Intelligent Planning Algorithms and Their Applications in Supply Chain Optimization

This article focuses on the application of operations‑optimization and simulation algorithms in supply‑chain planning, using intelligent planning algorithms to support cost optimization, efficiency improvement, and user‑experience enhancement.

01 Background Overview

Supply‑chain planning is a strategic upstream activity; adjustments at this level affect downstream processes, requiring a global view to evaluate cost, efficiency, and experience across many components and strategies, which involves massive data and multiple analysis dimensions.

Supply‑chain planning algorithms empower the business in breadth and depth: through operations models and efficient optimization algorithms they compute complex problems and search optimal combinations, while simulation and intelligent diagnosis reveal metric impacts of strategies, identify optimization opportunities, and deepen quantitative value perception.

02 Intelligent Planning Algorithms and Applications

2.1 Warehouse Network Planning

A well‑designed supply‑chain network improves efficiency and aligns with commercial models and competitive strategies. JD.com addresses two main scenarios: trunk‑network site selection and front‑warehouse site selection.

Trunk‑network planning targets national or regional multi‑level hub networks, optimizing cost and timeliness by jointly deciding product flow, candidate sites, network hierarchy, coverage, and transport modes (air, land, vehicle). To handle diverse strategies and constraints, a simulation‑driven model generates product × node × level × transport‑mode scenarios, and a 0‑1 integer programming model (Binary Scenarios Selection Model) selects the optimal scenario, decoupling decision logic from business specifics.

For large‑scale problems with millions of variables, a pre‑filter strategy combined with variable clustering and decision‑space reduction solves the problem in about 90 seconds, a scale where commercial solvers such as SCIP or Gurobi cannot even finish modeling.

Front‑warehouse planning is a single‑layer, city‑level network serving instant‑retail and offline stores. By integrating a GA + Rollout algorithm—combining genetic algorithms with a rollout‑style greedy exploration operator—the solution achieves 9‑15× speedup over SCIP and up to 48× improvement over Gurobi while maintaining comparable solution quality.

2.2 Inventory Layout

Inventory layout sits between network planning and replenishment, providing SKU placement decisions based on physical warehouse and transport network relationships. The system decouples three modules: strategy‑factor library, decision engine, and application layer. Users configure custom strategy factors, the decision engine outputs selected SKUs under given goals and constraints, and the application layer integrates with production systems or runs simulation evaluations, enabling online, automated, and rapid responses to business needs.

2.3 Supply‑Chain Simulation

Simulation serves as a quantitative decision‑support tool, capturing the “butterfly effect” of upstream changes on downstream processes. A sandbox‑style full‑chain simulation model combines network structure, product layout, inventory policies, and fulfillment strategies to generate scenario‑specific results.

Optimization of simulation scenarios and parameters leverages gradient‑descent methods from operations research and deep‑learning frameworks, while matrix computations accelerated by GPU achieve 10‑100× speedup in simulation runtime.

03 Summary and Outlook

Based on project experience, the article introduced intelligent planning algorithms that empower supply‑chain operations to achieve cost reduction and efficiency gains. With the next wave of AI technologies—large models, reinforcement learning, and improved explainability—future algorithms will become more efficient, higher‑quality, and easier to interpret, further advancing the digital intelligence of supply chains.

Recommended Reading

Industry SOTA: JD’s First Self‑Developed Billion‑Scale Temporal Model

JD Supply‑Chain Innovation: Data‑Driven Inventory Selection and Allocation Algorithms

Asia’s Only 2024 Gartner Technology Innovation Award: JD Supply‑Chain Exploration