IoT-driven dynamic replenishment of fresh produce in the presence of seasonal variations: A deep reinforcement learning approach using reward shaping
This study, published in Omega, a prominent journal in Management Science, investigates the application of Deep Reinforcement Learning (DRL) to dynamic replenishment in fresh produce supply chain. We specifically address the bidirectional seasonal variations in both supply lead time and demand of fresh produce. To enhance dynamic replenishment performance, a Reward shaping function was innovatively designed based on the "zero-inventory" paradigm.
Feb 12, 2025
Operation decisions and coordination contract in a dual-channel green supply chain with risk tolerance
This study focuses on a dual-channel green supply chain. It constructs a manufacturer-dominated supply chain game model based on the differing risk tolerances of the parties involved. The mean-variance method is utilized to analyze the operational decisions and expected profits of participants under centralized decision-making and wholesale price contracts, and a coordinating contract is designed.
Jun 6, 2024
Financing Strategy for a Dual-channel Supply Chain that Faces Loss-averse Retailers and Stochastic Demands
We investigates supply chain financing strategies under stochastic demand, incorporating loss-averse behaviors of capital-constrained retailers. Utilizing a Stackelberg game framework, we develop two financing models: trade credit and bank credit. Through equilibrium analysis of dual-channel supply chain operations, we derive optimal decision-making rules. Numerical simulations further quantify how the retailer’s loss aversion coefficient and initial capital jointly influence operational strategies and financing choices, providing actionable insights for risk management and resource allocation.
May 30, 2023