Joint chance constrained probabilistic simple temporal networks via column generation (extended abstract)

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Abstract

Probabilistic Simple Temporal Networks (PSTN) are used to represent scheduling problems under uncertainty. In a temporal network that is strongly controllable (SC) there exists a concrete schedule that is robust to any uncertainty. In this paper we introduce the Joint Chance-Constrained PSTN (JCC-PSTN) which lifts assumptions of independence and Boole's inequality, which are typically leveraged in PSTN literature. We solve the problem of JCC-PSTN SC via a column generation procedure and find that our approach offers on average a 10 times reduction in cost versus using Boole’s inequality.
Original languageEnglish
Number of pages2
Publication statusE-pub ahead of print - 23 Jun 2022
Event19th International Conference on the Integration of Constraint Programmng, Artificial Intelligence, and Operations Research - https://sites.google.com/usc.edu/cpaior-2022, Los Angeles, United States
Duration: 20 Jun 202223 Jun 2022

Conference

Conference19th International Conference on the Integration of Constraint Programmng, Artificial Intelligence, and Operations Research
Abbreviated titleCPAIOR 2022
Country/TerritoryUnited States
CityLos Angeles
Period20/06/2223/06/22

Keywords

  • probabilistic simple temporal networks (PSTN)
  • chance constrained optimisation
  • automated planning and scheduling

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