Supply Chain Optimization via Generative Simulation and Iterative Decision Policies

2025-07-09

AI Summary

Addressing the challenge of balancing responsiveness and economic efficiency in supply chain transportation, this paper proposes Sim-to-Dec—a novel simulation-decision paradigm integrating generative simulation with iterative decision optimization. The framework employs an autoregressive generative simulation module for fine-grained, cross-scenario generalization of transport dynamics; designs an end-to-end decision model with dual awareness of historical and future information to jointly leverage empirical knowledge and predictive signals; and establishes a tightly coupled mechanism linking simulation feedback to policy updates. Experiments on three real-world datasets demonstrate significant improvements: average on-time delivery rate increases by 12.3%, and average profit rises by 9.7%. Sim-to-Dec thus provides an observable, low-risk, and generalizable solution for strategic transportation decision-making under high uncertainty.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

High responsiveness and economic efficiency are critical objectives in supply chain transportation, both of which are influenced by strategic decisions on shipping mode. An integrated framework combining an efficient simulator with an intelligent decision-making algorithm can provide an observable, low-risk environment for transportation strategy design. An ideal simulation-decision framework must (1) generalize effectively across various settings, (2) reflect fine-grained transportation dynamics, (3) integrate historical experience with predictive insights, and (4) maintain tight integration between simulation feedback and policy refinement. We propose Sim-to-Dec framework to satisfy these requirements. Specifically, Sim-to-Dec consists of a generative simulation module, which leverages autoregressive modeling to simulate continuous state changes, reducing dependence on handcrafted domain-specific rules and enhancing robustness against data fluctuations; and a history-future dual-aware decision model, refined iteratively through end-to-end optimization with simulator interactions. Extensive experiments conducted on three real-world datasets demonstrate that Sim-to-Dec significantly improves timely delivery rates and profit.

Problem

• Optimizing supply chain transportation for responsiveness and efficiency
• Developing a simulation-decision framework for strategy design
• Improving delivery rates and profit through iterative policy refinement

Innovation

• Generative simulation via autoregressive modeling
• History-future dual-aware decision model
• End-to-end optimization with simulator interactions