Simulation-based Business Process Evaluation in Home Health Care

Logistics Management

Fabian Lorig

1,2 a

, Colja A. Becker

3 b

, Daniel S. Lebherz

3 c

, Stephanie C. Rodermund

3 d

and Ingo J. Timm

3 e

Department of Computer Science and Media Technology, Malm

o University, Sweden

Internet of Things and People Research Center, Malm

o University, Sweden

Center for Informatics Research and Technology, Trier University, Germany

Keywords:

Home Health Care, Agent-based Social Simulation, Multiagent Systems, Dynamic Microsimulation, Artiﬁcial

Intelligence, Automated Planning and Scheduling, Logistics.

Abstract:

Home health care (HHC) providers face an increasing demand in care services, while the labor market only

offers a limited number of professionals. To cope with this challenge from a HHC provider’s perspective,

available resources must be deployed efﬁciently taking into account individual human needs and desires of

employees as well as customers. On the one hand, corresponding strategic management questions arise,

e.g., distribution or relocation of establishments or expansion of the vehicle ﬂeet. On the other hand, logistical

challenges such as the ﬂexible and robust planning and scheduling of HHC service provision must be addressed

by operational HHC management. This paper targets both perspectives by providing an integrated simulation-

based framework for the evaluation of different business processes. Methods from Agent-based Simulation,

Dynamic Microsimulation, and (Distributed) Artiﬁcial Intelligence are combined to investigate HHC service

provision and to support practical decision-making. The presented approach aims to facilitate the reasonable

development of the HHC provider’s organization to ensure the sustainable delivery of required medical care.

1 INTRODUCTION

Many countries face challenges in coping with in-

creasing demand for care services. In Germany, for

example, the number of people in need of care will

rise by approximately 32% by 2030, resulting in a

growing shortage of care personnel (Rothgang et al.,

2016). Besides stationary facilities and the support

of relatives, home health care (HHC) is one possibil-

ity to receive essential care services. Here, caregivers

are usually equipped with cars and render the required

services at the patients’ homes.

As the availability of qualiﬁed caregivers on the

labor market is very limited, it is not feasible to hire

additional caregivers for coping with the increasing

demand in HHC. Thus, logistical optimization and

managing of existing resources in HHC gains in rele-

https://orcid.org/0000-0002-8209-0921

https://orcid.org/0000-0003-1818-2079

https://orcid.org/0000-0003-0956-4841

https://orcid.org/0000-0002-5168-3564

https://orcid.org/0000-0002-3369-813X

vance to enable efﬁcient service delivery. At the same

time, individual human needs and desires of employ-

ees as well as customers have to be taken into account.

On the one hand, ﬂexible and robust planning and

scheduling of HHC service provision is a challenging

logistical task for operational HHC management. On

the other hand, corresponding strategic management

questions arise, like the distribution or relocation of

local establishments or the expansion the company’s

vehicle ﬂeet. Testing and analyzing different strate-

gies during daily operation can be time consuming,

expensive, and thus economically harmful. To avoid

negative consequences from such real-world investi-

gations, the use of simulation is reasonable. Further,

methods from the ﬁeld of Artiﬁcial Intelligence can

be applied to extend simulation technology and to in-

crease the efﬁciency of operations by applying auto-

mated processes.

This paper combines these techniques and pro-

poses a simulation-based framework for the evalua-

tion and comparison of business processes in HHC

logistics. It can serve as an assistance for HHC

providers to facilitate the development of their orga-

226

Lorig, F., Becker, C., Lebherz, D., Rodermund, S. and Timm, I.

Simulation-based Business Process Evaluation in Home Health Care Logistics Management.

DOI: 10.5220/0009348902260235

In Proceedings of the 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2020), pages 226-235

ISBN: 978-989-758-420-6

nization such that medical care can be ensured in the

long term.

This paper is structured as follows: In Chapter 2,

foundations of HHC and the state of the art in model-

ing, simulating, and assisting HHC processes are pre-

sented. In Chapter 3, the conceptual architecture of

the framework is explained and its application is sub-

ject of Chapter 4. Finally, in Chapter 5, conclusions

are drawn and an outlook on future work is provided.

2 BACKGROUND

This section terminologically introduces the domain

of HHC as a combination of medical and non-medical

care that is rendered by a mobile nursing service at the

patients’ homes. Additionally, this section presents

the state of the art of two research areas that contribute

to the development of the framework, i.e., forecast of

care demand and assistance of operational HHC man-

agement.

2.1 Home Health Care

The provision of HHC services is usually part of the

public health care system. There are national dif-

ferences in HHC systems, yet, they have a common

basis. Independent of a speciﬁc public health care

system, populations can be divided by their individ-

uals’ care status, i.e., care-dependent or not care-

dependent. Care-dependent individuals are referred

to as care recipients and can be classiﬁed by different

levels of care, e.g., depending on the required time

of support. These levels may change, if the con-

dition of the care recipient changes. Additionally,

care recipients can be differentiated by their choice

of care (type of care), that determines how they are

treated. Typical choices are family care, where the

care recipient will be taken care of at home by fam-

ily or friends, nursing care, where the care recipient

moves into a nursing facility, or HHC, where em-

ployees of an ambulatory care service provider (HHC

provider) execute requested care services (service re-

quests). In this work, HHC refers to “the provision of

health care services to people of any age at home or

in other noninstitutional settings” (Dieckmann, 2015,

p. 3). To distinguish skilled medical services and

non-skilled services (such as personal care routines,

household maintenance, and social services), the non-

skilled services are described using the term home

care, in contrast to HHC, which also includes medical

treatments, nursing services, and physical therapies

(Prieto, 2008). Typically, one HHC provider employs

several caregivers with different qualiﬁcations. Care-

givers are equipped with diverse vehicles and render

the service requests in the respective patients’ homes

according to their qualiﬁcations.

2.2 Modeling and Simulation of Care

Demand

The combination of Dynamic Microsimulation (DM)

and Agent-based Social Simulation (ABSS) has been

proposed as a hybrid approach for forecasting indi-

vidual care demand (Lebherz et al., 2018). Seen indi-

vidually, DM and ABSS are well known in the ﬁeld

of care demand analysis or for simulating care deci-

sions. DM allows for simulating the developments

of micro-units, e.g., persons or households, over time

(Li and O’Donoghue, 2013). Here, statistical data and

derived probabilities are used for the estimation of

each micro-unit’s potential future (Rutter et al., 2011).

Decision-making analysis originated in Operations

Research (OR). Usually, the target of OR approaches

is the optimization of decision behavior, e.g., with

multiple objectives (Azc

arate et al., 2008). However,

when reconstructing human decision-making, there is

no need for such optimizations. Instead, sophisticated

methods that include psychological and sociological

theories are required for a realistic reconstruction of

human decision behavior. ABSS enables the inclu-

sion of such methods and thus seems more promising

(Davidsson, 2002; Lorig et al., 2018).

Most ABSS approaches in health care pursue dif-

ferent goals and are suitable for strategic decision

support (Liu and Wu, 2016) or process optimiza-

tion (Moore et al., 2012), e.g., by simulating roles

and activities in health care systems (Mustapha and

Frayret, 2016). Yet, there are also approaches that

focus on the forecast of care demand, e.g., Ma et al.

(Ma et al., 2016). Other approaches combine exist-

ing methods in hybrid simulations, e.g., Bae et al.

(Bae et al., 2016), who combine Agent-based Model-

ing and Microsimulation for studying population dy-

namics. However, for the forecast of individual care

demand, there is no approach that combines meth-

ods for population forecasting, e.g., DM, with so-

phisticated methods for simulating individual human

decision-making, e.g., ABSS. Yet, the combination of

both methods seems promising to take individual de-

cision behavior into account when forecasting future

care demand.

2.3 Supporting Operational HHC

Management

The support of operational management ranges from

basic technologies for carrying out daily tasks to com-

Simulation-based Business Process Evaluation in Home Health Care Logistics Management

227

HHC Provider Care Recipients

Modern HHC

Provision

Prediction of

Individual

Care Demand

KPI

Validation of

Simulation

Models

Data

Empirical

Studies

Care

Statistics

Census

HHC

Provider

DEMAND

INPUT

Figure 1: Conceptual Architecture of the Framework.

prehensive support for difﬁcult decisions using so-

phisticated Decision Support Systems (DSS). Emil-

iano et al. (Emiliano et al., 2017) identiﬁed logis-

tics problems in the domain of HHC and proposed

a framework, which structures management tasks for

the development of a DSS. An especially complex

and recurring management task that is important for

cost reduction is the scheduling and routing of avail-

able resources. Suitable methods for HHC scheduling

and routing include Variable Neighborhood Search

by means of a Mixed-Integer Linear Programming

model (Mankowska et al., 2014) or the use of fuzzy

models for more uncertain scenarios (Shi et al., 2017).

A comprehensive overview on existing approaches

is provided by Fikar and Hirsch (Fikar and Hirsch,

2017).

To make such approaches usable for the opera-

tions manager, Begur et al. (Begur et al., 1997), for

instance, developed a tool to support scheduling and

routing decisions of caregivers. To facilitate capac-

ity planning, Zhang et al. (Zhang et al., 2012) de-

veloped a DSS using optimization and Discrete Event

Simulation with demographic data. Besides this, the

use of methods from the ﬁeld of Artiﬁcial Intelli-

gence is an increasing trend in practical applications.

Becker et al. (Becker et al., 2019) surveyed the use

of multiagent systems and agent-based simulations to

support automated planning and scheduling in oper-

ational HHC logistics management. Discovered ap-

proaches mostly focus on the evaluation of speciﬁc

aspects of HHC logistics management systems, yet,

the study revealed shortcomings in end user provi-

sion, evaluation of developed concepts, and in deal-

ing with a shortage of qualiﬁed workers. Comprehen-

sive approaches that include both demand and supply

modeling were not identiﬁed.

3 EVALUATION FRAMEWORK

FOR HHC BUSINESS

PROCESSES

The framework proposed in this paper conceptualizes

and enables the evaluation of different business pro-

cesses in HHC logistics by means of simulation. It

allows for the long-term evaluation of strategies for

potential future scenarios while taking the possible

inﬂuences of the customers into account. The frame-

work consists of two components: a simulation that

predicts the care demand of individuals and an ap-

proach for the planning and provision of HHC ser-

vices. This section presents a conceptual architecture

in which all required components as well as their in-

teractions are introduced. Moreover, it outlines how

the simulation-based prediction of individual care de-

mand and the approach for modern HHC provision

facilitate the evaluation of business processes of HHC

providers.

3.1 Conceptual Architecture of the

Framework

To evaluate whether or not a speciﬁc business pro-

cess for the delivery of HHC services is economically

viable and satisfactory for care recipients and care-

givers, its present and future suitability must be sys-

tematically assessed under a variety of potential cir-

ICT4AWE 2020 - 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health

228

Census

Care

Statistics

Empirical

Studies

Artificial

Population

Forecast of

population

development

Individual

Care

Decision

Individual Care-Dependency

Order

Data

Individual

Care

Demand

Statistics on

provided

care services

Probabilities

of demand

for specific

care services

HHC

Provider

Data

Forecast of

required

care services

Demand for Specific Care Services

Dynamic

Microsimulation

Agent-Based

Social Simulation

Quantitative

Forecasting

Methods

Probability

Distribution

Fitting

Commissioning

of Care Services

Figure 2: Forecasting Individual Care Demand.

cumstances. To achieve this, the presented framework

(cf. Figure 1) models real-world care recipients, their

demand for care services, and HHC providers that ful-

ﬁll such services. This enables the simulation-based

evaluation of care provision approaches and serves as

an assistance system for HHC providers.

To allow for the investigation and comparison of

different business processes that serve as input of

the proposed framework, their performance must be

quantiﬁed. For this purpose, the framework returns

KPIs, e.g., adherence to schedules or the ratio be-

tween travel and service provision times. By this

means, a more profound decision-making basis is cre-

ated for the management. Yet, more advanced KPIs

such as customer satisfaction must be gathered and

evaluated after implementing the business process in

the real-world system.

Business processes of HHC providers may be dis-

criminated into three hierarchical layers depending on

the goal they pursue and how they deﬁne and struc-

ture the process. On the highest level, the strategic

layer deﬁnes business process based on future de-

velopments. Such strategic decisions might include

investment decisions such as the recruitment of new

staff. The practical implementation of such strategies

is achieved by the tactical layer. Here, the achieve-

ment of the business goal is addressed by algorithms

that, e.g., solve problems of staff allocation. Finally,

the lowest layer can be deﬁned as operational layer

where the business process speciﬁes the short-term

operational control. Such processes, for example, de-

ﬁne weekly schedules that assign each caregiver to

render speciﬁc services for multiple care recipients.

The hierarchical order of these layers also repre-

sents the stepwise implementation and evaluation of

business processes. Management decisions take place

of the strategic layer. Here, the structured and thor-

ough investigation of such processes might for in-

stance require the top-down application of different

scheduling algorithms as well as the simulation-based

analysis of multiple schedules by the framework.

The presented framework pursues an approach

where the individual care demand of a speciﬁc pop-

ulation is predicted by DM and ABSS. Based on this

demand, speciﬁc order data is generated that serves as

input for the provision of the requested services. The

fulﬁllment of the services is then simulated and dif-

ferent KPIs are gathered. As both components require

real-world data, sociodemographic and empirical data

sources are integrated by the framework, e.g., cen-

sus data and operational data from the care provider.

To ensure the generation of reproducible and credible

simulation results, a data-based validation of the sim-

ulation models is also an inherent part of the proposed

framework.

3.2 Prediction of Individual Care

Demand

The prediction of individual care demand, as imple-

mented by the framework, includes the determination

of recent care demand as well as the realistic predic-

tion of future care demand for reliable and sustain-

able planning (cf. Figure 2). This includes the fore-

cast of the number of care-dependent individuals as

well as information about their location and the re-

quested type of care. In previous work, Agent-Based

Microsimulation (ABMS) has been proposed for pre-

dicting future care demand (Lebherz et al., 2018).

Simulation-based Business Process Evaluation in Home Health Care Logistics Management

229

This approach can be used to generate synthetic in-

formation on the amount of HHC-demanding peo-

ple and potential service requests (order data) and al-

lows for the transformation of individual care demand

into speciﬁc service requests for HHC providers. For

the prediction of care demand, two perspectives need

to be considered: The forecast of individual care-

dependency, i.e., the prediction of the amount and lo-

cation of all HHC-demanding people in the investi-

gated area, and forecasts of the demand for speciﬁc

care services, i.e., the prediction of potentially re-

quested services. The combination of both perspec-

tives allows for the determination of the individual

care demand.

Forecasting Individual Care-dependency. This per-

spective allows for determining the number of care

recipients that choose HHC services for their care

support at an arbitrary (future) point in time. Here,

ABMS generates location, level of care, and further

information about the care recipient’s family situa-

tion. ABMS is an iterative approach and consists

of three steps. The ﬁrst step is the generation of

an artiﬁcial population. Sociodemographic and care

statistic data are used to generate an artiﬁcial popu-

lation that matches all required structural characteris-

tics of the real population, e.g., age structure. Further-

more, the combination of map data, census data, and

geo-referencing approaches allows for the allocation

of the artiﬁcial population to real-world households

(Lebherz et al., 2018). In the second step, DM is used

to predict the stepwise development of this popula-

tion into the future (Li and O’Donoghue, 2013). This

allows for the investigation of the demographic de-

velopment as well as of each individuals’ care status,

level of care, and family situation. In the third step,

ABSS is used to simulate individual care decisions

and the preferred type of care for every care depen-

dent person. A simulative decision-making process

based on objective and subjective criteria is used for

analyzing care decisions. Inﬂuences that potentially

affect this decision are implemented as different func-

tions, e.g., saving of cost or social steadiness, which

are objectively interpreted depending on each individ-

uals’ situation. By this means, data about the individ-

ual care level or living situation can be integrated into

the decision-making process. Finally, an individual

subjective assessment of these results allows for de-

riving unique decisions based on the individual per-

son’s characteristics (Lebherz et al., 2018). After a

determined number of iterations, the output is a set

containing information on every care recipient, its de-

mographic characteristics, and its choice of care. For

the application in the presented framework only those

recipients choosing HHC are considered.

Forecasting the Demand for Speciﬁc Care Ser-

vices. The second perspective pursues two different

goals. The ﬁrst one is a realistic assessment of spe-

ciﬁc services that are offered by HHC providers based

on the care recipient’s level of care. To this end, data

of service providers is required about the types of ser-

vices that are requested by care recipients depending

on their level of care. In a following step, quantitative

forecasting methods are used for the prediction of fu-

ture service requests. Based on this forecast, methods

for probability distribution ﬁtting can be used to cal-

culate probabilities of the demand for every speciﬁc

care service (n) combined with every possible level

of care (m). Hence, the ﬁrst result is a n × m matrix

consisting of probabilities for requesting a service.

The second goal is to determine which services

are usually provided by family members or friends

based on the assumption that even if a care recipi-

ent chooses HHC, some tasks are performed by fam-

ily members or friends. Such information must be

gathered through empirical studies, e.g., interviews.

Hence, as a second result of this perspective, prob-

abilities about the professional and non-professional

provision of services must be collected. In summary,

this perspective provides two different results, which

can be combined as probability matrices.

Forecasting Individual Care Demand and Order

Data. The set of HHC recipients combined with

the calculated probability matrices enables the fore-

cast of individual care demands. A random process

is used to assign possible services to the recipients

based on the provided probabilities. The probabilities

regarding the professional or non-professional provi-

sion of services are used in a second random process,

which transforms the demand forecast into realistic

order data for potential HHC providers. Here, ran-

domly chosen services are requested from the service

provider. Final order data consists of a list of HHC re-

cipients with all data provided by ABMS and two in-

dividual sets of services: HHC provider requests and

services provided by family or friends.

3.3 Modern Home Health Care

Logistics Management

Some management tasks in HHC are highly com-

plex or require great coordination effort. The idea of

modern HHC logistics management is to reduce the

range of such tasks by means of innovative informa-

tion systems, to create more space for social-related

tasks concerning customers and employees, like sen-

sitive human-to-human interaction in leading a team.

The vision comprises an Intelligent Assistance Sys-

tem for decision support and a Multiagent Control

ICT4AWE 2020 - 6th International Conference on Information and Communication Technologies for Ageing Well and e-Health

230

Wirtschaftsinformatik I

(Prof. Dr. Timm)

- 1 -

Universität Trier

The Vision: Modern Home Health Care Logistics Management

Model

Simulation-based

Intelligent

Assistance System

Multiagent Control

System

Forecast of

Order Data

HHC

Provider

Input Data

Operations

Manager

Figure 3: Modern HHC Logistics Management.

System for managing employees’ operational activi-

ties during execution. Both parts can be understood

as a mutual complement in practice (cf. Figure 3).

First, the intelligent assistance system is applied for

deﬁning and planning the execution of HHC services.

Afterwards, the multiagent system is used to control

the execution of the planned activities and to adapt the

execution if necessary.

Intelligent Assistance System. The system focuses

on adapting the current way of providing HHC ser-

vices to increase efﬁciency. It can be described as a

comprehensive model-driven DSS with an Intelligent

User Interface. According to Power (Power, 2008),

a model-driven DSS has access to a ﬁnancial, opti-

mization, or simulation model, is able to manipulate

this model, and to analyze the new situation to sup-

port decision makers. The system presented in this

work is based on an agent-based simulation and pro-

vides support by the means of automated simulation

studies. The suggested simulation model consists of

care recipients in their homes and caregivers perform-

ing HHC services using vehicles in an abstract envi-

ronment. To describe individual entities, the model

is capable of representing individual behavior of em-

ployees and customers.

Through manipulation of the modeled (artiﬁcial)

world, different scenarios can be evaluated and the

gained knowledge can be applied to improve real-

world processes. Examined scenarios include differ-

ent possibilities for performing services and the con-

sideration of potential future circumstances, which

are realized by using forecast order data. To manage

data and to control the DSS, an intelligent user inter-

face is applied. Here, the model’s input data and the

higher-level system are administered by a software

agent, which is able to communicate with the oper-

ations manager (user) via different channels. Since

not all employees are familiar with information tech-

nology, this interface is to be understood as an intu-

itive connection to a virtual employee. Accordingly,

the core computational application of the intelligent

assistance system is controlled and conducted by the

software agent. It comprises the design and con-

ducting of simulation experiments as well as output

data analysis as deﬁned in Hypothesis-Driven Simu-

lation Studies (Lorig et al., 2017). Based on the fore-

casted order data and the available resources as input

data, multiple simulation experiments are systemati-

cally designed and executed to identify the most ef-

ﬁcient provision of HHC services. To this end, the

simulation model is modiﬁed accordingly and sev-

eral alternatives of service provision are evaluated.

For instance, speciﬁc combinations of skilled and

non-skilled workers that form a set of teams can be

tested in the artiﬁcial world and KPIs can be measured

to assess their suitability. Furthermore, correspond-

ing planning data for order fulﬁllment and routing is

generated, aggregated, and structured in a schedule,

which can be treated as a separate output of the sys-

tem. Besides the generation of daily schedules, real-

world processes can beneﬁt from using insights from

simulation output data analysis to adapt schedule pat-

terns or adjust involved long-term resources. The lat-

ter, for instance, can be realized by hiring profession-

als with speciﬁc skills according to the analysis re-

sults.

Multiagent Control System. To link the described

model with the real world, the multiagent control sys-

tem uses the model to create a cyber-physical rela-

tion: real-world entities (like employees) are repre-

sented by virtual surrogates in the simulation model

and communication is directed in both ways. Similar

to the application of the model by the intelligent as-

sistance system, the control system creates a virtual

world in a non-terminating simulation run represent-

ing the real world. Deﬁned properties (like geograph-

ical location) of the modeled entities are continuously

synchronized with the values of the real world. Based

on the current simulation state, unavoidable devia-

tions from the schedule can be handled by comput-

ing several opportunities in parallel simulation runs.

These start from the current simulation state, and by

selecting the best alternative to change the current

schedule for the remaining tasks. Because of infor-

Simulation-based Business Process Evaluation in Home Health Care Logistics Management

231

mation exchange and coordination on the virtual level

between the representatives, the respective real-world

participants can reduce communication efforts and are

provided with necessary data and instructions. While

the operations manager is the user of the intelligent

assistance system in the ﬁrst step, he or she is now

part of the multiagent control system itself and repre-

sented by a virtual surrogate, which initiates coordi-

nation tasks like auctions and votes.

This also enables, for instance, the real-time pro-

cessing of urgent customer service requests, which re-

quire the coordination of the consultation with care-

givers. Moreover, customers with an afﬁnity for tech-

nology can be connected to the system by using per-

sonal devices, which eases the sending of such re-

quests. Wearable technologies on the customer side

allow for emergency requests, which can be han-

dled automatically by the multiagent control system.

Changing preferences of the employees (caregivers)

can also trigger coordination processes in a similar

way. In summary, the multiagent control system of-

fers two main functions regarding disturbances of the

previously planned execution of HHC services: On

the one hand, the current real-world status is trans-

ferred to the simulation and can be used for (short-

term) experiments to compute a well-suited solution

to change the current schedule. On the other hand,

coordination and communication between the real-

world participants is carried out by virtual representa-

tives and reduces time and effort among the employ-

ees of the HHC provider.

4 APPLICATION AND

VALIDATION OF THE

FRAMEWORK

To demonstrate the feasibility of the proposed frame-

work, its application is presented in this section. This

includes the identiﬁcation and acquisition of a data

basis, the application of the introduced models for

care demand and decision support, as well as the pre-

sentation of a frame for validating the models.

4.1 Data Basis

Data required for the model application and validation

comes from four sources. Census data represents so-

ciodemographic data and contains information, e.g.,

about ﬁnancial aspects or residential situations. Care

statistics of the respective regions include information

on, e.g., the number of care recipients and their level

of care. To produce reliable results, we use empiri-

cal studies to generate and validate the model behav-

ior. A ﬁrst study is concerned with care recipients

and contains information about different motives to

choose a speciﬁc type of care as well as subjectively

perceived individual conditions. A second study fo-

cuses on care recipients that make use of HHC and

the services they request. HHC provider data con-

tains information such as average duration of differ-

ent services, travel times, and demand for various ser-

vices depending on the level of care. Since the created

population is projected into the future, collected data

must be updated using validated statistical methods

and trend analyses.

4.2 Application of the Care Demand

Model

The care demand model starts with the generation of

an artiﬁcial population. For this purpose, all available

data (e.g., census, land register, or map data) is used to

create a population that matches the real-world popu-

lation on all required characteristics (e.g., care status

and level of care). The iterative ABMS process is ini-

tialized with this population. First, DM projects the

population one year into the future. During this step,

the care level of individuals might change so it is nec-

essary to adapt their type of care. ABSS is then used

to simulate a new care decision for each individual.

First, the given situational context is evaluated and

all objective decisive factors (e.g., monetary or so-

cial criteria) are determined with rating functions for

each potential decision. Following this, an assessment

is performed to derive a reasonable decision depend-

ing on each care recipients objective situation accord-

ing to census data. Income or family members liv-

ing in the same household can inﬂuence the decision.

Here, the same objective situation leads to the same

decision. Hereafter, a subjective assessment is made

based on the care recipient’s individual constitution.

Every care recipient is characterized by an individual

conﬁguration of four motives, represented by social

actor types, i.e., homo economicus, homo sociologi-

cus, identity keeper, emotional man. This allows for

different preferences and interests in decisive factors

(Lorig et al., 2018). For instance, homo economicus

tries to save money and to reduce efforts, while homo

sociologicus lays emphasize on a steady social envi-

ronment and cares less about money.

At the end of this phase, an individual assessment

is made that interprets the objective assessment based

on the recipient’s individual constitution and gener-

ates an updated population. After a predeﬁned num-

ber of iterations, a subset consisting of all care recip-

ients that have HHC as type of care is used for fur-

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232

ther process. Now, every person of this set is assessed

regarding different HHC services, based on a random

process and explored probabilities. A subsequent sec-

ond random process is used for choosing a subset of

these services that will be requested at a professional

HHC provider. Finally, each person of the artiﬁcial

population who choses HHC demands a set of ser-

vices as a set of order data.

4.3 Application of the HHC Model

The order data determines which customer requests

which service during which time interval. The HHC

provider has the option to read in a duty roster for the

considered period of time, so further planning algo-

rithms can use information of employees’ availability.

Both serve as input data for the intelligent assistance

system. Furthermore, overtime hours of employees,

customer geolocation data, and type and quantity of

available vehicles are also required. Before using a

state of the art algorithm for temporal planning, the

system retrieves current or predicted trafﬁc data. As a

ﬁrst step, the algorithm creates a schedule and related

routing data on the basis of the input data. After that,

a simulation run is executed for evaluation using the

agent-based simulation model.

Depending on the HHC provider’s questions, the

design of experiments is conducted which includes

planning of further simulation runs to answer what-if

questions. In addition, experiments can be conducted

to ﬁnd efﬁcient solutions of service delivery. For ex-

ample, an HHC provider wants to examine the impact

of buying an additional vehicle. Another example is

that the operational manager wants to know if cur-

rent processes of service provision are able to cope

with an increasing demand in 5 years, and if not what

are possible adaptions to handle the situation. Finally,

methods of output data analysis are applied to provide

knowledge on a signiﬁcant level. Key performance

indicators can be deﬁned and measured data is aggre-

gated accordingly. If the simulation model represents

the real world with sufﬁcient accuracy, conclusions

for real processes can be drawn.

4.4 Empirical Validation of the Models

Finally, the validity of the simulation framework must

be ensured. Since the models are developed and eval-

uated independently of each other, veriﬁcation and

internal validation are not considered here. To en-

sure credible results, the validity of the models and

some respective results have to be determined. There-

fore, this paper examines the individual components

of the framework, i.e., the decision-making model and

the HHC model, and investigate them for validity us-

ing empirical data. The used data basis consists of

the conducted empirical studies and HHC data as de-

scribed in Section 4.1.

The validity of models can be ensured in vari-

ous ways. For example, face validation is a method

in which experts evaluate the model (Sargent, 2013).

They determine whether the assumptions made dur-

ing concept development are correct and whether the

model is suitable for solving the problem. Further-

more, the model output is checked for appropriateness

for the application area. These methods are subjec-

tive, since the assessment of the model depends on an

expert and his domain knowledge. Objective valida-

tion includes, for example, sensitivity analysis, which

tests the effects of changes in the values of the input

variables on the model output. The framework valida-

tion presented here focuses on comparing the model

output with data of the real world in order to approx-

imate the model output to it and validate the model

(empirical or historical validation). The system out-

put is compared with the test data to determine dif-

ferences, e.g. using statistical methods. This type of

validation is independent of experts or assessments by

third parties and therefore objective (Sargent, 2013).

The arrows from the empirical data basis in Fig-

ure 4 point to the components of the framework to

be validated. First, the decision-making model and

the results that require empirical validation are exam-

ined. Therefore, Section 4.1 presents two empirical

studies. The generation of the initial population and

its projection one year into the future are based on

valid inputs (e.g., census data) and models (cf. Sec-

tion 3.2). Therefore, the decision-making model is the

ﬁrst starting point of an empirical validation. A de-

cision is made using objective and subjective factors

of the agents regarding the type of care. Objective

factors are derived from census data. The subjective

factors are compared with empirical data, so that the

effect of motivations deﬁned in the model can be vali-

dated against reality through target-oriented question-

ing. Furthermore, the waiting functions, whose valid-

ity cannot be guaranteed by statistical methods alone,

have to be validated against empirical data. This con-

cerns functions that work upon subjective perceptions

of care recipients, such as social pressure based on the

experience of the individual. The resulting updated

population can be validated by comparison with esti-

mation models of health statistics.

The next step of the model that has to be valid is

the mapping of services to the agents on the list of

HHC recipients. Here, the second empirical study,

which contains information about services requested

by care recipients, is used. The same applies to the

Simulation-based Business Process Evaluation in Home Health Care Logistics Management

233

Prediction of Individual Care Demand

List of agents with

care level and care

decision

Decision-Making

Model

Input

Output

Empirical Data

Basis

Order Data

HHC Model

List of agents with

home care

Modern HHC Provision

Figure 4: Empirical Validation of the Framework.

next step in the framework which reduces the list of

services which are not rendered by family members

but requested at HHC providers. For these two lists,

both their generation and validation are based on the

same data of the empirical study. This means that

the data is divided into a training set for generation

and a test set for validation. The ﬁnal output of the

model is the order data, which is transferred to the

HHC simulation model. Here, the model itself has

to be validated, in order to produce a reliable output.

Therefore, internal documentation data of the HHC

provider is used. Caregivers use hand-sized devices

to enable electronic documentation while executing

daily tasks. The corresponding data is stored in the

HHC providers’ database. In addition to this, the cor-

responding order data, and the used schedule serve as

model input in order to compare the model output and

the real world documentation data. According to this,

the corresponding time recording is deﬁned as mea-

suring points in the simulation run. If the artiﬁcial

output data matches the gathered real data, the model

is considered to be sufﬁciently valid.

5 CONCLUSIONS AND

OUTLOOK

In this paper, we introduced a framework that allows

HHC agencies to systematically analyze and optimize

business processes with respect to current and future

care demand. To this end, methods from the ﬁelds of

Agent-based Simulation, Dynamic Microsimulation,

and (Distributed) Artiﬁcial Intelligence were com-

bined. Questions ranging from strategic to opera-

tional logistics management were addressed and the

approach’s results can help to increase the efﬁciency

of HHC business processes while at the same time

taking human needs into account.

In future work, we will extend the modeling and

simulation of service provision to IoT-, robotics-, and

qualiﬁcation-based innovations to allow for preinvest-

ment analysis. In long term research, we are working

on combining micro- and agent-based simulation for

validated care demand prognosis. Also, we will fur-

ther elaborate on aspects of AI planning for the practi-

cal application in a multiagent setting as well as on re-

ﬁning knowledge generation and processing. This in-

cludes the development of a system for the automated

design and conducting of simulation experiments of

HHC scenarios.

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