Pattern-based decompositions for human resource planning in home health care services

Abstract

Home health care services play a crucial role in reducing the hospitalization costs due to the increase of chronic diseases of elderly people. At the same time, they allow us to improve the quality of life for those patients that receive treatments at their home. Optimization tools are therefore necessary to plan service delivery at patients' homes. Recently, solution methods that jointly address the assignment of the patient to the caregiver (assignment), the definition of the days (pattern) in which caregivers visit the assigned patients (scheduling), and the sequence of visits for each caregiver (routing) have been proposed in the scientific literature. However, the joint consideration of these three levels of decisions may be not affordable for large providers, due to the required computational time. In order to combine the strength and the flexibility guaranteed by a joint assignment, scheduling and routing solution approach with the computational efficiency required for large providers, in this study we propose a new family of two-phase methods that decompose the joint approach by incrementally incorporating some decisions into the first phase. The concept of pattern is crucial to perform such a decomposition in a clever way. Several scenarios are analyzed by changing the way in which resource skills are managed and the optimization criteria adopted to guide the provider decisions. The proposed methods are tested on realistic instances. The numerical experiments help us to identify the combinations of decomposition techniques, skill management policies and optimization criteria that best fit with problem instances of different size

Matching daily home health-care demands with supply in service-sharing platforms

“…These matched tasks may have different priorities ( Cinar et al, 2019 ; M. Lin et al, 2016 ). Each caregiver has a predefined qualification that is a function of the caregiver, sex constraints, or language skills ( Braekers et al, 2016 , Cappanera and Scutellà, 2015 , Cappanera et al, 2018 , Eveborn et al, 2009 , Grenouilleau et al, 2019 , Lin et al, 2018 , Maya Duque et al, 2015 , Mosquera et al, 2019 , Nickel et al, 2012 , Rest and Hirsch, 2016 , Yalçındağ et al, 2016 ). The caregiver’s qualification must match the required qualification for each task that the caregiver is required to perform ( Braekers et al, 2016 , Lin et al, 2018 , Maya Duque et al, 2015 , Mosquera et al, 2019 ).…”

“…The customer surplus and caregiver profit derive from the preferences of both stakeholders and fairness. Few studies consider a single objective such as fairness ( Cappanera and Scutellà, 2015 , Cappanera et al, 2018 ) or cost control ( Yalçındağ et al, 2016 ). Rather, most studies implement multiple objectives with weighted objective functions ( Grenouilleau et al, 2019 , Lin et al, 2018 , Liu et al, 2017 , Rest and Hirsch, 2016 , Trautsamwieser and Hirsch, 2014 , Yuan et al, 2018 ).…”

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)…”

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

“…Some heuristic approaches are developed to solve the problem. Yalçındağ et al proposed a human resource planning model for home health care services. They developed a mathematical model based on a decomposition approach to minimize the maximum operator utilization rate.…”