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CSDDD Unleashed: How the EU’s Hard Law Directive Is Forcing Global Supply Chain Reengineering
Papers

CSDDD Unleashed: How the EU’s Hard Law Directive Is Forcing Global Supply Chain Reengineering

The EU’s Corporate Sustainability Due Diligence Directive (CSDDD) has entered force, transforming global supply chains from logistical networks into legally accountable systems. With penalties up to 5% of global turnover and extraterritorial reach, it compels companies—including Chinese exporters generating €150M+ in EU revenue—to conduct rigorous, continuous human rights and environmental due diligence across all tiers. Operational realities expose stark gaps in supplier capacity, especially in emerging economies, driving co-investment in traceability, grievance mechanisms, and regenerative practices. Financial institutions are embedding CSDDD alignment into lending and investment criteria, creating a bifurcated global supply chain where sustainability performance directly determines capital access and valuation. This is not compliance—it is the foundational architecture of planetary-scale corporate governance.

Papers

A Multi-stage Bonus Allocation Framework for Meal Delivery Platforms

This research proposes a multi-stage bonus allocation framework to address order acceptance challenges in meal delivery platforms. Through dynamic bonus incentives, the framework can reduce canceled orders by over 60% within a limited budget while saving restaurants more than 30% in food waste compensation.

How Meituan Reduces Order Cancellations by 25% with Dynamic Bonus Allocation: KDD 2022 Research from Huazhong University
Papers

How Meituan Reduces Order Cancellations by 25% with Dynamic Bonus Allocation: KDD 2022 Research from Huazhong University

Meituan and Huazhong University research presents Multi-Stage Bonus Allocation (MSBA) framework, optimizing subsidy fund efficiency through Lagrangian dual dynamic programming, achieving 25% order cancellation reduction and 31% restaurant compensation cost savings in real-world A/B testing. This research demonstrates how operations research and machine learning can be combined to solve real-world logistics optimization problems at massive scale.

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