Rights statement: This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 373, 2016 DOI: 10.1016/j.ins.2016.09.010
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem. / Asta, Shahriar; Karapetyan, Daniel; Kheiri, Ahmed et al.
In: Information Sciences, Vol. 373, 10.12.2016, p. 476-498.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem
AU - Asta, Shahriar
AU - Karapetyan, Daniel
AU - Kheiri, Ahmed
AU - Özcan, Ender
AU - Parkes, Andrew J.
N1 - This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 373, 2016 DOI: 10.1016/j.ins.2016.09.010
PY - 2016/12/10
Y1 - 2016/12/10
N2 - Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully-designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of “hidden” instances, i.e. instances not available at the algorithm design phase.
AB - Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully-designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of “hidden” instances, i.e. instances not available at the algorithm design phase.
KW - Hybrid heuristics
KW - Hyper-heuristics
KW - Metaheuristics
KW - Monte Carlo tree search
KW - Multi-project scheduling
KW - Permutation based local search
U2 - 10.1016/j.ins.2016.09.010
DO - 10.1016/j.ins.2016.09.010
M3 - Journal article
AN - SCOPUS:84987861878
VL - 373
SP - 476
EP - 498
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
ER -