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Applying Multi-Agent Negotiation to Solve the Production Routing Problem With Privacy Preserving

Luiza Pellin Biasoto, Vinicius Renan de Carvalho, Jaime Simão Sichman

TL;DR

The paper addresses the Production Routing Problem with Privacy Preserving (PRPPP), a privacy-constrained extension of the classic PRP that integrates production, inventory, distribution, and routing decisions. It introduces a hybrid Multi-Agent System (MAS) where a coordinator supplier and retailer agents negotiate delivery plans using private data, guided by a utility-based framework with agenda transactions (Insertion, Removal, Substitution) and neighborhood voting. The approach formalizes agent roles, data requirements, and negotiation dynamics (including Algorithm 1 control flow), demonstrating how privacy can be preserved while still achieving cost-effective, coordinated planning. This framework has practical implications for real-world supply chains requiring privacy protection, real-time adaptability, and integrated decision-making across multiple organizational boundaries.

Abstract

This paper presents a novel approach to address the Production Routing Problem with Privacy Preserving (PRPPP) in supply chain optimization. The integrated optimization of production, inventory, distribution, and routing decisions in real-world industry applications poses several challenges, including increased complexity, discrepancies between planning and execution, and constraints on information sharing. To mitigate these challenges, this paper proposes the use of intelligent agent negotiation within a hybrid Multi-Agent System (MAS) integrated with optimization algorithms. The MAS facilitates communication and coordination among entities, encapsulates private information, and enables negotiation. This, along with optimization algorithms, makes it a compelling framework for establishing optimal solutions. The approach is supported by real-world applications and synergies between MAS and optimization methods, demonstrating its effectiveness in addressing complex supply chain optimization problems.

Applying Multi-Agent Negotiation to Solve the Production Routing Problem With Privacy Preserving

TL;DR

The paper addresses the Production Routing Problem with Privacy Preserving (PRPPP), a privacy-constrained extension of the classic PRP that integrates production, inventory, distribution, and routing decisions. It introduces a hybrid Multi-Agent System (MAS) where a coordinator supplier and retailer agents negotiate delivery plans using private data, guided by a utility-based framework with agenda transactions (Insertion, Removal, Substitution) and neighborhood voting. The approach formalizes agent roles, data requirements, and negotiation dynamics (including Algorithm 1 control flow), demonstrating how privacy can be preserved while still achieving cost-effective, coordinated planning. This framework has practical implications for real-world supply chains requiring privacy protection, real-time adaptability, and integrated decision-making across multiple organizational boundaries.

Abstract

This paper presents a novel approach to address the Production Routing Problem with Privacy Preserving (PRPPP) in supply chain optimization. The integrated optimization of production, inventory, distribution, and routing decisions in real-world industry applications poses several challenges, including increased complexity, discrepancies between planning and execution, and constraints on information sharing. To mitigate these challenges, this paper proposes the use of intelligent agent negotiation within a hybrid Multi-Agent System (MAS) integrated with optimization algorithms. The MAS facilitates communication and coordination among entities, encapsulates private information, and enables negotiation. This, along with optimization algorithms, makes it a compelling framework for establishing optimal solutions. The approach is supported by real-world applications and synergies between MAS and optimization methods, demonstrating its effectiveness in addressing complex supply chain optimization problems.
Paper Structure (15 sections, 51 equations, 7 figures, 1 algorithm)

This paper contains 15 sections, 51 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. THE PRODUCTION ROUTING PROBLEM (PRP)
  3. The Production Routing Problem with Privacy Preserving (PRPPP)
  4. Input Data
  5. Agents
  6. Other Components
  7. Negotiation Protocol
  8. Utility
  9. Agenda Transactions
  10. Pseudo-Algorithm
  11. Conclusions and future work
  12. APPENDIX- PRP Formulation
  13. Transactions' Calculation Record
  14. Transaction 1
  15. Transaction 2

Figures (7)

  • Figure 1: Production Routing Problem (PRP) Adulyasak2014b
  • Figure 2: Transaction 1 - Insertion combined with a removal for a delivery of 10 units of product from retailer 3, anticipating it from period 2 to 1.
  • Figure 3: Transaction 1 - Negotiation and voting phases. As the delta utility from retailer 3 is positive for the proposed change, the voting phase is triggered. The result is a tie - 2 in favor and 2 against the change, so the supplier vote in favor in order to break the tie. This results in a successful transaction. (See complete calculation record in Appendix)
  • Figure 4: Transaction 2 - Substitution between two retailers' deliveries. Retailer 4 has a delivery of 5 units anticipated to period 1, while retailer 2 has a delivery of 15 units postponed to period 2.
  • Figure 5: Transaction 2 - Negotiation and voting phases. Retailers 2 and 4 negotiate and decide to apply changes only for retailer 2 (Y, N). Subsequently, the voting phase is triggered, with 3 votes in favor and 2 against. Then, the transaction succeeds only for the change in the delivery plan of retailer 2. (See complete calculation record in Appendix)
  • ...and 2 more figures