Solving a multi-objective model toward home care staff planning considering cross-training and staff’s preferences by NSGA-II and NRGA

Abstract

Home Care (HC) sta assignment problem is de ned as deciding which sta to assign to each patient. In this study, a multi-objective non-linear mathematical programming model is presented to address sta assignment problem considering crosstraining of caregivers for HC services. The rst objective of the model is to minimize the cost of workload balancing, cross-training, and maintenance. The second objective minimizes the number of employees for each service, while the third objective function maximizes the satisfaction level of caregivers. Several constraints including skill matching, sta preferences, regularity, synchronization, sta absenteeism, and multi-functionality are considered to build a service plan. Due to NP-hardness of the problem, a Non-dominated Sorting Genetic Algorithm (NSGA-II) with a proposed who-rule heuristic initialization procedure is applied. Due to the absence of benchmark available in the literature, a Non-dominated Ranking Genetic Algorithm (NRGA) is employed to validate the obtained results. The data required to run the model are gathered from a real-world HC provider. The results indicate that the proposed NSGA-II is superior to the NRGA with regard to comparison indexes. Based on the results obtained, it is now possible to determine which sta to cross-train for each service and how to assign sta to services.

A sustainable and efficient home health care network design model under uncertainty

“…They use the threshold method to find the optimal solutions for the multi-objective model. Habibnejad-Ledari et al (2019) propose a multi-objective Non-Linear Programming (NLP) model to address staff assignment problems in a home care system. They aim to minimize the costs and employees for each service and maximize the worker satisfaction level.…”

Scheduling and Routing of Dispatching Medical Staff to Homes Healthcare from Different Medical Centers with considering Fairness Policy

“…For example, they suggest scheduling patients according to the rule of the shortest processing time (SPT) in the presence of homogeneous probabilities and variable appointment times. Habibnejad-Ledari et al [20] develop a model for sequential appointment scheduling that considers both patient choice and service fairness at the same time. In stochastic programming, no-show probabilities are assigned to different patients with homogeneous appointment intervals.…”

Synchronized Timetables for Bus Rapid Transit Networks in Small and Large Cities

“…Moreover, thenon-dominated sorting genetic algorithm for multi-objective optimization (NSGA-II) first proposed by Debet al [52,53], is proved to be an efficient solution approach. A similar procedure to what was described is employed in the NSGA-II for a bi-objective problem; however instead of a fitness value for each chromosome, there is a fitness vector which consists of different fitness values of different fitness functions.…”