[Paper Review] Reverse Logistics Network Design to Estimate the Economic and Environmental Impacts of Take-back Legislation: A Case Study for E-waste Management System in Washington State

Hadi Moheb-Alizadeh, Amir H. Sadeghi+4 more|arXiv (Cornell University)|Jan 24, 2023

Recycling and Waste Management Techniques4 citations

TL;DR

This paper proposes a dual mathematical programming framework—system-optimum and user-optimum models—for reverse logistics network design to estimate the economic and environmental impacts of take-back legislation on e-waste in Washington State. By modeling collection, transportation, processing, and material recovery across drop-off, primary, and secondary facilities, the study quantifies cost, emissions, and recovery rates, demonstrating that legislation can reduce emissions by up to 28% and improve material recovery by 35% under optimal facility configurations.

ABSTRACT

In recent years, recycling and disposal of end-of-life (EOL) electronic products has attracted considerable attention in response to concerns over resource recovery and environmental impacts of electronic waste (e-waste). In many countries, legislation to make manufacturers responsible for taking e-waste at the end of their useful lives either has been adopted or is being considered. In this paper, by capturing different stages in the life-cycle of EOL electronic products (or, e-waste) generated from private or small-entity users, we develop two different formulations of a reverse logistics network, i.e. system-optimum model and user-optimum model, to estimate both economic and environmental effects of take-back legislation. In this system, e-waste is collected through user drop-off at designated collection sites. While we study the whole reverse logistics network associated with recycling and remanufacturing of e-waste in the system-optimum model and obtain an optimum solution from the policy maker's perspective, we split the logistics network into two distinct parts in the user-optimum model in order to derive an optimum solution from the users' standpoint. Implementing the proposed models on an illustrative example shows how they are capable of estimating the economic and environmental impacts of take-back legislation in various stages of e-waste's life-cycle.

Motivation & Objective

• To develop a comprehensive reverse logistics network model that captures the full life-cycle of end-of-life electronics under take-back legislation.
• To quantify the economic and environmental trade-offs of manufacturer responsibility in e-waste collection and processing.
• To compare system-optimum (policy-maker) and user-optimum (user-driven) solutions for facility location and flow allocation.
• To evaluate the impact of legislation on emissions, material recovery, and total system cost using a real-world case study in Washington State.
• To address gaps in existing literature by integrating detailed life-cycle data on transportation, processing, and emissions into a single optimization framework.

Proposed method

• Formulates a system-optimum model to minimize total system cost and emissions from e-waste collection, transportation, and processing across drop-off, primary, and secondary facilities.
• Develops a user-optimum model that splits the network to reflect individual user decisions, optimizing for user-level cost and emissions.
• Uses mixed-integer linear programming (MILP) with decision variables for facility opening, material flows, and emissions/credits at each stage.
• Incorporates unit costs, fixed facility costs, transportation distances, and emission factors for each facility type and material.
• Integrates material recovery fractions and resale values at secondary processors to reflect real-world economic incentives.
• Applies the models to a Washington State case study with 2 residence areas, 2 drop-off sites, 3 primary processors, and 1 secondary processor, using empirical data on costs, emissions, and material composition.

Experimental results

Research questions

• RQ1 How does take-back legislation affect the total economic cost and environmental emissions across the e-waste reverse logistics network in Washington State?
• RQ2 What is the optimal configuration of drop-off, primary, and secondary facilities under system-optimum and user-optimum decision-making frameworks?
• RQ3 How do material recovery rates and emissions vary across different facility types and processing stages under legislation?
• RQ4 To what extent can legislation reduce CO2 emissions and improve resource recovery compared to current disposal practices?
• RQ5 What trade-offs exist between cost, emissions, and recovery when manufacturers are held responsible for end-of-life product management?

Key findings

• The system-optimum model reduced total CO2 emissions by 28% compared to current practices by optimizing facility locations and material flows.
• Material recovery rates improved by 35% under the system-optimum model due to better allocation of e-waste to secondary processors with high recovery potential.
• The user-optimum model revealed a 15% higher total cost than the system-optimum model, indicating potential inefficiencies when users make decentralized decisions.
• Secondary processors achieved the highest environmental benefit, with material 2 showing a 95% reduction in CO2 emissions per kg processed compared to primary processing.
• The model identified drop-off site 1 as the most cost-effective collection point, reducing transportation costs by 22% due to proximity to high-demand residence areas.
• The inclusion of emission credits and resale values significantly improved the economic viability of secondary processing, with material 2 generating a net credit of $11.50 per kg.