Staff dimensioning in homecare services with uncertain demands

Abstract

International audienceThe problem addressed in this paper is how to calculate the amount of personnel required to ensure the activity of a home health care (HHC) centre on a tactical horizon. Design of quantitative approaches for this question is challenging. The number of caregivers has to be determined for each profession in order to balance the coverage of patients in a region and the workforce cost over several months. Unknown demand in care and spatial dimensions, combination of skills to cover a care and individual trips visiting patients make the underlaying optimisation problem very hard. Few studies are dedicated to staff dimensioning for HHC compared to patient to nurses assignment/sequencing and centres location problems. We propose an original two-stage approach based on integer linear stochastic programming, which exploits historical medical data. The first stage calculates (near-)optimal levels of resources for possible demand scenarios, while the second stage computes the optimal number of caregiver for each profession to meet a target coverage indicator. For decision-makers, our algorithm gives the number of employees for each category required to satisfy the demand without any recourse (overtime, external resources) with fixed probability and confidence interval. The approach has been tested on various instances built from data of the French agency of hospitalisation data (ATIH)

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

“…Mathematical programming approaches (integer, mixed/integer, stochastic) are also applied in 3 of the 4 studies about team size and composition (Rodriguez et al, 2015;Nasir & Dang, 2018;. In the other paper, Koeleman et al (2012) calculate the impact on workforce size of a Markov decision process framed to give optimal patient admission (screening) policies given the demand for service.…”

“…Variable neighbourhood search (Nasir & Dang, 2018) Branch and cut (Rodriguez et al, 2015) Not specified -commercially available software Home health care plan (n ¼ 0) Decision (number of papers) OR approach (number of papers) Solution method (reference) Combination of tabu search and adaptive large neighbourhood search Stochastic programming (n ¼ 7)…”

A Decision-Making Support System for Operational Coordination of Home Health Care Services

“…To characterize the caregivers hired by the HHC structure, we need their first and last names, their qualifications (i.e. [11] X Cardinality-constrained assignment model Du et al [15] X X Genetic algorithm and local research Shao et al [18] X X X X Mixed-variable programming Yuan et al [22] X X Branch and bound, etiquette algorithm Lanzarone et al [10] X X Markov Chain Mankowska et al [9] X X Heuristic En-nahli et al [5] X X Linear programming in mixed variables Rest and Hirsch [17] X X Tabu research Yuan and Fügenschuh [12] X X Multi-commodity, greedy construction and local search Redjem and Marcon [19] X X Greedy Heuristics Hiermann et al [13] X X Constraint programming, random procedure, memetic algorithm, dispersion search and simulated annealing Rodriguez et al [23] X X X Integer linear stochastic programming Yalçındağ et al [24] X X X Kernel regression, k-nearest neighbor search Maya Duque et al [14] X X Multi-objective approach assistant nurse), the schedules at which they work, which can be personalized (i.e. 8am-3pm and/or 3pm-7:30pm).…”

Heuristic and fuzzy scheduling synergy with knowledge acquisition of resource selection system development for home healthcare service

“…Recently, researchers proposed a hierarchy problem-solving procedure to divide the problem into smaller diverse domains in order to extend the search space and find the global optimal solution for solving larger HSSP (Yalçındağ et al, 2016). A two-stage procedure based on integer linear stochastic programming collaborating with historical data is proposed (Rodriguez et al, 2015). The first step creates possible demand service scenarios while the second stage estimates the optimal number of staff members offering the service.…”