Effective Management for Blockchain-Based Agri-Food Supply Chains Using Deep Reinforcement Learning

Abstract

In agri-food supply chains (ASCs), consumers pay for agri-food products produced by farmers. During this process, consumers emphasize the importance of agri-food safety while farmers expect to increase their profits. Due to the complexity and dynamics of ASCs, the effective traceability and management for agri-food products face huge challenges. However, most of the existing solutions cannot well meet the requirements of traceability and management in ASCs. To address these challenges, we first design a blockchain-based ASC framework to provide product traceability, which guarantees decentralized security for the agri-food tracing data in ASCs. Next, a Deep Reinforcement learning based Supply Chain Management (DR-SCM) method is proposed to make effective decisions on the production and storage of agri-food products for profit optimization. The extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed blockchain-based framework and the DR-SCM method under different ASC environments. The results show that reliable product traceability is well guaranteed by using the proposed blockchain-based ASC framework. Moreover, the DR-SCM can achieve higher product profits than heuristic and Q-learning methods. INDEX TERMS Agri-food supply chains, agri-food safety, product traceability, profit optimization, blockchain, deep reinforcement learning.

Blockchain Technology for Secure Supply Chain Management: A Comprehensive Review

“…--Food Supply Chain Chen et al [70] Design BC-based ASC framework to present product traceability.To make successful decisions about agri-food product production and storage for profit maximization, a Deep Reinforcement Learning-based SCM(DR-SCM)technique was used.…”

Deep Reinforcement Learning for Supply Chain Synchronization

“…Wang et al applied a DRL agent to the supply chain coordination problem under uncertainty [27]. Chen et al proposed the DRL framework for effective management for blockchainbased [28]. Perez et al compared several reinforcement learning and heuristic methods, including DRL, on a single product inventory-control problem under stochastic stationary consumer demand [12].…”

An Exploration of Blockchain-based Traceability in Food Supply Chains: On the Benefits of Distributed Digital Records from Farm to Fork

“…This central authority actor, however, for a variety of reasons, may turn out to have a somewhat or very limited view of an entire supply chain, which thus hinders collaboration, delays information processing, and increases the risk of data corruption, as data flows through intermediaries (Apte & Petrovsky, 2016;Mukri, 2018). Thus, a traditional pre-or nonblockchain system is more vulnerable to corruption, hacking, data leaking, contractual disputes, tampering, and fraud (Azzi et al, 2019;Min, 2019;Chen et al, 2021). This makes blockchains for supply chain management a proverbial "game changer", meaning a foundational technological disruption to both global and local current supply chain systems.…”

“…Internal traceability includes, for example, sharing logistic data, inventory data, contracts, prices, and organic product certification links, while external traceability refers to, for example, providing food origin information and farmer data to consumers (Yon & Woo, 2018;van Hilten et al, 2020;Xiong et al, 2020). Thus, we see consumers calling for food safety, while farmers wish traceability systems that can aid them in crop management that increases their profits (Xiong et al, 2020;Chen et al, 2021). An increasing need therefore exists to provide traceability from "farm to fork", whereas the current costs of putting traceability systems into place are a major barrier for most supply chain actors (Aung & Chang, 2014;Casino et al, 2019).…”