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Published on: October 17, 2016
Instance dataset for resource-constrained project scheduling with diverging material flows
Marco Gehring1, Rebekka Volk1, Frank Schultmann1
1Karlsruhe Institute of Technology, Institute for Industrial Production (IIP), Hertzstr. 16, 76187 Karlsruhe, Germany.
This data article introduces a dataset for the resource-constrained project scheduling problem with cumulative resources (RCPSP/c), crucial for projects like nuclear dismantling. It offers 192 instances and benchmark solutions to advance scheduling method research.
Area of Science:
- Operations Research
- Computer Science
- Engineering Management
Background:
- Project scheduling with diverging material flows presents challenges due to limited processing and storage capacities.
- Nuclear dismantling and deconstruction projects exemplify scenarios with complex material management requirements.
- Existing scheduling models may not fully capture the intricacies of cumulative resource constraints.
Purpose of the Study:
- To introduce a novel dataset for the resource-constrained project scheduling problem with cumulative resources (RCPSP/c).
- To provide a benchmark for evaluating and developing new solution methods for RCPSP/c.
- To facilitate research on scheduling problems involving resource production and consumption.
Main Methods:
- Mathematical formulation of the problem as a resource-constrained project scheduling problem with cumulative resources (RCPSP/c).
- Generation of 192 artificial instances with varying complexities.
- Computation of heuristic solutions for each instance, including different objective function variants.
Main Results:
- A comprehensive dataset of 192 RCPSP/c instances is now available.
- Heuristic solutions and modeling variants are provided for each instance.
- The dataset enables comparative analysis of scheduling algorithms.
Conclusions:
- The dataset serves as a valuable benchmark for the RCPSP/c.
- It supports the evaluation of heuristic and exact methods for complex project scheduling.
- Facilitates advancements in scheduling for industrial deconstruction and material flow management.
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