Details
| Original language | English |
|---|---|
| Pages (from-to) | 329-356 |
| Number of pages | 28 |
| Journal | Business Research |
| Volume | 11 |
| Issue number | 2 |
| Early online date | 22 May 2018 |
| Publication status | Published - Sept 2018 |
Abstract
We consider a novel generalization of the resource-constrained project scheduling problem (RCPSP). Unlike many established approaches for the RCPSP that aim to minimize the makespan of the project for given static capacity constraints, we consider the important real-life aspect that capacity constraints can often be systematically modified by temporarily assigning costly additional production resources or using overtime. We, furthermore, assume that the revenue of the project decreases as its makespan increases and try to find a schedule with a profit-maximizing makespan. Like the RCPSP, the problem is NP-hard, but unlike the RCPSP, it turns out that an optimal schedule does not have to be among the set of so-called active schedules. Scheduling such a project is a formidable task, both from a practical and a theoretical perspective. We develop, describe, and evaluate alternative solution encodings and schedule decoding mechanisms to solve this problem within a genetic algorithm framework and we compare the solutions obtained to both optimal reference values and the results of a commercial local search solver called LocalSolver.
Keywords
- Encodings, Genetic algorithm, Heuristics, Local-search, Overtime, Project scheduling, RCPSP
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Business, Management and Accounting (miscellaneous)
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In: Business Research, Vol. 11, No. 2, 09.2018, p. 329-356.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Profit-oriented scheduling of resource-constrained projects with flexible capacity constraints
AU - Schnabel, André
AU - Kellenbrink, Carolin
AU - Helber, Stefan
N1 - Funding Information: Acknowledgements The authors thank the German Research Foundation (DFG) for financial support of this research project in the CRC 871 ‘‘Regeneration of complex durable goods’’.
PY - 2018/9
Y1 - 2018/9
N2 - We consider a novel generalization of the resource-constrained project scheduling problem (RCPSP). Unlike many established approaches for the RCPSP that aim to minimize the makespan of the project for given static capacity constraints, we consider the important real-life aspect that capacity constraints can often be systematically modified by temporarily assigning costly additional production resources or using overtime. We, furthermore, assume that the revenue of the project decreases as its makespan increases and try to find a schedule with a profit-maximizing makespan. Like the RCPSP, the problem is NP-hard, but unlike the RCPSP, it turns out that an optimal schedule does not have to be among the set of so-called active schedules. Scheduling such a project is a formidable task, both from a practical and a theoretical perspective. We develop, describe, and evaluate alternative solution encodings and schedule decoding mechanisms to solve this problem within a genetic algorithm framework and we compare the solutions obtained to both optimal reference values and the results of a commercial local search solver called LocalSolver.
AB - We consider a novel generalization of the resource-constrained project scheduling problem (RCPSP). Unlike many established approaches for the RCPSP that aim to minimize the makespan of the project for given static capacity constraints, we consider the important real-life aspect that capacity constraints can often be systematically modified by temporarily assigning costly additional production resources or using overtime. We, furthermore, assume that the revenue of the project decreases as its makespan increases and try to find a schedule with a profit-maximizing makespan. Like the RCPSP, the problem is NP-hard, but unlike the RCPSP, it turns out that an optimal schedule does not have to be among the set of so-called active schedules. Scheduling such a project is a formidable task, both from a practical and a theoretical perspective. We develop, describe, and evaluate alternative solution encodings and schedule decoding mechanisms to solve this problem within a genetic algorithm framework and we compare the solutions obtained to both optimal reference values and the results of a commercial local search solver called LocalSolver.
KW - Encodings
KW - Genetic algorithm
KW - Heuristics
KW - Local-search
KW - Overtime
KW - Project scheduling
KW - RCPSP
UR - http://www.scopus.com/inward/record.url?scp=85058507779&partnerID=8YFLogxK
U2 - 10.1007/s40685-018-0063-5
DO - 10.1007/s40685-018-0063-5
M3 - Article
VL - 11
SP - 329
EP - 356
JO - Business Research
JF - Business Research
SN - 2198-3402
IS - 2
ER -