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SOCIAL AND ECOLOGICAL CAPABILITIES FOR A SUSTAINABLE
HIERARCHICAL PRODUCTION PLANNING
Marco Trost
Prof. Dr. Thorsten Claus
Enrico Teich
Maximilian Selmair
Department of Business Science
Dresden Technical University
Markt 23, 02763 Zittau, Germany
E-mail: marco.trost@mailbox.tu-dresden.de
Prof. Dr. Frank Herrmann
Innovation and Competence Centre for Production
Logistics and Factory Planning (IPF)
Regensburg Technical University of Applied Sciences
KEYWORDS
production planning, hierarchical planning, social vari-
ables, ecological variables, sustainable production plan-
ning, sustainable hierarchical production planning
ABSTRACT
Production planning and production control mainly
focus on optimising the entire production system of a
company. On the basis of hierarchical planning as a
suitable method for solving this task, this paper shows -
besides the economic dimension taken into account so
far - that there are also social and ecological effects
which will have to be considered in the process of plan-
ning. For this purpose, we would like to indicate here
which social and ecological parameters can be or have
already been taken into account for master production
scheduling, for lot sizing and resource scheduling. As a
result, an overview has been created which presents the
existing concepts of sustainable production planning
and production control as well as the existing deficits
regarding the sustainability perspective.
INTRODUCTION
The concepts of hierarchical planning, as put forward
by Hax, and Meal (1975), represent the state of the art
in research and industry. In this paper, we have looked
at hierarchical planning considering the restricted ca-
pacities as suggested by Drexl et al. (1994), with respect
to production planning and production control (PPC).
We distinguish between three planning stages: Master
production scheduling (1), lot sizing (2) and resource
scheduling (3) (refer for figure 1).
The planning approaches applied so far mostly consider
single stages of planning and forbear from considering
an interrelationship in connection with a hierarchical
approach. Because of ecological impacts emanating
from a company’s environment, such as an increased
ecologically motivated demand behaviour, an increasing
shortage of resources and growing waste disposal and
energy costs as well as the rising average age of the
economically active population and the ensuing aggra-
vation of skill shortage, it will also have to be noted that
the aspect of sustainability will gain a high significance
in the future. In this paper, we have therefore examined
existing concepts of sustainability for the various indi-
vidual stages of hierarchical planning and analysed
deficits regarding an integrative concept.
This paper has been structured as follows: Chapter 2
shows the relevance of sustainability. Subsequently,
Chapter 3 looks into master production scheduling and
shows which social and ecological aspects can be con-
sidered at this level of planning. Likewise, batch sizing
and resource scheduling are examined in chapters 4 and
5. The concluding Chapter 6 presents our conclusions as
well as a summary.
Fig. 1: The concept of hierarchical planning (based on
Drexl et al. 1994)
RELEVANCE OF SUSTAINABLE VARIABLES
The idea of sustainability is a frequently discussed no-
tion in science and practice (Andriolo et al. 2014). In
the Brundtland report, this notion has been defined as “a
development that meets the needs of the present without
compromising the ability of future generations to meet
their own needs“, (Brundtland 1987). Any industrial
progress should consist of the elements “economy”,
“ecology” and “social aspects” which need to form a
triad of equivalent priorities since the equal balance of
these elements will become a critical task of sustainable
corporate governance in the future, particularly due to
the aggravation of skill shortage and the rising average
age of the economically active population. In industrial
companies, this task will first and foremost have to be
managed by PPC (Haasis 2008).
Any economic parameters such as storage costs or de-
fault charges are traditional parameters in the field of
PPC. By considering these parameters, companies suc-
ceeded in minimising costs and improving their perfor-
mance. Besides that, the global intertwinement of mar-
kets creates an enormous cost pressure for production
companies, forcing them to become more and more
flexible; as a result of this, methods of dynamic plan-
ning and optimization are required which have to take
uncertainties of the market into account and which pri-
marily permit a sufficient scope of action in terms of
capacity. (Lanza and Peters 2011).
Due to the ongoing climate changes and the resulting
political decisions and requirements, ecological parame-
ters such as efficiency of energy and resources will
however also constitute an important dimension in the
future. In the framework of the climate and energy
package, the EU committed to reduce greenhouse gas
emissions by 20 per cent, to increase the percentage of
renewable energies to 20 per cent of the total energy
demand and to increase energy efficiency by 20 per
cent, by the year 2020 in comparison with a develop-
ment without further efforts to reduce energy consump-
tion. In addition to this, the Paris Agreement concluded
during the United Nations Climate Change Conference
in 2015 provides to curb global warming to a value
significantly below 2 degrees Celcius (if possible 1.5
degrees Celcius). As soon as ecological parameters are
taken into account at all levels of hierarchical planning,
this will provide an enormous potential for supporting
the conformity with these requirements (Müller et al.
2008; Erlach and Westkämper 2009; Vorderwinkler and
Heiß 2011). The Frauenhofer IPT estimated that the
potential energy savings feasible in Germany in the
medium run would range between 25 and 30 per cent
(Drescher and Rohde 2009), where mainly cost savings
would be a critical criterion, in addition to positive ef-
fects on the environment and the climate (Lanza and
Peters 2011), a fact which will put even more emphasis
on the intertwinement of ecological and economical
parameters.
The third dimension to be considered here comprises
social parameters which must always be considered as
soon as human labour is involved. In spite of different
models which are used for work shift planning, job
rotation or staff scheduling, the working conditions do
not improve (Schmucker 2014). Among others, the
report: “DGB-Index Gute Arbeit 2013” (good work
index of the German trade union confederation, 2013)
classified the physical stress and work intensity as
“poor” and even as “lower medium range” (Schmucker
2014). This report shows that 45 per cent of the work
force do not assume to be able to exercise their profes-
sion until they will have reached the age of retirement
(Schmucker 2014). The consequences of increased
stress may be psychosomatic, psychological, or behav-
ioural. As short-term reactions, an increased heart rate
(psychosomatic), frustration (psychological), increased
failure rate (behavioural & individual) or aggressive
behaviour (behavioural & social) can be observed; these
factors lead to increased durations of absence from
work, resignation as well as psychosomatic diseases
(Nerdinger et al. 2014). The general responsibility of
companies for their employees urges them to improve
working conditions; and the fact that the economically
active population decreases, as forecast by the German
Federal Ministry of Labour and Social Affairs, increas-
ingly forces companies to boost the performance poten-
tial of their employees. A mutual solution to these tasks
may for example consist in reducing physical and psy-
chological overstress and in benefiting from learning
effects. In addition to the improvements of the work
environment and the development of carefully adapted
production processes, PPC therefore offers lots of pos-
sibilities for improving their employees’ performance
potential (Vorderwinkler and Heiß 2011).
MASTER PRODUCTION SCHEDULING
Master production scheduling as a central planning
module captures all production segments of a given
production site, as well as its main products and its ag-
gregate capacity requirements. The task to be solved
consists in preparing production programmes over sev-
eral time periods and in coordinating these programmes
between the various production segments. The starting
points of master production scheduling are existing
customer orders as well as short-term demand forecasts
for end products. The resources needed are organised in
groups and units having the same functions and necessi-
tating the same amount of costs (cost centres). The ob-
jective is to minimize the relevant costs incurred in
connection with production, storage and resources on
the basis of the deadlines specified for the target to be
reached (Günther and Tempelmeier 2012).
As a rule, the actual capacity needed per unit of quantity
to be produced is considered as constant. In case of a
purely machine-based production, this assumption is
correct. However as soon as manual processes are used
in a production system, besides the machine-based pro-
cesses, the capacity needed for producing a certain unit
of quantity of a product will also depend on the em-
ployee to whom the job is assigned. However because
of the huge complexity and the generally prevailing
aggregate approach, a detailed human resources plan-
ning is not expedient in the field of master production
scheduling. The objective should rather consist in build-
ing up and maintaining a constant performance level of
the employees, also against the background of social
influences on the production system. This performance
level is determined by the respective employee’s quali-
fication, experience and workload, however in the
framework of master production scheduling, we need to
consider employee groups. A possible clustering may
for example be set up on the basis of various qualifica-
tions.
The nurse-scheduling method for instance considers
social effects. In case of a planning horizon ranging
between one and three months and a period length of
one shift, further parameters are taken into account in
addition to the necessity of covering the capacity re-
quirements with the lowest possible number of employ-
ees. For instance it is imperative to comply with legal
requirements. However any cyclical shift systems which
can be set up easily, as suggested by Warner (1976),
Warner and Prawda (1972) and Miller et al. (1976), are
not sufficient to ensure this. However these models are
characterised by a low flexibility towards fluctuating
capacity requirements, so that dynamic planning alter-
natives such as the model created by Smith and Wiggins
(1977) have to be given preference (Ozkarahan 1989).
There, the individual preferences of employees are addi-
tionally taken into account. Human resources schedul-
ing in general has been analysed by literature for a long
time. The investigations made by Dantzig (1954) and
Edie (1954) were the starting points. The fundamental
objective is to cover the required capacities. Here, legal
requirements of the Law on Working Hours and sto-
chastic influences such as illness and vacation were also
considered. However as a rule, these models assume
given capacity requirements which can only be modi-
fied by means of advance production or subcontracting.
Since the utilisation of learning effects and the reduc-
tion of employees’ stress exposure have a direct impact
on execution times and therefore on the capacity re-
quirements, it is necessary to look at master production
scheduling and human resources planning in an inte-
grated manner. Here, it becomes apparent that there are
various approaches which are based on a group-related
consideration of employees’ preferences, simultaneous-
ly ensuring the availability of required capacities. How-
ever particular approaches considering this explicitly for
the PPC and simultaneously including the interdepend-
encies between the employees assigned to the jobs on
the one hand and the capacity requirements on the other
hand, are still lacking.
Besides capacity requirements, production also gener-
ates a certain energy demand which in turn causes fur-
ther costs. As a rule, the energy price to be considered is
assumed to be constant. However in case of particularly
energy-intensive (high-consumption) productions sys-
tems, it may be advantageous to procure energy on the
basis of individual contracts or from the spot market. In
the future, we will have to expect fluctuating energy
prices. These fluctuations may be of the seasonal or the
intra day type. Because of the change-over to regenera-
tive sources of energy, we will have to expect that the
amplitude of these fluctuations will keep on rising in the
future as witnessed in the past. By planning energy
intensive processes in low price periods, companies
may benefit from the volatility of energy prices and thus
save costs. In addition to variable energy prices, a re-
striction of energy supplies will have to be considered
in the ecological dimension. In the future, it will not be
possible to ensure constant energy supplies with the
help of regenerative sources of energy (e.g. solar and
wind energy) since sufficient energy storage capacities
are still lacking (Laux 2013). If phases of low energy
generation overlap with peaks of demand, bottle necks
will be the result. The idea of integrating these aspects
as early as at the moment of the preparation of the mas-
ter production scheduling has not been considered so
far, a fact which constitutes a respective research task.
LOT SIZING
As a result of the previous planning activities, the data
of net quantities, as required in the specific periods of
time are now available which are used as starting points
of lot sizing. These net quantities may be produced on
the basis of the “just in time” principle, which will
however cause considerable set-up times and therefore
set-up costs. Therefore, the task of lot sizing is to com-
bine these required quantities in reasonable batches. The
increased storage costs caused here lead to a batch size
problem. In addition to this, it is imperative to take
existing sequence relations between subordinate and
superordinate products and the restricted resource ca-
pacities into account, and this will then give rise to a
multi-tiered dynamic batch size problem with capacity
restrictions. This can be represented in a simplified
manner by means of the Multi-Level Capacitated Lot
Sizing Problem (MLCLSP). Explanations in this respect
may be found in Tempelmeier (2008), Tempelmeier
(2012) and Herrmann (2009).
The major part of research work in the field of lot siz-
ing, which is discussed in specialist literature focuses on
optimising economic and ecological target parameters.
In their economic dimension, the conventional target
parameters such as costs of production, ordering, set-up
work, storage and transport are looked at in the context
of the decision to be taken about lot sizes. In their eco-
logical dimension, a predominant part of research work
focuses on different approaches aiming at minimising
carbon dioxide emissions (CO2-emissions) and on the
ensuing costs. Here, the interrelation between batch
size and CO2-emissions, resulting from production,
transport and storage of this batch size is used as the
basis of batch sizing. For example the approaches sug-
gested by Absi et al. (2013) and by Wahab et al. (2011)
envisage minimising the costs of batch-size related
CO2-emissions in connection with transport processes.
Other works enlarged this approach even further by
integrating the batch size-related CO2-emission costs of
storage (Battini et al. 2014, Bouchery et al. 2012). In
case of perishable products, any decisions on batch
sizes, which are based on an overestimation of the fu-
ture product demand may result in the generation of
waste. The disposal of these kinds of waste will also
generate costs in the form of expenses for CO2-
emissions which need to be taken into account by batch
sizing. Approaches in this respect have been provided
by the works of Battini et al. (2014) as well as Bonney
and Jaber (2011). Arslan and Turkay (2013), Benjaafar
et al. (2013), Chen et al. (2013) and Hua et al. (2011)
integrated aspects of CO2-trading (compliance with
emission limits, payment of penalties when emission
limits are exceeded, prices of emission right trading)
into their batch size models and therefore they also aim
at minimizing the costs. Besides CO2 emissions, there
are further ecological target parameters which have not
been taken into account yet by the research done in the
field of batch sizing. As examples, those waste quanti-
ties may be referred to here which are brought about by
batch packaging (discrepancy between batch size and
transport size) or as a result of rejected parts produced
in start-up phases after retrofitting processes initiated by
batch changes. The energy demand depending on batch
sizes in production, storage and transport processes
should be included here as well.
The analysis of the state of science regarding the exist-
ence of methods which also take the social dimension
into account leads us to the research work done by
Arslan and Turkay (2013). In this approach, the man
hours required for producing, transporting and storing
batches are considered as a minimization target; or a
limiting value is specified as a side condition of batch
sizing. Battini et al. (2014) attributed an indirect signif-
icance to the minimisation of transport costs. Since any
minimisation of transport costs mostly goes along with
a reduction of the number of transports, it is possible to
reduce the probability of the occurrence of accidents
and traffic jams. The minimisation of emission costs
also provides an indirect social contribution, since envi-
ronmental pollution will thus be reduced and a contribu-
tion is made to the protection of the environment for the
benefit of the generations to come. A further approach
which has a social dimension besides the economic one
is the work done by Jaber and Bonney (2007). There, a
two-phase model of learning and forgetting is integrated
in a classical EMQ-model (Economic manufacture
quantity model), where effects of learning and forget-
ting are taken into account as a function of batch sizes.
The two-phase model of learning and forgetting is
based on the work submitted by Jaber and Kher (2002),
which splits the process of learning and forgetting into a
cognitive part and a motor skill part. Further significant
social factors which are influenced by the decision on
the batch size are in particular the aspects of work ergo-
nomics. The bigger the size of the batch to be manufac-
tured is, the higher the frequency of identical work steps
will be which production workers will have to perform
repeatedly. This monotony may have a negative impact
on the employees’ performance potential both in physi-
cal and a psychological terms, a fact which will in turn
lead to an increase of stress. An approach aiming at
illustrating the process of exhaustion was presented by
the work of Jaber et al. (2013) which also considered
aspects of exhaustion and recovery in addition to a one-
phase process of learning and forgetting. However this
model was not integrated into a batch size model.
In summary, we need to emphasise that there are differ-
ent approaches which take the economic, ecological and
social dimensions into account, independently to differ-
ent degrees. However a combination of all these ap-
proaches aiming at establishing a really sustainable
model has not been achieved yet. Besides that, these
approaches partially assume unrestricted capacities, a
problem which leads to production schedules that can-
not be implemented in entrepreneurial practice. There-
fore, an extension will be required here (e.g. including
an MLCLSP-model) in addition to a combination of
these approaches.
RESOURCE SCHEDULING
Batch sizing is followed by resource scheduling; here,
the production orders prepared during batch sizing have
to be released on the basis of the previously determined
major deadlines and to be allocated to concrete work
systems. This elucidates the respective interrelationship
between the various planning stages. The length of a
period is reduced to any smaller amount of time and any
time-consuming processes have to be taken into account
(Günther and Tempelmeier 2012). As a possible solu-
tion to this problem, we refer to the Resource Con-
strained Project Scheduling Problem model RCPSP-
model presented by Günther and Tempelmeier (2012).
The field of application of the RCPSP models is wide
and not limited to the original domain of project sched-
uling (Hartmann and Briskorn 2010). Hartmann and
Briskorn (2010) have put various versions of RCPSP
together and classified them. Stadtler (2005) combined
the MLCLSP and the RCPSP and developed an inte-
grated model. However these studies put the focus on
the economic dimension.
At a social level, Boysen and Fliedner (2011) showed
exact and heuristic solutions aiming at reducing the
stress exposure of an airport’s ground. The workload
the ground staff is exposed to depends on the arrival
frequency of aeroplanes so that overwork may occur in
case of a high arrival frequency of large aeroplanes.
Here, the available time window for arrivals is limited
by the earliest and the latest times which result from
flight distances, speed and quantities of aviation fuels.
This may for instance be compared to the planning of
production orders. The equivalents of earliest and latest
time are the major deadlines of batch sizing. As a result,
the work pressure the employees are exposed to may be
reduced by means of an adapted scheduling of the pro-
duction orders. In addition to a possible reduction of
work pressure, Peteghem and Vanhoucke (2015) re-
ferred to significant potentials which become available
as soon as learning effects are considered regarding the
discrete time/resource trade-off scheduling problem
(DTRTP). Particularly the reduction of throughput time
was presented as a significant result. Furthermore, the
authors suggested to consider learning effects in sto-
chastic terms in order to permit an approach that would
be more realistic than deterministic methods. In parallel,
effects of forgetting must also be considered in addition
to learning effects. As regards resource planning, the
consequence is that production orders can be planned in
such a way that their sequence will generate the highest
possible level of learning, that a maximum work load of
the employees is not exceeded and that the production
targets are achieved according to the time schedule. The
available literature offers several approaches to this.
However this approach is still lacking a concrete con-
sideration of social parameters for resource scheduling.
In the ecological dimension, energy savings are possible
when the modes of operation of machines are consid-
ered in resource scheduling (Selmair et al. 2015).
Selmair et al. (2015) explained that energy demand and
operation time, the latter one being based on resource
scheduling, are not directly related which means that
energy demand is not directly proportional to the total
throughput time, a fact which unveils a considerable
potential for optimization. For assessing the energy
demand within the optimization process, a flexible en-
ergy price has to be chosen, as suggested by Selmair et
al. (2015), since as a rule, companies conclude special
contracts which stipulate individual energy prices. In
addition to this, a possible restriction of the energy sup-
ply and of CO2-emissions has to be considered for the
entire planning horizon (for example one day) . In a
future research task, a sustainable model will have to be
developed (e.g an RCPSP-model) which will aim at a
timely achievement of production targets by considering
social effects for a more realistic ascertainment of pro-
cess times and for a reduction of employees’ exposure
to work pressure as well as ecological effects for a re-
duction of energy costs.
CONCLUSION
This paper presented various possibilities which could
generate an improvement both in the social and in the
ecological dimension besides an efficient and timely
achievement of production targets, along with a reduc-
tion of costs. Here; the tasks of PPC have to be fulfilled
by means of hierarchical planning methods, since their
application will avoid the disadvantages occurring in a
simultaneous planning approach such as a lack of avail-
able data. By looking at the three different stages of
hierarchical planning, we have pointed out that various
models for solving the subproblems are presented in
literature; however as a rule these are limited to the
economic dimension.
In the section of master production scheduling we
pointed out that a more sustainable approach could help
obtaining more realistic results and various kinds of cost
savings. In this context, cost savings benefit from fluc-
tuations in energy prices and can also be achieved by
reducing the work pressure employees are exposed to,
since reduced work pressure contributes to reducing the
frequency of work-related diseases, to increasing the
motivation and therefore to achieving lower and more
constant execution times. In the field of batch sizes, we
have shown that some approaches taking the ecological
dimension into account do exist. Furthermore it became
however apparent that these approaches have not been
combined yet in order to create a really sustainable ap-
proach. In addition to this, it will be necessary to con-
vert a combined model into a model which is close to
reality (e.g. MLCLSP). In resource scheduling, we re-
ferred to the RCPSP-model as a multifaceted solution
model. In combination with the approaches suggested
by Selmair et al. (2015) and Boysen and Fliedner
(2011) this will permit reducing work pressure of em-
ployees as well as energy costs. Besides that, a signifi-
cant reduction of throughput times can be achieved as
soon as learning effects are taken into account, as
demonstrated by Peteghem (2015).
In summary, it can be said that, based on the integration
of the ecological and social dimension, hierarchical
panning offers supplementary improvement potentials,
which may yield additional cost savings and considera-
bly contribute to protecting our resources (in social and
ecological terms), besides a timely achievement of the
production targets. However the scientific works pre-
sented here are exclusively approaches of a sustainable
PPC which need to be combined at each level of plan-
ning. Besides that, the concrete interdependencies be-
tween the various planning stages will have to be ana-
lysed explicitly. We also have to point out that the pos-
sibilities indicated here are just mere options for the
time being. The effects and integration possibilities of
these options have to be investigated further.
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AUTHOR BIOGRAPHIES
MARCO TROST is doctoral student at
the Department of Business Science at the
Dresden Technical University and he is
sponsored by the European Social Fund
(ESF). His e-mail address is:
Marco.Trost@mailbox.tu-dresden.de.
PROF. DR. THORSTEN CLAUS holds
the professorship for Production and In-
formation Technology at the International
Institute (IHI) Zittau, a central academic
unit of Dresden Technical University and
he is the director of the International Insti-
tute (IHI) Zittau. His e-mail address is: Thor-
sten.Claus@tu-dresden.de.
PROF. DR. FRANK HERRMANN
holds the professorship for operative pro-
duction planning and control at the OTH
Regensburg and he is the head of the In-
novation and Competence Centre for Pro-
duction Logistics and Factory Planning
(IPF). His e-mail address is: Frank.Herrmann@OTH-
Regensburg.de.
ENRICO TEICH is doctoral student at
the Department of Business Science at the
Dresden Technical University and he is
research associate at the professorship for
Production and Information Technology at
the International Institute (IHI) Zittau, a
central academic unit of Dresden Technical University.
His e-mail address is: Enrico.Teich@tu-dresden.de.
MAXIMILIAN SELMAIR is doctoral
student at the Department of Business
Science at the Dresden Technical Univer-
sity. His e-mail address is: Maximili-
an.Selmair@mailbox.tu-dresden.de
