slapstack-controls 0.1.1

Storage Strategies for the SLAPStack simulation Framework.

SLAPStack-Controls

This package contains several control heuristics associated with the SLAPStack block stacking warehouse (BSW) simulation. The code is hosted together with the simulation on github. SLAPStack contains two use cases, namely WEPAStacks and Crossstacks. See the linked repository for more information.

For WEPAStacks, the following storage location allocation problem (SLAP) strategies were implemented and tested (a comparison of these strategies is available through [3]):

• Closest open pure lane (COPL) and
• Class-based popularity with the following stock keeping unit (SKU) popularity measures:
  • SKU turnover time (indirectly proportional to popularity)
  • The historic number of picks per SKU (directly proportional to popularity)
  • The number of future SKU picks over the next planning period, e.g. week (directly proportional to popularity)
  • The historic SKU throughput calculated as the sum of picks and deliveries per SKU (directly proportional to popularity)
  • The future SKU throughput over the next planning period

For CrossStacks (publication pending), the implemented strategies are:

• Closest to destination (CTD),
• Closest open location (COL),
• Random location (RND), and
• Two dual command cycle inspired heuristics:
  • Closest to the next delivery order (CTNR)
  • Shortest leg (SL)

Note that the CrossStacks SLAP strategies could be applied to the WEPAStacks use case and vice-versa, however this application has not yet been tested.

The following unit load selection problem (ULSP) policies are implemented:

• Batch Last In First Out (BLIFO)

Citing the Project

If you use SLAPStack, WEPAStacks or CrossStacks in your research, you can cite this repository as follows:

@misc{rinciog2023slapstack
    author = {Rinciog, Alexandru and Pfrommer, Jakob and Morrissey Michael 
      and Sohaib Zahid and Vasileva, Anna and Ogorelysheva, Natalia and 
      Rathod, Hardik and Meyer Anne},
    title = {SLAPStack},
    year = {2023},
    publisher = {GitHub},
    journal = {GitHub Repository},
    howpublished = {\url{https://github.com/malerinc/slapstack.git}},
}

References

[1] Pfrommer, J., Meyer, A.: Autonomously organized block stacking warehouses: A review of decision problems and major challenges. Logistics Journal: Proceedings 2020(12) (2020)

[2] Rinciog, A., Meyer, A.: Fabricatio-rl: A reinforcement learning simulation framework for production scheduling. In: 2021 Winter Simulation Conference (WSC). pp. 1–12. IEEE (2021)

[3] Pfrommer, J.; Rinciog, A.; Zahid, S.; Morrissey, M; Meyer A. (2022): SLAPStack: A Simulation Framework and a Large-Scale Benchmark Use Case for Autonomous Block Stacking Warehouses. International Conference on Computational Logistics (ICCL) 2022.