liu et al, 2018) Not specified -commercially available software (Bachouch et al, 2011;Bard et al, 2013;En-Nahli et al, 2015;Nasir & Dang, 2016;Yalçında g et al 2016) Constraint programming (n ¼ 4)…”

The Multi-Period Electric Home Healthcare Routeing and Scheduling Problem With Nonlinear Charging

“…In order to test the algorithm, a set of new test instances is generated, since there are no existing benchmarking instances for the E-HHCRSP-NL. We extend the instances from the single-period HHCRSP work of Hiermann, Prandtstetter, Rendl, Puchinger and Raidl [44] and Erdem and Bulkan [45] to generate a series of small-and large-scale instances from them to maintain the same ratio of the given features (time windows, the characteristics of clients and nurses, the qualification levels of EVs, job specifications). Table A.1 in the Appendix represents the small-and large-scale instances generated.…”