---
_id: '58650'
abstract:
- lang: eng
text: 'Technical systems are characterized by increasing interdisciplinarity, complexity
and networking. A product and its corresponding production systems require interdisciplinary
multi-objective optimization. Sustainability and recyclability demands increase
said complexity. The efficiency of previously established engineering methods
is reaching its limits, which can only be overcome by systematic integration of
extreme data. The aim of "hybrid decision support" is as follows: Data science
and artificial intelligence should be used to supplement human capabilities in
conjunction with existing heuristics, methods, modeling and simulation to increase
the efficiency of product creation.'
alternative_title:
- Hybride Entscheidungsunterstützung in der Produktentstehung - Mit Data Science und
Künstlicher Intelligenz die Leistungsfähigkeit erhöhen
article_type: original
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Jens
full_name: Pottebaum, Jens
id: '405'
last_name: Pottebaum
orcid: http://orcid.org/0000-0001-8778-2989
- first_name: Peter
full_name: Nyhuis, Peter
last_name: Nyhuis
- first_name: Rainer
full_name: Stark, Rainer
last_name: Stark
- first_name: Klaus-Dieter
full_name: Thoben, Klaus-Dieter
last_name: Thoben
- first_name: Petra
full_name: Wiederkehr, Petra
last_name: Wiederkehr
citation:
ama: Gräßler I, Pottebaum J, Nyhuis P, Stark R, Thoben K-D, Wiederkehr P. Hybrid
Decision Support in Product Creation - Improving performance with data science
and artificial intelligence. Industry 40 Science. 2025;2025(1). doi:10.30844/i4sd.25.1.18
apa: Gräßler, I., Pottebaum, J., Nyhuis, P., Stark, R., Thoben, K.-D., & Wiederkehr,
P. (2025). Hybrid Decision Support in Product Creation - Improving performance
with data science and artificial intelligence. Industry 4.0 Science, 2025(1).
https://doi.org/10.30844/i4sd.25.1.18
bibtex: '@article{Gräßler_Pottebaum_Nyhuis_Stark_Thoben_Wiederkehr_2025, title={Hybrid
Decision Support in Product Creation - Improving performance with data science
and artificial intelligence}, volume={2025}, DOI={10.30844/i4sd.25.1.18},
number={1}, journal={Industry 4.0 Science}, publisher={GITO mbH Verlag}, author={Gräßler,
Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter
and Wiederkehr, Petra}, year={2025} }'
chicago: Gräßler, Iris, Jens Pottebaum, Peter Nyhuis, Rainer Stark, Klaus-Dieter
Thoben, and Petra Wiederkehr. “Hybrid Decision Support in Product Creation - Improving
Performance with Data Science and Artificial Intelligence.” Industry 4.0 Science
2025, no. 1 (2025). https://doi.org/10.30844/i4sd.25.1.18.
ieee: 'I. Gräßler, J. Pottebaum, P. Nyhuis, R. Stark, K.-D. Thoben, and P. Wiederkehr,
“Hybrid Decision Support in Product Creation - Improving performance with data
science and artificial intelligence,” Industry 4.0 Science, vol. 2025,
no. 1, 2025, doi: 10.30844/i4sd.25.1.18.'
mla: Gräßler, Iris, et al. “Hybrid Decision Support in Product Creation - Improving
Performance with Data Science and Artificial Intelligence.” Industry 4.0 Science,
vol. 2025, no. 1, GITO mbH Verlag, 2025, doi:10.30844/i4sd.25.1.18.
short: I. Gräßler, J. Pottebaum, P. Nyhuis, R. Stark, K.-D. Thoben, P. Wiederkehr,
Industry 4.0 Science 2025 (2025).
date_created: 2025-02-15T09:31:30Z
date_updated: 2025-02-15T09:40:52Z
department:
- _id: '152'
doi: 10.30844/i4sd.25.1.18
intvolume: ' 2025'
issue: '1'
keyword:
- AI
- artificial intelligence
- Data Science
- decision support
- extreme data
- Künstliche Intelligenz
- product creation
- product development
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: Industry 4.0 Science
publication_identifier:
issn:
- 2942-6170
publication_status: published
publisher: GITO mbH Verlag
quality_controlled: '1'
status: public
title: Hybrid Decision Support in Product Creation - Improving performance with data
science and artificial intelligence
type: journal_article
user_id: '405'
volume: 2025
year: '2025'
...
---
_id: '56282'
abstract:
- lang: eng
text: "Algorithmic bias has long been recognized as a key problem affecting decision-making
processes that integrate artificial intelligence (AI) technologies. The increased
use of AI in making military decisions relevant to the use of force has sustained
such questions about biases in these technologies and in how human users programme
with and rely on data based on hierarchized socio-cultural norms, knowledges,
and modes of attention.\r\n\r\nIn this post, Dr Ingvild Bode, Professor at the
Center for War Studies, University of Southern Denmark, and Ishmael Bhila, PhD
researcher at the “Meaningful Human Control: Between Regulation and Reflexion”
project, Paderborn University, unpack the problem of algorithmic bias with reference
to AI-based decision support systems (AI DSS). They examine three categories of
algorithmic bias – preexisting bias, technical bias, and emergent bias – across
four lifecycle stages of an AI DSS, concluding that stakeholders in the ongoing
discussion about AI in the military domain should consider the impact of algorithmic
bias on AI DSS more seriously."
author:
- first_name: Ishmael
full_name: Bhila, Ishmael
id: '105772'
last_name: Bhila
- first_name: Ingvild
full_name: Bode, Ingvild
last_name: Bode
citation:
ama: Bhila I, Bode I. The Problem of Algorithmic Bias in AI-Based Military Decision
Support Systems. ICRC Humanitarian Law & Policy Blog; 2024.
apa: Bhila, I., & Bode, I. (2024). The problem of algorithmic bias in AI-based
military decision support systems. ICRC Humanitarian Law & Policy Blog.
bibtex: '@book{Bhila_Bode_2024, title={The problem of algorithmic bias in AI-based
military decision support systems}, publisher={ICRC Humanitarian Law & Policy
Blog}, author={Bhila, Ishmael and Bode, Ingvild}, year={2024} }'
chicago: Bhila, Ishmael, and Ingvild Bode. The Problem of Algorithmic Bias in
AI-Based Military Decision Support Systems. ICRC Humanitarian Law & Policy
Blog, 2024.
ieee: I. Bhila and I. Bode, The problem of algorithmic bias in AI-based military
decision support systems. ICRC Humanitarian Law & Policy Blog, 2024.
mla: Bhila, Ishmael, and Ingvild Bode. The Problem of Algorithmic Bias in AI-Based
Military Decision Support Systems. ICRC Humanitarian Law & Policy Blog,
2024.
short: I. Bhila, I. Bode, The Problem of Algorithmic Bias in AI-Based Military Decision
Support Systems, ICRC Humanitarian Law & Policy Blog, 2024.
date_created: 2024-09-30T11:44:28Z
date_updated: 2024-11-26T09:49:48Z
has_accepted_license: '1'
keyword:
- Algorithmic Bias
- AI
- Decision Support Systems
- Autonomous Weapons Systems
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://blogs.icrc.org/law-and-policy/2024/09/03/the-problem-of-algorithmic-bias-in-ai-based-military-decision-support-systems/
oa: '1'
publication_status: published
publisher: ICRC Humanitarian Law & Policy Blog
related_material:
link:
- relation: confirmation
url: https://blogs.icrc.org/law-and-policy/2024/09/03/the-problem-of-algorithmic-bias-in-ai-based-military-decision-support-systems/
status: public
title: The problem of algorithmic bias in AI-based military decision support systems
type: misc
user_id: '105772'
year: '2024'
...
---
_id: '56477'
abstract:
- lang: eng
text: We describe a prototype of a Clinical Decision Support System (CDSS) that
provides (counterfactual) explanations to support accurate medical diagnosis.
The prototype is based on an inherently interpretable Bayesian network (BN). Our
research aims to investigate which explanations are most useful for medical experts
and whether co-constructing explanations can foster trust and acceptance of CDSS.
author:
- first_name: Felix
full_name: Liedeker, Felix
id: '93275'
last_name: Liedeker
- first_name: Philipp
full_name: Cimiano, Philipp
last_name: Cimiano
citation:
ama: 'Liedeker F, Cimiano P. A Prototype of an Interactive Clinical Decision Support
System with Counterfactual Explanations. In: ; 2023.'
apa: Liedeker, F., & Cimiano, P. (2023). A Prototype of an Interactive Clinical
Decision Support System with Counterfactual Explanations. xAI-2023 Late-breaking
Work, Demos and Doctoral Consortium co-located with the 1st World Conference on
eXplainable Artificial Intelligence (xAI-2023), Lissabon.
bibtex: '@inproceedings{Liedeker_Cimiano_2023, title={A Prototype of an Interactive
Clinical Decision Support System with Counterfactual Explanations}, author={Liedeker,
Felix and Cimiano, Philipp}, year={2023} }'
chicago: Liedeker, Felix, and Philipp Cimiano. “A Prototype of an Interactive Clinical
Decision Support System with Counterfactual Explanations,” 2023.
ieee: F. Liedeker and P. Cimiano, “A Prototype of an Interactive Clinical Decision
Support System with Counterfactual Explanations,” presented at the xAI-2023 Late-breaking
Work, Demos and Doctoral Consortium co-located with the 1st World Conference on
eXplainable Artificial Intelligence (xAI-2023), Lissabon, 2023.
mla: Liedeker, Felix, and Philipp Cimiano. A Prototype of an Interactive Clinical
Decision Support System with Counterfactual Explanations. 2023.
short: 'F. Liedeker, P. Cimiano, in: 2023.'
conference:
end_date: 2023-07-28
location: Lissabon
name: xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with
the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023)
start_date: 2023-07-26
date_created: 2024-10-09T14:50:09Z
date_updated: 2024-10-09T15:04:53Z
department:
- _id: '660'
keyword:
- Explainable AI
- Clinical decision support
- Bayesian network
- Counterfactual explanations
language:
- iso: eng
project:
- _id: '128'
name: 'TRR 318 - C5: TRR 318 - Subproject C5'
status: public
title: A Prototype of an Interactive Clinical Decision Support System with Counterfactual
Explanations
type: conference
user_id: '93275'
year: '2023'
...
---
_id: '29539'
abstract:
- lang: eng
text: Explainable Artificial Intelligence (XAI) is currently an important topic
for the application of Machine Learning (ML) in high-stakes decision scenarios.
Related research focuses on evaluating ML algorithms in terms of interpretability.
However, providing a human understandable explanation of an intelligent system
does not only relate to the used ML algorithm. The data and features used also
have a considerable impact on interpretability. In this paper, we develop a taxonomy
for describing XAI systems based on aspects about the algorithm and data. The
proposed taxonomy gives researchers and practitioners opportunities to describe
and evaluate current XAI systems with respect to interpretability and guides the
future development of this class of systems.
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
citation:
ama: 'Kucklick J-P. Towards a model- and data-focused taxonomy of XAI systems. In:
Wirtschaftsinformatik 2022 Proceedings. ; 2022.'
apa: Kucklick, J.-P. (2022). Towards a model- and data-focused taxonomy of XAI systems.
Wirtschaftsinformatik 2022 Proceedings. Wirtschaftsinformatik 2022 (WI22),
Nürnberg (online).
bibtex: '@inproceedings{Kucklick_2022, title={Towards a model- and data-focused
taxonomy of XAI systems}, booktitle={Wirtschaftsinformatik 2022 Proceedings},
author={Kucklick, Jan-Peter}, year={2022} }'
chicago: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI
Systems.” In Wirtschaftsinformatik 2022 Proceedings, 2022.
ieee: J.-P. Kucklick, “Towards a model- and data-focused taxonomy of XAI systems,”
presented at the Wirtschaftsinformatik 2022 (WI22), Nürnberg (online), 2022.
mla: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI Systems.”
Wirtschaftsinformatik 2022 Proceedings, 2022.
short: 'J.-P. Kucklick, in: Wirtschaftsinformatik 2022 Proceedings, 2022.'
conference:
end_date: 2022-02-23
location: Nürnberg (online)
name: Wirtschaftsinformatik 2022 (WI22)
start_date: 2022-02-21
date_created: 2022-01-26T08:22:03Z
date_updated: 2022-01-26T08:24:30Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence
- XAI
- Interpretability
- Decision Support Systems
- Taxonomy
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1056&context=wi2022
oa: '1'
publication: Wirtschaftsinformatik 2022 Proceedings
status: public
title: Towards a model- and data-focused taxonomy of XAI systems
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '5675'
abstract:
- lang: eng
text: When responding to natural disasters, professional relief units are often
supported by many volunteers which are not affiliated to humanitarian organizations.
The effective coordination of these volunteers is crucial to leverage their capabilities
and to avoid conflicts with professional relief units. In this paper, we empirically
identify key requirements that professional relief units pose on this coordination.
Based on these requirements, we suggest a decision model. We computationally solve
a real-world instance of the model and empirically validate the computed solution
in interviews with practitioners. Our results show that the suggested model allows
for solving volunteer coordination tasks of realistic size near-optimally within
short time, with the determined solution being well accepted by practitioners.
We also describe in this article how the suggested decision support model is integrated
in the volunteer coordination system which we develop in joint cooperation with
a disaster management authority and a software development company.
author:
- first_name: Gerhard
full_name: Rauchecker, Gerhard
last_name: Rauchecker
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
citation:
ama: 'Rauchecker G, Schryen G. Decision Support for the Optimal Coordination of
Spontaneous Volunteers in Disaster Relief. In: Proceedings of the 15th International
Conference on Information Systems for Crisis Response and Management. ; 2018.'
apa: Rauchecker, G., & Schryen, G. (2018). Decision Support for the Optimal
Coordination of Spontaneous Volunteers in Disaster Relief. In Proceedings of
the 15th International Conference on Information Systems for Crisis Response and
Management. Rochester, NY, USA.
bibtex: '@inproceedings{Rauchecker_Schryen_2018, title={Decision Support for the
Optimal Coordination of Spontaneous Volunteers in Disaster Relief}, booktitle={Proceedings
of the 15th International Conference on Information Systems for Crisis Response
and Management}, author={Rauchecker, Gerhard and Schryen, Guido}, year={2018}
}'
chicago: Rauchecker, Gerhard, and Guido Schryen. “Decision Support for the Optimal
Coordination of Spontaneous Volunteers in Disaster Relief.” In Proceedings
of the 15th International Conference on Information Systems for Crisis Response
and Management, 2018.
ieee: G. Rauchecker and G. Schryen, “Decision Support for the Optimal Coordination
of Spontaneous Volunteers in Disaster Relief,” in Proceedings of the 15th International
Conference on Information Systems for Crisis Response and Management, Rochester,
NY, USA, 2018.
mla: Rauchecker, Gerhard, and Guido Schryen. “Decision Support for the Optimal Coordination
of Spontaneous Volunteers in Disaster Relief.” Proceedings of the 15th International
Conference on Information Systems for Crisis Response and Management, 2018.
short: 'G. Rauchecker, G. Schryen, in: Proceedings of the 15th International Conference
on Information Systems for Crisis Response and Management, 2018.'
conference:
location: Rochester, NY, USA
name: 15th International Conference on Information Systems for Crisis Response and
Management
date_created: 2018-11-14T15:35:54Z
date_updated: 2022-01-06T07:02:28Z
ddc:
- '000'
department:
- _id: '277'
extern: '1'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2018-12-07T11:25:06Z
date_updated: 2018-12-13T15:05:44Z
file_id: '6020'
file_name: 2018_ISCRAM_Conference_Proceedings - Publication Version.pdf
file_size: 488472
relation: main_file
file_date_updated: 2018-12-13T15:05:44Z
has_accepted_license: '1'
keyword:
- Coordination of spontaneous volunteers
- volunteer coordination system
- decision support
- scheduling optimization model
- linear programming
language:
- iso: eng
oa: '1'
publication: Proceedings of the 15th International Conference on Information Systems
for Crisis Response and Management
status: public
title: Decision Support for the Optimal Coordination of Spontaneous Volunteers in
Disaster Relief
type: conference
user_id: '61579'
year: '2018'
...
---
_id: '5671'
abstract:
- lang: eng
text: Multi-attribute value theory (MAVT)-based recommender systems have been proposed
for dealing with issues of existing recommender systems, such as the cold-start
problem and changing preferences. However, as we argue in this paper, existing
MAVT-based methods for measuring attribute importance weights do not fit the shopping
tasks for which recommender systems are typically used. These methods assume well-trained
decision makers who are willing to invest time and cognitive effort, and who are
familiar with the attributes describing the available alternatives and the ranges
of these attribute levels. Yet, recommender systems are most often used by consumers
who are usually not familiar with the available attributes and ranges and who
wish to save time and effort. Against this background, we develop a new method,
based on a product configuration process, which is tailored to the characteristics
of these particular decision makers. We empirically compare our method to SWING,
ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory
experiment with 153 participants. Results indicate that our proposed method performs
better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation
accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms
of cognitive load, and that participants were faster with our method than with
any other method. We conclude that our method is a promising option to help support
consumers' decision processes in e-commerce shopping tasks.
author:
- first_name: Michael
full_name: Scholz, Michael
last_name: Scholz
- first_name: Verena
full_name: Dorner, Verena
last_name: Dorner
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
- first_name: Alexander
full_name: Benlian, Alexander
last_name: Benlian
citation:
ama: Scholz M, Dorner V, Schryen G, Benlian A. A configuration-based recommender
system for supporting e-commerce decisions. European Journal of Operational
Research. 2017;259(1):205-215.
apa: Scholz, M., Dorner, V., Schryen, G., & Benlian, A. (2017). A configuration-based
recommender system for supporting e-commerce decisions. European Journal of
Operational Research, 259(1), 205–215.
bibtex: '@article{Scholz_Dorner_Schryen_Benlian_2017, title={A configuration-based
recommender system for supporting e-commerce decisions}, volume={259}, number={1},
journal={European Journal of Operational Research}, publisher={Elsevier}, author={Scholz,
Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander}, year={2017},
pages={205–215} }'
chicago: 'Scholz, Michael, Verena Dorner, Guido Schryen, and Alexander Benlian.
“A Configuration-Based Recommender System for Supporting e-Commerce Decisions.”
European Journal of Operational Research 259, no. 1 (2017): 205–15.'
ieee: M. Scholz, V. Dorner, G. Schryen, and A. Benlian, “A configuration-based recommender
system for supporting e-commerce decisions,” European Journal of Operational
Research, vol. 259, no. 1, pp. 205–215, 2017.
mla: Scholz, Michael, et al. “A Configuration-Based Recommender System for Supporting
e-Commerce Decisions.” European Journal of Operational Research, vol. 259,
no. 1, Elsevier, 2017, pp. 205–15.
short: M. Scholz, V. Dorner, G. Schryen, A. Benlian, European Journal of Operational
Research 259 (2017) 205–215.
date_created: 2018-11-14T15:06:18Z
date_updated: 2022-01-06T07:02:27Z
ddc:
- '000'
department:
- _id: '277'
extern: '1'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2018-12-07T11:30:59Z
date_updated: 2018-12-13T15:06:56Z
file_id: '6025'
file_name: EJOR article.pdf
file_size: 762889
relation: main_file
file_date_updated: 2018-12-13T15:06:56Z
has_accepted_license: '1'
intvolume: ' 259'
issue: '1'
keyword:
- E-Commerce
- Recommender System
- Attribute Weights
- Configuration System
- Decision Support
language:
- iso: eng
oa: '1'
page: 205 - 215
publication: European Journal of Operational Research
publisher: Elsevier
status: public
title: A configuration-based recommender system for supporting e-commerce decisions
type: journal_article
user_id: '61579'
volume: 259
year: '2017'
...
---
_id: '5678'
abstract:
- lang: eng
text: Many academic disciplines - including information systems, computer science,
and operations management - face scheduling problems as important decision making
tasks. Since many scheduling problems are NP-hard in the strong sense, there is
a need for developing solution heuristics. For scheduling problems with setup
times on unrelated parallel machines, there is limited research on solution methods
and to the best of our knowledge, parallel computer architectures have not yet
been taken advantage of. We address this gap by proposing and implementing a new
solution heuristic and by testing different parallelization strategies. In our
computational experiments, we show that our heuristic calculates near-optimal
solutions even for large instances and that computing time can be reduced substantially
by our parallelization approach.
author:
- first_name: Gerhard
full_name: Rauchecker, Gerhard
last_name: Rauchecker
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
citation:
ama: 'Rauchecker G, Schryen G. High-Performance Computing for Scheduling Decision
Support: A Parallel Depth-First Search Heuristic. In: Australasian Conference
on Information Systems. ; 2015:1-13.'
apa: 'Rauchecker, G., & Schryen, G. (2015). High-Performance Computing for Scheduling
Decision Support: A Parallel Depth-First Search Heuristic. In Australasian
Conference on Information Systems (pp. 1–13).'
bibtex: '@inproceedings{Rauchecker_Schryen_2015, title={High-Performance Computing
for Scheduling Decision Support: A Parallel Depth-First Search Heuristic}, booktitle={Australasian
Conference on Information Systems}, author={Rauchecker, Gerhard and Schryen, Guido},
year={2015}, pages={1–13} }'
chicago: 'Rauchecker, Gerhard, and Guido Schryen. “High-Performance Computing for
Scheduling Decision Support: A Parallel Depth-First Search Heuristic.” In Australasian
Conference on Information Systems, 1–13, 2015.'
ieee: 'G. Rauchecker and G. Schryen, “High-Performance Computing for Scheduling
Decision Support: A Parallel Depth-First Search Heuristic,” in Australasian
Conference on Information Systems, 2015, pp. 1–13.'
mla: 'Rauchecker, Gerhard, and Guido Schryen. “High-Performance Computing for Scheduling
Decision Support: A Parallel Depth-First Search Heuristic.” Australasian Conference
on Information Systems, 2015, pp. 1–13.'
short: 'G. Rauchecker, G. Schryen, in: Australasian Conference on Information Systems,
2015, pp. 1–13.'
date_created: 2018-11-14T15:39:50Z
date_updated: 2022-01-06T07:02:30Z
ddc:
- '000'
department:
- _id: '277'
extern: '1'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2018-12-07T11:40:18Z
date_updated: 2018-12-13T15:08:28Z
file_id: '6031'
file_name: ACIS_2015_paper_7.pdf
file_size: 6771871
relation: main_file
file_date_updated: 2018-12-13T15:08:28Z
has_accepted_license: '1'
keyword:
- scheduling
- decision support
- heuristic
- high performance computing
- parallel algorithms
language:
- iso: eng
oa: '1'
page: 1-13
publication: Australasian Conference on Information Systems
status: public
title: 'High-Performance Computing for Scheduling Decision Support: A Parallel Depth-First
Search Heuristic'
type: conference
user_id: '61579'
year: '2015'
...
---
_id: '5685'
abstract:
- lang: eng
text: In double-sided markets for computing resources an optimal allocation schedule
among job offers and requests subject to relevant capacity constraints can be
determined. With increasing storage demands and emerging storage services the
question how to schedule storage jobs becomes more and more interesting. Since
such scheduling problems are often in the class NP-complete an exact computation
is not feasible in practice. On the other hand an approximation to the optimal
solution can easily be found by means of using heuristics. The problem with this
attempt is that the suggested solution may not be exactly optimal and is thus
less satisfying. Considering the two above mentioned solution approaches one can
clearly find a trade-off between the optimality of the solution and the efficiency
to get to a solution at all. This work proposes to apply and combine heuristics
in optimization to gain from both of their benefits while reducing the problematic
aspects. Following this method it is assumed to get closer to the optimal solution
in a shorter time compared to a full optimization.
author:
- first_name: Josef
full_name: Finkbeiner, Josef
last_name: Finkbeiner
- first_name: Christian
full_name: Bodenstein, Christian
last_name: Bodenstein
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
- first_name: Dirk
full_name: Neumann, Dirk
last_name: Neumann
citation:
ama: 'Finkbeiner J, Bodenstein C, Schryen G, Neumann D. Applying heuristic methods
for job scheduling in storage markets. In: 18th European Conference on Information
Systems (ECIS 2010). ; 2010.'
apa: Finkbeiner, J., Bodenstein, C., Schryen, G., & Neumann, D. (2010). Applying
heuristic methods for job scheduling in storage markets. In 18th European Conference
on Information Systems (ECIS 2010).
bibtex: '@inproceedings{Finkbeiner_Bodenstein_Schryen_Neumann_2010, title={Applying
heuristic methods for job scheduling in storage markets}, booktitle={18th European
Conference on Information Systems (ECIS 2010)}, author={Finkbeiner, Josef and
Bodenstein, Christian and Schryen, Guido and Neumann, Dirk}, year={2010} }'
chicago: Finkbeiner, Josef, Christian Bodenstein, Guido Schryen, and Dirk Neumann.
“Applying Heuristic Methods for Job Scheduling in Storage Markets.” In 18th
European Conference on Information Systems (ECIS 2010), 2010.
ieee: J. Finkbeiner, C. Bodenstein, G. Schryen, and D. Neumann, “Applying heuristic
methods for job scheduling in storage markets,” in 18th European Conference
on Information Systems (ECIS 2010), 2010.
mla: Finkbeiner, Josef, et al. “Applying Heuristic Methods for Job Scheduling in
Storage Markets.” 18th European Conference on Information Systems (ECIS 2010),
2010.
short: 'J. Finkbeiner, C. Bodenstein, G. Schryen, D. Neumann, in: 18th European
Conference on Information Systems (ECIS 2010), 2010.'
date_created: 2018-11-14T15:45:11Z
date_updated: 2022-01-06T07:02:32Z
ddc:
- '000'
department:
- _id: '277'
extern: '1'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2018-12-11T15:21:06Z
date_updated: 2018-12-13T15:19:40Z
file_id: '6188'
file_name: s1-ln7055316-1881058806-1939656818Hwf-1884822883IdV-5442784107055316PDF_HI0001.pdf
file_size: 171336
relation: main_file
file_date_updated: 2018-12-13T15:19:40Z
has_accepted_license: '1'
keyword:
- Decision Support System
- Algorithms
- Optimization
- Market Engineering
language:
- iso: eng
oa: '1'
publication: 18th European Conference on Information Systems (ECIS 2010)
status: public
title: Applying heuristic methods for job scheduling in storage markets
type: conference
user_id: '61579'
year: '2010'
...