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Sharing is caring: data sharing in multi-agent supply chains

Wan Wang, Haiyan Wang, Adam Sobey

TL;DR

A multi-agent system where the factory agent can share information downstream, increasing the observability of the environment and the results show that data sharing can boost the performance, especially when combined with a cooperative reward shaping.

Abstract

Modern supply networks are complex interconnected systems. Multi-agent models are increasingly explored to optimise their performance. Most research assumes agents will have full observability of the system by having a single policy represent the agents, which seems unrealistic as this requires companies to share their data. The alternative is to develop a Hidden-Markov Process with separate policies, making the problem challenging to solve. In this paper, we propose a multi-agent system where the factory agent can share information downstream, increasing the observability of the environment. It can choose to share no information, lie, tell the truth or combine these in a mixed strategy. The results show that data sharing can boost the performance, especially when combined with a cooperative reward shaping. In the high demand scenario there is limited ability to change the strategy and therefore no data sharing approach benefits both agents. However, lying benefits the factory enough for an overall system improvement, although only by a relatively small amount compared to the overall reward. In the low demand scenario, the most successful data sharing is telling the truth which benefits all actors significantly.

Sharing is caring: data sharing in multi-agent supply chains

TL;DR

A multi-agent system where the factory agent can share information downstream, increasing the observability of the environment and the results show that data sharing can boost the performance, especially when combined with a cooperative reward shaping.

Abstract

Modern supply networks are complex interconnected systems. Multi-agent models are increasingly explored to optimise their performance. Most research assumes agents will have full observability of the system by having a single policy represent the agents, which seems unrealistic as this requires companies to share their data. The alternative is to develop a Hidden-Markov Process with separate policies, making the problem challenging to solve. In this paper, we propose a multi-agent system where the factory agent can share information downstream, increasing the observability of the environment. It can choose to share no information, lie, tell the truth or combine these in a mixed strategy. The results show that data sharing can boost the performance, especially when combined with a cooperative reward shaping. In the high demand scenario there is limited ability to change the strategy and therefore no data sharing approach benefits both agents. However, lying benefits the factory enough for an overall system improvement, although only by a relatively small amount compared to the overall reward. In the low demand scenario, the most successful data sharing is telling the truth which benefits all actors significantly.
Paper Structure (20 sections, 16 equations, 7 figures, 7 tables)

This paper contains 20 sections, 16 equations, 7 figures, 7 tables.

Table of Contents

  1. Data sharing in multi-agent supply chain systems
  2. Multi-agent supply chain environment with information sharing
  3. Problem setting
  4. State space
  5. State transition
  6. Action space
  7. Observation space
  8. Reward function
  9. Baseline reward
  10. Collaborative reward
  11. Data sharing with collaborative reward shaping scenarios
  12. High demand environment with baseline rewards
  13. High demand environment with collaborative reward shaping
  14. Low demand environment with the baseline reward
  15. Low demand environment with collaborative reward shaping
  16. ...and 5 more sections

Figures (7)

  • Figure 1: Flow of data sharing through the supply chain environment.
  • Figure 2: Comparison of factory rewards for the various communication approaches to the baseline environment with No Communication in the high demand environment.
  • Figure 3: Comparison of factory rewards for the various communication approaches to the baseline environment with No Communication in the low demand environment.
  • Figure 4: Comparison of retailer rewards for the various communication approaches to the baseline environment with no communication in the high demand environment.
  • Figure 5: Comparison of retailer rewards to the baseline environment with no communication in the low demand environment.
  • ...and 2 more figures