Balancing conflicting goals among different stakeholders is a challenging problem in various application domains. In this paper, we analyse it in the context of home healthcare. Fairness objective functions for nurses and patients are combined with system-level governance goals of the territorial centers in charge of the assistance service. From the solution of several multi-objective problems including one objective for each actor hierarchically ordered, the best goal for each stakeholder is identified and interesting managerial conclusions are drawn. To efficiently solve large-size instances, we introduce a parallel Adaptive Large Neighborhood Search algorithm with destroy and repair operators customised to solve multi-objective multi-actor problems. The algorithm proves to be highly efficient and effective when compared to a commercial Mixed Integer Programming solver, either in its plain form or enforced by a mild start and a primal heuristic. Additionally, we devise a metaheuristic method to generate the Pareto frontier of an instance.

Mediating governance goals with patients and nurses satisfaction: a multi-actor multi-objective problem including fairness

Bonomi V.
;
Mansini R.;Zanotti R.
2024-01-01

Abstract

Balancing conflicting goals among different stakeholders is a challenging problem in various application domains. In this paper, we analyse it in the context of home healthcare. Fairness objective functions for nurses and patients are combined with system-level governance goals of the territorial centers in charge of the assistance service. From the solution of several multi-objective problems including one objective for each actor hierarchically ordered, the best goal for each stakeholder is identified and interesting managerial conclusions are drawn. To efficiently solve large-size instances, we introduce a parallel Adaptive Large Neighborhood Search algorithm with destroy and repair operators customised to solve multi-objective multi-actor problems. The algorithm proves to be highly efficient and effective when compared to a commercial Mixed Integer Programming solver, either in its plain form or enforced by a mild start and a primal heuristic. Additionally, we devise a metaheuristic method to generate the Pareto frontier of an instance.
2024
PE1_15 Discrete mathematics and combinatorics
PE1_19 Control theory and optimization
Inglese
Internazionale
62
10
3753
3780
28
Home healthcare; nurse routing problem; fairness measures; multi-objective multi-actor; parallel ALNS; Pareto frontier; >
https://www.tandfonline.com/doi/full/10.1080/00207543.2023.2248522
no
Goal 3: Good health and well-being
Goal 11: Sustainable cities and communities
Goal 10: Reduced inequalities
3
info:eu-repo/semantics/article
262
Bonomi, V.; Mansini, R.; Zanotti, R.
1 Contributo su Rivista::1.1 Articolo in rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/586905
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