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Instance dataset for resource-constrained project scheduling with diverging material flows.

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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.

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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.