Hyper Temporal Networks

TL;DR

Hyper Temporal Networks (HyTNs) extend Simple Temporal Networks by allowing hyperarc constraints, enabling more expressive temporal modeling while maintaining practical efficiency in consistency checking.

Contribution

The paper introduces HyTNs as a generalization of STNs that can handle disjunctive constraints with efficient consistency algorithms.

Findings

HyTNs can represent complex synchronization constraints.

Practical pseudo-polynomial algorithms exist for HyTNs.

HyTNs are more expressive than traditional STNs.

Abstract

Simple Temporal Networks (STNs) provide a powerful and general tool for representing conjunctions of maximum delay constraints over ordered pairs of temporal variables. In this paper we introduce Hyper Temporal Networks (HyTNs), a strict generalization of STNs, to overcome the limitation of considering only conjunctions of constraints but maintaining a practical efficiency in the consistency check of the instances. In a Hyper Temporal Network a single temporal hyperarc constraint may be defined as a set of two or more maximum delay constraints which is satisfied when at least one of these delay constraints is satisfied. HyTNs are meant as a light generalization of STNs offering an interesting compromise. On one side, there exist practical pseudo-polynomial time algorithms for checking consistency and computing feasible schedules for HyTNs. On the other side, HyTNs offer a more powerful model accommodating natural constraints that cannot be expressed by STNs like Trigger off exactly delta min before (after) the occurrence of the first (last) event in a set., which are used to represent synchronization events in some process aware information systems/workflow models proposed in the literature.