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A Digital Twin Approach for Adaptive Compliance in Cyber-Physical Systems: Case of Smart Warehouse Logistics

Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos

TL;DR

The novel contribution of this work is a Digital Twin-oriented architecture for adaptive compliance leveraging abstract goal modelling, fine-grained agent-based modelling and runtime simulation for managing compliance trade-offs.

Abstract

Engineering regulatory compliance in complex Cyber-Physical Systems (CPS), such as smart warehouse logistics, is challenging due to the open and dynamic nature of these systems, scales, and unpredictable modes of human-robot interactions that can be best learnt at runtime. Traditional offline approaches for engineering compliance often involve modelling at a higher, more abstract level (e.g. using languages like SysML). These abstract models only support analysis in offline-designed and simplified scenarios. However, open and complex systems may be unpredictable, and their behaviours are difficult to be fully captured by abstract models. These systems may also involve other business goals, possibly conflicting with regulatory compliance. To overcome these challenges, fine-grained simulation models are promising to complement abstract models and support accurate runtime predictions and performance evaluation with trade-off analysis. The novel contribution of this work is a Digital Twin-oriented architecture for adaptive compliance leveraging abstract goal modelling, fine-grained agent-based modelling and runtime simulation for managing compliance trade-offs. A case study from smart warehouse logistics is used to demonstrate the approach considering safety and productivity trade-offs.

A Digital Twin Approach for Adaptive Compliance in Cyber-Physical Systems: Case of Smart Warehouse Logistics

TL;DR

The novel contribution of this work is a Digital Twin-oriented architecture for adaptive compliance leveraging abstract goal modelling, fine-grained agent-based modelling and runtime simulation for managing compliance trade-offs.

Abstract

Engineering regulatory compliance in complex Cyber-Physical Systems (CPS), such as smart warehouse logistics, is challenging due to the open and dynamic nature of these systems, scales, and unpredictable modes of human-robot interactions that can be best learnt at runtime. Traditional offline approaches for engineering compliance often involve modelling at a higher, more abstract level (e.g. using languages like SysML). These abstract models only support analysis in offline-designed and simplified scenarios. However, open and complex systems may be unpredictable, and their behaviours are difficult to be fully captured by abstract models. These systems may also involve other business goals, possibly conflicting with regulatory compliance. To overcome these challenges, fine-grained simulation models are promising to complement abstract models and support accurate runtime predictions and performance evaluation with trade-off analysis. The novel contribution of this work is a Digital Twin-oriented architecture for adaptive compliance leveraging abstract goal modelling, fine-grained agent-based modelling and runtime simulation for managing compliance trade-offs. A case study from smart warehouse logistics is used to demonstrate the approach considering safety and productivity trade-offs.
Paper Structure (24 sections, 6 equations, 10 figures)

This paper contains 24 sections, 6 equations, 10 figures.

Table of Contents

  1. Introduction
  2. Challenges
  3. Compliance and quality goals
  4. Limited design effort and knowledge
  5. Dynamic runtime behaviour
  6. Legal interpretation
  7. Contribution
  8. Related Work
  9. Runtime Compliance Governance for Business Process
  10. Compliance and Requirements Engineering
  11. Adaptive Compliance
  12. Problem Formulation
  13. Digital Twin for Adaptive Compliance
  14. A Reference Architectural Framework
  15. Compliance Modelling and Assessment
  16. ...and 9 more sections

Figures (10)

  • Figure 1: Design process: from compliance source to behavioural rules of agents.
  • Figure 2: The reference architecture for a compliance-aware Digital Twin.
  • Figure 3: Pareto efficiency of two objectives: compliance level and goal satisfaction. A and C both dominate B, but A and C are mutually non-dominated solutions.
  • Figure 4: Overall layout of the case study. Human workers move according to black dashed arrows. The movement of AMRs (red arrows) may intersect with the workers. The safety compliance behavioural rule onboard an AMR should instruct the AMR to slow down to avoid collision with human workers.
  • Figure 5: The GRL model for safety compliance and productivity.
  • ...and 5 more figures