Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing

TL;DR

This paper introduces an Edge-AI-driven decentralized task allocation framework for circular smart manufacturing, improving efficiency and sustainability through ranking-aware negotiation and local decision-making.

Contribution

It develops a novel ranking-aware learning framework that aligns with decentralized decision processes, enhancing task allocation in dynamic, resource-constrained manufacturing environments.

Findings

Improved delay and deadline adherence under high load.

Enhanced energy efficiency under tight constraints.

Effective decentralized coordination without central control.

Abstract

Task allocation in smart manufacturing systems needs to operate under decentralized decision-making, dynamic workloads, and shared resource constraints. In circular manufacturing settings, these challenges are further intensified by the need to balance operational efficiency with resource and energy sustainability. While learning-based approaches have been explored, many focus on predicting absolute performance metrics that do not necessarily translate into improved allocation outcomes, since decentralized assignment is governed by the relative ordering of candidate machines. This work proposes an Edge-AI-driven decentralized task allocation framework based on ranking-aware negotiation, where lightweight decision intelligence is embedded at the machine level to enable low-latency coordination without centralized control. The framework is developed progressively: a resource-aware heuristic first establishes the decentralized bidding structure, an Edge-AI-based regression model then provides learned local bid approximation, and a ranking-aware formulation finally reshapes the learning objective to align with the ordering-based nature of winner selection. Each machine evaluates incoming tasks using local information, including processing capability, queue state, and resource contention. The framework is evaluated via discrete-event simulation under high-load and tight-deadline scenarios using delay, deadline violations, throughput, and energy consumption. Results show improved delay and deadline adherence under high load, and enhanced energy efficiency under tighter constraints, leading to more resource-efficient operation aligned with circular manufacturing objectives. These findings demonstrate that aligning learning objectives with decentralized decision structures is critical for effective negotiation-driven task allocation.