WA3 - Multi-Objective Optimization 2

The rapid growth of Waste Electrical and Electronic Equipment (WEEE) poses significant challenges for energy sustainability, resource security and carbon mitigation. Electrical appliances embody substantial energy and material inputs, and inefficient end-of-life management increases lifecycle emissions and weakens circular energy transitions. In response to Extended Producer Responsibility (EPR) and emerging Right-to-Repair (R2R) regulations, supply chains are being reconfigured to prioritise repair, reuse and high-quality recycling. This study develops a multi-objective Mixed-Integer Linear Programming (MILP) model to optimise a Circular Closed-Loop (CirCLoop) supply chain for WEEE management. The model simultaneously minimises total system costs and CO₂ emissions while maximising material circularity. Regulatory constraints are embedded directly, including minimum recovery and recycling rates under EPR and repairability requirements under R2R. The framework integrates forward and reverse logistics, facility location decisions, and information flows across manufacturers, retailers, collection points, warehouses, repair centres, recycling facilities and disposal sites. The model is empirically parameterised using operational data from the regional WEEE system in Andalusia, Spain, involving FAEL and RECILEC. Scenario analysis evaluates trade-offs between cost efficiency, environmental performance and circularity under different policy-weight configurations. Results indicate that optimised regional infrastructure planning can reduce transport-related emissions, strengthen regulatory compliance and improve the energy efficiency of material recovery systems. The proposed framework supports the design of low-carbon circular energy systems by offering a policy-sensitive optimisation tool for regional decision-makers and supply chain actors, contributing to resilient and climate-aligned circular transitions.

Hydro‑Québec’s distribution network serves many municipalities through service centers across the province, and demand for line workers is expected to grow over time. We therefore propose a mixed‑integer‑programming decision-making tool to support directors in allocating and reallocating crews, revising municipal assignments, expanding infrastructure capacity, and placing new service centers. To reconcile two conflicting performance metrics, we adopt a hierarchical multi objective framework and propose an intuitive strategy for selecting a Pareto optimal solution.

Shipping represents one of the leading sources of anthropogenic noise in the oceans, primarily caused by propeller cavitation and propulsion system vibrations. This acoustic pollution disrupts the behaviour of numerous marine species by interfering with their communication, navigation, and reproduction, posing serious threats to marine biodiversity. To mitigate this impact, several measures have been developed, including vessel speed reduction, route optimization, propeller maintenance, and the adoption of quieter propulsion technologies. However, these noise reduction strategies carry direct implications for GHG emissions and operational performance, affecting delivery times and operational costs, creating complex trade-offs across all three dimensions. Since these acoustic, environmental, and operational aspects are rarely studied jointly, maritime stakeholders currently lack adequate decision-support tools to address them simultaneously. This paper develops a multi-criteria decision-making (MCDM) framework to jointly evaluate the acoustic, environmental, and operational performance of a vessel. The framework structures the selection of Key Performance Indicators (KPIs) across these three dimensions and provides maritime operators and regulators with a practical tool to support decision-making around underwater noise mitigation.

POPS est un logiciel d’aide à la décision reposant principalement sur des prévisions de la demande client. Il permet aux utilisateurs de générer, d’analyser et d’ajuster des recommandations stratégiques dans le but d’améliorer la performance du réseau de distribution d’Hydro‑Québec. Le présent travail porte sur le développement et l’amélioration de modèles de prévision fiables, robustes et efficaces. La prévision est réalisée en plusieurs phases afin d’intégrer de façon cohérente l’ensemble des variables exogènes disponibles, notamment des variables économiques définies à différentes échelles : provinciale, régionale, annuelle et mensuelle.