Home Health Care Routing Problem Research Articles

A modified metaheuristic algorithm for a home health care routing problem with health team skill levels

Home Health Care (HHC) services have emerged as a result of the need to provide care to patients/people who cannot be treated in hospital due to their special conditions and/or need home care. The care and treatment processes provided by this service have been appreciated by researchers as they can be less expensive and just as effective than care provided by a hospital or a skilled care facility. HHC gains importance due to the increasing epidemic disease cases, the increase in the elderly population, etc. In this study, it is aimed to provide high quality service as well as minimizing travel costs by considering the constraints related to travel balancing and type of transportation vehicle. So, environmental and economic factors could be included in the model. The proposed Home Health Care Routing Problem with Health Team Skill Level (HTSL-HHCRP) model was coded in GAMS 24.2.1 software. A new hybrid method named as Genetic Algorithm Simulated Annealing-Marine Predators Algorithm (GASA-MPA) has been developed to solve the large dimensions of the problem. In hybrid approach, SA has been used with an initial point obtained by GA. On the other hand, feasibility of the neighborhood solutions is guaranteed by solving a constraint satisfaction problem via MPA. Test problems’ performance was analyzed using the Relative Deviation Index (RDI) evaluation criteria. In addition, the sensitivity of the results according to some parameters selected by scenario analysis was examined. Results confirm the efficiency of the GASA-MPA algorithm in terms of time and objective function. Furthermore, by addressing skill levels of the healthcare team, it is shown that the proposed algorithm performs better than more traditional evolutionary algorithms in HHCRP. The comparisons show that the proposed method outperformed the classical methods not only in terms of best solution, but also the Gap (%) and RDI values.

Home health care routing and scheduling in densely populated communities considering complex human behaviours

This study focuses on the home health care routing problem (HHCRP) in the scenario of high population density areas where many elders live closely together. This study considers two main objectives. The first is to reduce travel and wait times for nurses or elders. The second concerns socially related objectives in scheduling problems, such as ‘quality of life’ and empowerment, by considering assumptions related to the acquaintanceship and mutual preferences of nurses and elders. This study models the effects of mutual preferences and acquaintanceship on service time in HHCRP. We use the Markov decision process and chance-constrained programming (CCP) to model the system to conserve the sequential service provision parameters and better represent the influence of stochastic service times. Because traditional deterministic algorithms cannot solve such a model, we apply a model-free reinforcement learning algorithm, Q-learning (QL), as well as the ant colony optimisation (ACO) algorithm. Thus, we tackle this problem by developing a model and algorithm to solve complex, large-scale systems. This study’s theoretical and practical contributions are verified by feedback from researchers and practitioners.

An exact algorithm for the resource constrained home health care vehicle routing problem

We consider a home health care routing problem with scarce resources which is a variation of the vehicle routing problem with resource constraints. In this problem, the services are provided by nurses and aids at patients’ homes. There are different types of patients that are categorized based on the services they demand and should be serviced by appropriate staff teams or single personnel where the teams are transported by rental vehicles. In this problem, the objective is to minimize the total transportation cost, by considering the patients’ requirements and the qualification of the staff, as well as the resource limitations. In this paper, we present a mathematical formulation of the problem that is modeled similar to a vehicle routing problem and propose a branch-and-price algorithm to solve it. Our proposed algorithm utilizes speed up techniques from the literature to enhance the effectiveness of the proposed solution method. A comprehensive computational study is conducted on Solomon based instances and the results illustrate the efficiency of the algorithm and the effective use of available resources.

Use of Electric Vehicles in Home-Health Care Routing Problems: Analysis of a Multi-objective Approach under Uncertainty

Our aim in this study is to propose a novel approach for optimizing a variant of the home health care routing problem, in which the health-care workers perform a requested number of visits to patients by using electric vehicles. Our approach on the one hand uses multi-objective optimization methods to find optimal planning routes of EVs and on the other hand deals with uncertainty in some major problem constraints. The goal is to help decision makers choosing appropriate routes; while minimising the cost of electric vehicular displacements in terms of: the total travel cost for all home-health workers and the waiting time for all patients. Moreover, we consider in our problem the fuzzy nature of some major constraints.

Analysis of electric vehicles in home health care routing problem

The road transportation is one of the largest contributors to greenhouse gas emissions globally, and rapid urbanisation increases the environmental and economic challenges. Electric vehicles support green supply chain and clean routing operations when compared with the traditional fossil fuel-powered vehicles. This paper analyses a variant of the home health care routing problem in which a group of health care workers performs a requested number of jobs by using electric vehicles. The problem considers multi-depot, heterogeneous fleet, time windows, preferences, competencies, connected activities, the range of electric vehicles, charging status, and charge strategies. We develop a hybrid metaheuristic which successfully combines genetic algorithm and a variable neighbourhood descent, and offer several algorithmic procedures tailored to handle the rich constraints of the problem. Extensive computational experiments on small, medium and large-scale instances have shown that the hybrid metaheuristic is effective on the problem.

Home health care routing problem via off-line learning and neural network

As the aging problem of China worsens, a new industry named ‘home care’ is rising, aiming to handle the ever-growing demand of elderly cares that nursing homes could no longer handle. In the meantime, smart and autonomous decision-making is facing a new explosion recently due to the rise of Machine Learning. Therefore this paper tends to build an algorithm that could autonomously allocate demands to workers in home care scenario following complex constraints, based on off-line learning with a neural network approximator replacing the action-value function. Experimental results on several large scale instances show that the fully connected network can precisely predict the action-value based on full information of the environment, enabling the agent to make decisions that outperform the baseline model and algorithm. In addition, managerial insights are drawn according to the results given by the neural network-based agent. Conclusions and future research directions are discussed as well.

A Lagrangian Relaxation-based Algorithm to Solve a Home Health Care Routing Problem

Nowadays, a rapid growth in the rate of life expectancy can be seen especially in the developed countries. Accordingly, the population of elderlies has been increased. By another point of view, the number of hospitals, retirement homes along with medical staffs has not been grown with a same rate. Hence, Home Health Care (HHC) operations including a set of nurses and patients have been developed recently by both academia and health practitioners to consider elderlies’ preferences willing to receive their cares at their homes instead of hospitals or retirement homes. To alleviate the drawbacks of pervious works and make HHC more practical, this paper introduces not only a new mathematical formulation considering new suppositions in this research area but also a solution approach based on Lagrangian relaxation theory has been employed for the first time. The main strategy of used algorithm aims to fill the gap between the lower bound and upper bound of problem and finds a solution which has both optimality and feasibility properties. By generating a number of numerical examples, results show the performance of the proposed algorithm analyzed by different criteria as well as the efficiency of developed formulation through a set of sensitivity analyses.

A bi-objective green home health care routing problem

Home Health Care (HHC) is generally defined and considered as a set of nurses serving the patients at home by providing the required drugs and other HHC services (e.g. nursing, housekeeping and cleaning etc.). By increasing the life expectancy in the developed countries, the number of hospitals and requirement homes along with medical staffs have not been grown at the same rate. Accordingly, the demand of HHC services is growing, dramatically. According to the literature and the increasing of HHC usage in real world, less attention to environmental pollution or green emission of such systems has been reported. This motivates our attempt to contribute the issue of green HHC among the first studies by developing a bi-objective optimization model. To alleviate the drawbacks of existing solution methodologies in the literature, this work not only uses a number of modification and hybrid versions of recent and old metaheuristics but also develops four fast heuristics. An extensive comparative study confirms the superiority of fourth heuristic along with modification and hybridizing algorithms proposed when solving the large-scale problems as well as the performance of developed model has been evaluated through a set of sensitivity analyses.

Modeling and solving simultaneous delivery and pick-up problem with stochastic travel and service times in home health care

Home Health Care (HHC) companies are widespread in European countries and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs constitute one of the largest forms of expenditure in the HHC industry, it is of importance to research the optimization of the HHC logistics. This paper considers an HHC routing problem with stochastic travel and service times, which comes from the logistics practice of HHC companies. A Stochastic Programming model with Recourse (SPR model) is proposed based on the specific structure of the supply chain. Three series of experiments are carried out to evaluate the model. Firstly, the SPR model is reduced to the deterministic one, and five approaches (Gurobi Solver, Hybrid Genetic Algorithm, Simulated Annealing, Bat Algorithm and Firefly Algorithm) are respectively implemented to solve the deterministic model. The SPR model is finally solved by the proposed SA-based heuristic algorithm. Evaluation of the solutions achieved by the two models highlights the advantages of considering stochastic travel and service times. Finally, a case study is given to explain how the SPR model works for an HHC company. This research will help HHC companies to make appropriate decisions when planning their vehicle routes.

A Home Health Care Routing Problem with Stochastic Travel and Service Time

Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs constitute one of the largest forms of expenditure in the Home Health Care industry, it is of importance to research the optimization of the Home Health Care logistics. This paper considers a Home Health Care Routing Problem with stochastic travel and service time, which comes from the logistics practice of the home health care company. A stochastic programming model with recourse (SPR) is proposed, the Hybrid Genetic Algorithm (HGA) and stochastic simulation method are integrated to solve the proposed model. Three series of experiments are carried out to evaluate the model. Firstly, the SPR model is reduced to the deterministic one, experimental results for the related instances validate the effectiveness and efficiency of the proposed HGA. Then HGA and stochastic simulation are integrated to solve the stochastic model. Comparisons between the solutions obtained by stochastic model and deterministic one validate the reasonable and robustness of considering the stochastic travel and service time. This research will help HHC companies to make appropriate decisions when arranging their vehicle routes.

A hybrid genetic algorithm for a home health care routing problem with time window and fuzzy demand

Home Health Care (HHC) companies are widespread in European countries, and aim to serve patients at home to help them recover from illness and injury in a personal environment. Since transportation costs are among the biggest sources of expenditure in company activities, it is of great significance to optimize this in the Home Health Care industry. From the perspective of optimizing the cost of transportation, this paper studies the Vehicle Routing Scheduling problem as it applies to HHC companies. According to a survey of the HHC companies, during the process of delivering medication drugs, the quantity of drugs required for each patient is non-deterministic when the company makes planned routes. This paper considers uncertain demand as a fuzzy variable, which is closer to a potential real life scenario. A Home Health Care Scheduling Problem with fuzzy demand is considered and a fuzzy chance constraint model is designed. We propose a hybrid genetic algorithm integrated with stochastic simulation methods to solve the proposed model. Firstly, the problem is reduced to the classical vehicle routing problem within a time window. Experimental results for Solomon’s and Homberger’s benchmark instances show that the proposed algorithm performs efficiently. Then other experiments on the fuzzy version model are undertaken with the variable value of the Dispatcher Preference Index (DPI) parameter between [0, 1]. Finally, the influence of DPI on the final objective and the indicators of the problem are discussed using stochastic simulation, and the best value of DPI is obtained. This research will help HHC companies to make appropriate decisions when arranging their vehicle scheduling routes.