Maximising the net present value of project schedules using CMSA and parallel ACO

Dhananjay Thiruvady; Christian Blum; Andreas T. Ernst

doi:10.1007/978-3-030-05983-5_2

Maximising the net present value of project schedules using CMSA and parallel ACO

Dhananjay Thiruvady, Christian Blum, Andreas T. Ernst

School of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

15 Citations (Scopus)

Abstract

This study considers the problem of resource constrained project scheduling to maximise the net present value. A number of tasks must be scheduled within a fixed time horizon. Tasks may have precedences between them and they use a number of common resources when executing. For each resource, there is a limit, and the cumulative resource requirements of all tasks executing at the same time must not exceed the limits. To solve this problem, we develop a hybrid of Construct, Merge, Solve and Adapt (CMSA) and Ant Colony Optimisation (ACO). The methods are implemented in a parallel setting within a multi-core shared memory architecture. The results show that the proposed algorithm outperforms the previous state-of-the-art method, a hybrid of Lagrangian relaxation and ACO.

Original language	English
Title of host publication	Hybrid Metaheuristics
Subtitle of host publication	11th International Workshop, HM 2019, Proceedings
Editors	Maria J. Blesa Aguilera, Christian Blum, Pedro Pinacho-Davidson, Julio Godoy del Campo, Haroldo Gambini Santos
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	16-30
Number of pages	15
Volume	11299
ISBN (Electronic)	9783030059835
ISBN (Print)	9783030059828
DOIs	https://doi.org/10.1007/978-3-030-05983-5_2
Publication status	Published - 1 Jan 2019
Event	International Workshop on Hybrid Metaheuristics, 2019 - Concepción, Chile Duration: 16 Jan 2019 → 18 Jan 2019 Conference number: 11th http://hm2019.ing.udec.cl/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11299
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Hybrid Metaheuristics, 2019
Abbreviated title	HM 2019
Country/Territory	Chile
City	Concepción
Period	16/01/19 → 18/01/19
Internet address	http://hm2019.ing.udec.cl/

Keywords

Ant Colony Optimisation
Construct
Merge
Net present value
Project scheduling
Solve & Adapt

Access to Document

10.1007/978-3-030-05983-5_2

Cite this

Thiruvady, D., Blum, C., & Ernst, A. T. (2019). Maximising the net present value of project schedules using CMSA and parallel ACO. In M. J. Blesa Aguilera, C. Blum, P. Pinacho-Davidson, J. Godoy del Campo, & H. Gambini Santos (Eds.), Hybrid Metaheuristics: 11th International Workshop, HM 2019, Proceedings (Vol. 11299, pp. 16-30). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11299). Springer. https://doi.org/10.1007/978-3-030-05983-5_2

Thiruvady, Dhananjay ; Blum, Christian ; Ernst, Andreas T. / Maximising the net present value of project schedules using CMSA and parallel ACO. Hybrid Metaheuristics: 11th International Workshop, HM 2019, Proceedings. editor / Maria J. Blesa Aguilera ; Christian Blum ; Pedro Pinacho-Davidson ; Julio Godoy del Campo ; Haroldo Gambini Santos. Vol. 11299 Cham Switzerland : Springer, 2019. pp. 16-30 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This study considers the problem of resource constrained project scheduling to maximise the net present value. A number of tasks must be scheduled within a fixed time horizon. Tasks may have precedences between them and they use a number of common resources when executing. For each resource, there is a limit, and the cumulative resource requirements of all tasks executing at the same time must not exceed the limits. To solve this problem, we develop a hybrid of Construct, Merge, Solve and Adapt (CMSA) and Ant Colony Optimisation (ACO). The methods are implemented in a parallel setting within a multi-core shared memory architecture. The results show that the proposed algorithm outperforms the previous state-of-the-art method, a hybrid of Lagrangian relaxation and ACO.",

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Thiruvady, D, Blum, C & Ernst, AT 2019, Maximising the net present value of project schedules using CMSA and parallel ACO. in MJ Blesa Aguilera, C Blum, P Pinacho-Davidson, J Godoy del Campo & H Gambini Santos (eds), Hybrid Metaheuristics: 11th International Workshop, HM 2019, Proceedings. vol. 11299, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11299, Springer, Cham Switzerland, pp. 16-30, International Workshop on Hybrid Metaheuristics, 2019, Concepción, Chile, 16/01/19. https://doi.org/10.1007/978-3-030-05983-5_2

Maximising the net present value of project schedules using CMSA and parallel ACO. / Thiruvady, Dhananjay; Blum, Christian; Ernst, Andreas T.
Hybrid Metaheuristics: 11th International Workshop, HM 2019, Proceedings. ed. / Maria J. Blesa Aguilera; Christian Blum; Pedro Pinacho-Davidson; Julio Godoy del Campo; Haroldo Gambini Santos. Vol. 11299 Cham Switzerland: Springer, 2019. p. 16-30 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11299).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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T1 - Maximising the net present value of project schedules using CMSA and parallel ACO

AU - Thiruvady, Dhananjay

AU - Blum, Christian

AU - Ernst, Andreas T.

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N2 - This study considers the problem of resource constrained project scheduling to maximise the net present value. A number of tasks must be scheduled within a fixed time horizon. Tasks may have precedences between them and they use a number of common resources when executing. For each resource, there is a limit, and the cumulative resource requirements of all tasks executing at the same time must not exceed the limits. To solve this problem, we develop a hybrid of Construct, Merge, Solve and Adapt (CMSA) and Ant Colony Optimisation (ACO). The methods are implemented in a parallel setting within a multi-core shared memory architecture. The results show that the proposed algorithm outperforms the previous state-of-the-art method, a hybrid of Lagrangian relaxation and ACO.

AB - This study considers the problem of resource constrained project scheduling to maximise the net present value. A number of tasks must be scheduled within a fixed time horizon. Tasks may have precedences between them and they use a number of common resources when executing. For each resource, there is a limit, and the cumulative resource requirements of all tasks executing at the same time must not exceed the limits. To solve this problem, we develop a hybrid of Construct, Merge, Solve and Adapt (CMSA) and Ant Colony Optimisation (ACO). The methods are implemented in a parallel setting within a multi-core shared memory architecture. The results show that the proposed algorithm outperforms the previous state-of-the-art method, a hybrid of Lagrangian relaxation and ACO.

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ER -

Thiruvady D, Blum C, Ernst AT. Maximising the net present value of project schedules using CMSA and parallel ACO. In Blesa Aguilera MJ, Blum C, Pinacho-Davidson P, Godoy del Campo J, Gambini Santos H, editors, Hybrid Metaheuristics: 11th International Workshop, HM 2019, Proceedings. Vol. 11299. Cham Switzerland: Springer. 2019. p. 16-30. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-05983-5_2

Maximising the net present value of project schedules using CMSA and parallel ACO

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Keywords

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