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Pagani P, Pfann F (2020). Deep Neural Networks for the Scheduling of Resource-Constrained Activity Sequences: A Preliminary Investigation. Logistics Journal : Proceedings, Vol. 2020. (urn:nbn:de:0009-14-51546)

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%0 Journal Article
%T Deep Neural Networks for the Scheduling of Resource-Constrained Activity Sequences: A Preliminary Investigation
%A Pagani, Paolo
%A Pfann, Fabian
%J Logistics Journal : Proceedings
%D 2020
%V 2020
%N 12
%@ 2192-9084
%F pagani2020
%X The scheduling of activity sequences under resource constraints, also known as Resource-Constrained Project Scheduling Problem (RCPSP), is a well-known optimization problem that consists in finding an activity execution schedule that minimizes the total duration of the considered sequence. This problem is generally tackled with heuristic and meta-heuristic methods. This paper proposes a different approach based on artificial neural networks, used as decision tools, and machine learning. Moreover, it is shown that such methodology is able to provide good and fast activity execution schedules.
%L 620
%K Maschinelles Lernen
%K Planung
%K RCPSP
%K Resource-Constrained Project Scheduling Problem
%K artificial neural networks
%K künstliche neuronale Netze
%K machine learning
%K scheduling
%R 10.2195/lj_Proc_pagani_en_202012_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-51546
%U http://dx.doi.org/10.2195/lj_Proc_pagani_en_202012_01
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@Article{pagani2020,
  author = 	"Pagani, Paolo
		and Pfann, Fabian",
  title = 	"Deep Neural Networks for the Scheduling of Resource-Constrained Activity Sequences: A Preliminary Investigation",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2020",
  volume = 	"2020",
  number = 	"12",
  keywords = 	"Maschinelles Lernen; Planung; RCPSP; Resource-Constrained Project Scheduling Problem; artificial neural networks; k{\"u}nstliche neuronale Netze; machine learning; scheduling",
  abstract = 	"The scheduling of activity sequences under resource constraints, also known as Resource-Constrained Project Scheduling Problem (RCPSP), is a well-known optimization problem that consists in finding an activity execution schedule that minimizes the total duration of the considered sequence. This problem is generally tackled with heuristic and meta-heuristic methods. This paper proposes a different approach based on artificial neural networks, used as decision tools, and machine learning. Moreover, it is shown that such methodology is able to provide good and fast activity execution schedules.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_pagani_en_202012_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-51546"
}
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RIS

TY  - JOUR
AU  - Pagani, Paolo
AU  - Pfann, Fabian
PY  - 2020
DA  - 2020//
TI  - Deep Neural Networks for the Scheduling of Resource-Constrained Activity Sequences: A Preliminary Investigation
JO  - Logistics Journal : Proceedings
VL  - 2020
IS  - 12
KW  - Maschinelles Lernen
KW  - Planung
KW  - RCPSP
KW  - Resource-Constrained Project Scheduling Problem
KW  - artificial neural networks
KW  - künstliche neuronale Netze
KW  - machine learning
KW  - scheduling
AB  - The scheduling of activity sequences under resource constraints, also known as Resource-Constrained Project Scheduling Problem (RCPSP), is a well-known optimization problem that consists in finding an activity execution schedule that minimizes the total duration of the considered sequence. This problem is generally tackled with heuristic and meta-heuristic methods. This paper proposes a different approach based on artificial neural networks, used as decision tools, and machine learning. Moreover, it is shown that such methodology is able to provide good and fast activity execution schedules.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-51546
DO  - 10.2195/lj_Proc_pagani_en_202012_01
ID  - pagani2020
ER  -
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Wordbib

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ISI

PT Journal
AU Pagani, P
   Pfann, F
TI Deep Neural Networks for the Scheduling of Resource-Constrained Activity Sequences: A Preliminary Investigation
SO Logistics Journal : Proceedings
PY 2020
VL 2020
IS 12
DI 10.2195/lj_Proc_pagani_en_202012_01
DE Maschinelles Lernen; Planung; RCPSP; Resource-Constrained Project Scheduling Problem; artificial neural networks; künstliche neuronale Netze; machine learning; scheduling
AB The scheduling of activity sequences under resource constraints, also known as Resource-Constrained Project Scheduling Problem (RCPSP), is a well-known optimization problem that consists in finding an activity execution schedule that minimizes the total duration of the considered sequence. This problem is generally tackled with heuristic and meta-heuristic methods. This paper proposes a different approach based on artificial neural networks, used as decision tools, and machine learning. Moreover, it is shown that such methodology is able to provide good and fast activity execution schedules.
ER
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Mods

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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2020, Iss. 12
Title

Deep Neural Networks for the Scheduling of Resource-Constrained Activity Sequences: A Preliminary Investigation (eng)
Author Paolo Pagani, Fabian Pfann
Language eng
Abstract The scheduling of activity sequences under resource constraints, also known as Resource-Constrained Project Scheduling Problem (RCPSP), is a well-known optimization problem that consists in finding an activity execution schedule that minimizes the total duration of the considered sequence. This problem is generally tackled with heuristic and meta-heuristic methods. This paper proposes a different approach based on artificial neural networks, used as decision tools, and machine learning. Moreover, it is shown that such methodology is able to provide good and fast activity execution schedules. Die Planung von ressourcenbeschränkten Aktivitätsfolgen, bekannt als das ressourcenbeschränkte Projektplanungsproblem, ist ein bekanntes Optimierungsproblem, das darin besteht, einen Ausführungsplan zu finden, der die Gesamtdauer der betrachteten Aktivitätsfolge minimiert. Dieses Problem wird im Allgemeinen mit heuristischen und meta-heuristischen Methoden gelöst. In diesem Beitrag wird ein alternativer Lösungsansatz vorgestellt, der eine Entscheidungsstrategie umfasst, die auf künstlichen neuronalen Netzen und maschinellem Lernen basiert. Darüber hinaus wird gezeigt, dass ein solcher Ansatz in der Lage ist, für Aktivitätsfolgen gute Ausführungspläne in kurzer Zeit zu generieren.
Subject Maschinelles Lernen, Planung, RCPSP, Resource-Constrained Project Scheduling Problem, artificial neural networks, künstliche neuronale Netze, machine learning, scheduling
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-51546
DOI https://doi.org/10.2195/lj_Proc_pagani_en_202012_01