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Strategic Allocation of Post-Consumer Textiles across Reuse, Recycling and Value Recovery Pathways

This study proposes a strategic allocation framework that optimizes the routing of heterogeneous post-consumer textiles across reuse, repair, resale, repurposing, and recycling pathways based on product condition, market demand, and processing constraints to maximize value retention and circularity while minimizing waste.

Original authors: MD Rahimul Islam

Published 2026-09-28

📖 6 min read🧠 Deep dive

CC BY 4.0

Original authors: MD Rahimul Islam

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Every year, the world generates a mountain of discarded clothing. These are not just piles of fabric; they are a chaotic mix of cotton shirts, polyester jackets, wool sweaters, and synthetic blends, each in a different state of wear. Some are barely worn and ready for a second life, while others are stained, torn, or made of fibers that are difficult to separate. For decades, the standard approach to this waste has been to send it all down a single road: either to a landfill or to a recycling plant. But this "one-size-fits-all" method often throws away value. A high-quality coat sent to be shredded for fiber loses its potential to be worn again, while a stained shirt sent to a second-hand shop might clog the system with items no one wants. The challenge for the circular economy is not just to collect these textiles, but to sort them intelligently, directing each item to the specific path where it can keep its value the longest.

A researcher at the University of North Texas, Md. Rahimul Islam, has developed a new way to make these decisions. Instead of relying on a fixed rule like "always reuse first" or "always recycle first," he created a strategic framework that acts like a traffic controller for discarded clothes. This system looks at the specific condition of every item, its material makeup, how far it needs to travel, and what the current market demands. It then calculates the best destination for each piece of clothing, weighing the economic return against the environmental cost and the physical limits of the facilities available to process them. The goal is to keep as much value as possible within the system, whether that means selling a jacket as a second-hand item, repairing a pair of jeans, or breaking down a shirt into raw fiber only when no other option remains.

To test this idea, the researcher built a detailed computer simulation. Since gathering data from every real-world sorting facility is difficult and often involves private business secrets, he constructed a virtual network using public information and established industry data. He imagined a stream of one thousand kilograms of mixed post-consumer textiles arriving at a sorting center. In this virtual environment, the system evaluated each item against a set of criteria: Is it clean? Is it torn? Is it made of pure cotton or a difficult blend? How much does it cost to ship it to a repair shop versus a recycling plant? The model then assigned these items to one of several paths: direct reuse, repair, resale, refurbishment, repurposing, fiber recycling, or, as a last resort, residual disposal.

The results of this simulation were striking. Under normal operating conditions, the smart allocation system managed to divert 96% of the collected textiles away from the landfill. More importantly, it kept 75% of the total weight in product-level pathways, meaning the majority of the clothes were kept in a form where they could still be worn or used as whole items. Only 21% of the material was sent to be recycled into fibers, and a small 4% ended up as waste. This approach generated significantly more economic value than traditional methods. While a strategy that prioritizes recycling first might capture only about 68 cents of value for every kilogram processed, this optimized system retained roughly $1.10 per kilogram. The difference comes from recognizing that the highest value often lies in keeping the product intact, rather than breaking it down.

The study also tested how this system would react when things went wrong, mimicking real-world disruptions. When the researchers simulated a sudden 25% increase in the amount of clothes collected, the system adjusted by shifting some items from high-demand resale channels to other available paths like repurposing, ensuring the flow didn't get stuck. When the demand for second-hand goods dropped by half, the model automatically redirected those items toward repair or recycling instead of letting them sit unsold. Even when recycling capacity was cut by 30%, the system found a way to maintain a high diversion rate by leaning more heavily on repair and refurbishment. This flexibility proved that a rigid set of rules cannot handle the volatility of the real world; a dynamic system that responds to changing conditions is far more resilient.

However, the research also highlighted that the best path for a piece of clothing is not always the same. The simulation showed that the decision depends heavily on a few key factors. The most influential drivers were the demand in second-hand markets, the efficiency of recycling technology, the cost of processing, and the available capacity of facilities. If a market for used clothes is weak, even a perfect garment might be better suited for repurposing than for resale. If a recycling plant is too far away, the environmental cost of transport might make local repair a better choice, even if the item is damaged. The study suggests that there is no single "correct" hierarchy for all textiles. Instead, the most effective strategy is a conditional one: keep the best items in the product cycle, fix the ones that can be saved, repurpose the rest, and only recycle what cannot be used in any other form.

While these findings are based on a computer model and not a physical factory, they offer a clear blueprint for how the industry could operate. The researcher notes that future work would need to test this framework in real facilities to confirm the numbers. There are also opportunities to improve the system by using technology like computer vision to automatically identify fiber types and condition, which would make the sorting process faster and more accurate. For now, the study provides a powerful argument against the idea that all discarded clothes should be treated the same. By treating each item according to its specific potential and the current state of the market, we can keep more value in the economy and send far less to the landfill. The path forward is not a single road, but a complex, responsive network that knows exactly where to send every single piece of clothing.

Technical Summary: Strategic Allocation of Post-Consumer Textiles across Reuse, Recycling and Value Recovery Pathways

Problem Statement
Post-consumer textile waste is increasing in volume and complexity due to short product lifecycles and changing consumer demand. A critical challenge in circular textile management is that discarded garments are highly heterogeneous, varying significantly in physical condition, fiber composition, remaining useful life, contamination levels, and repairability. Current recovery practices often rely on fixed priority rules (e.g., "reuse-first" or "recycling-first") that fail to account for these variations. Such static approaches can lead to the misallocation of resources, such as directing high-quality garments toward material recycling or sending recyclable materials to pathways with insufficient capacity or market demand. The core problem is determining how to allocate heterogeneous textile streams across multiple recovery pathways (reuse, repair, resale, refurbishment, repurposing, fiber recycling, and residual treatment) while simultaneously optimizing for product value retention, material recovery, environmental burden, processing costs, logistics, and pathway capacity.

Methodology
The study proposes a simulation-based quantitative optimization framework designed to strategically allocate post-consumer textiles. The methodology follows a sequential workflow:

Data Collection and Classification: The model utilizes a dataset constructed from publicly available statistics, peer-reviewed literature, and industry information. Textile streams are classified based on condition scores (derived from physical integrity, cleanliness, remaining life, and repairability), fiber composition, contamination levels, and repair requirements.
Pathway Eligibility and Suitability: A binary eligibility parameter ( $E_{ij}$ ) determines if a specific textile stream $i$ is feasible for pathway $j$ based on technical and market constraints. A suitability score ( $S_{ij}$ ) is then calculated, weighing factors such as remaining useful life, market demand, recovery yield, processing burden, and transportation burden.
Multi-Objective Optimization: The core of the framework is a constrained multi-objective optimization model. The decision variable is the quantity of textile stream $i$ $i$ assigned to pathway $j$ $j$ ( $x_{ij}$ $x_{ij}$ ). The model maximizes a composite objective function ( $Z$ $Z$ ) that balances:
- Retained economic value ( $V$ )
- Recovered material/product value ( $M$ )
- Circularity contribution ( $C$ )
- Minimization of environmental burden ( $G$ )
- Minimization of processing and logistics burden ( $P$ )
- Subject to constraints including material balance, pathway capacity limits, market demand, and pathway eligibility.
Scenario Simulation and Sensitivity Analysis: The model is evaluated under a baseline scenario and eight disruption scenarios (e.g., increased collection volume, reduced resale demand, capacity constraints, transport distance changes). Sensitivity analysis is conducted to identify which parameters (e.g., recycling yield, resale demand, processing cost) most significantly influence allocation decisions.
Benchmarking: The proposed model is compared against four fixed-priority benchmark strategies: Recycling-first, Reuse-first, Value-first, and Capacity-first.

Key Contributions
The paper makes three primary contributions to the field of circular textile management:

Unified Allocation Model: It develops a single framework that allocates heterogeneous textile streams across both product-level recovery pathways (reuse, repair, resale, refurbishment, repurposing) and material-level pathways (fiber recycling), treating them as competing options for the same input stream.
Integrated Decision Variables: Unlike previous studies that often focus on individual pathways, this model jointly considers textile characteristics (condition, composition), operational factors (processing cost, logistics, capacity), and market dynamics (demand, yield) to determine optimal allocation.
Dynamic Evaluation: The framework evaluates allocation strategies not just under static conditions but also under operational disruptions. It uses sensitivity analysis to demonstrate how allocation decisions shift in response to changes in market demand, capacity constraints, and processing costs, moving beyond fixed recovery hierarchies.

Results
Under baseline conditions (1,000 kg of heterogeneous textiles), the proposed framework achieved the following performance metrics:

Diversion Rate: 96.0% of collected material was diverted from residual disposal.
Pathway Distribution: 75.0% of the total input was directed to product and value-retention pathways (direct reuse: 27%, repair: 12%, resale: 18%, refurbishment: 8%, repurposing: 10%). The remaining 21% went to fiber recycling, and 4% to residual disposal.
Economic and Material Performance: The model achieved a material recovery rate of 93.2% and retained an economic value of approximately USD 1.10/kg.
Benchmark Comparison: Compared to fixed-priority benchmarks, the proposed model outperformed all alternatives. Specifically, it increased retained economic value by 61.8% compared to the "Recycling-first" strategy (USD 1.10/kg vs. USD 0.68/kg) and achieved the highest diversion rate (96.0% vs. 92.0–95.0% for others).
Scenario Resilience: In disruption scenarios (e.g., a 50% drop in resale demand or a 30% reduction in recycling capacity), the model successfully redistributed flows to feasible alternative pathways rather than defaulting to disposal, though overall retained value decreased under combined disruptions.

Significance and Claims
The paper claims that post-consumer textile recovery is better formulated as a constrained multi-pathway allocation problem rather than a binary decision between reuse and recycling. The study demonstrates that a flexible, condition-based allocation strategy yields superior economic and circularity outcomes compared to rigid, single-priority rules. By explicitly modeling the trade-offs between product-level value retention and material recovery, the framework supports the development of more resource-efficient and resilient circular apparel supply chains.

The author maintains a modest stance regarding the study's scope, noting that the results are based on simulated data and transparent modeling assumptions rather than direct observations from commercial recovery facilities. Consequently, the numerical results are presented as a proof-of-concept demonstration of the model's behavior and logic. The framework is intended to provide a reproducible approach for evaluating pathways and to support future research involving facility-level validation, automated textile classification, and stochastic optimization.

Technical Summary: Strategic Allocation of Post-Consumer Textiles across Reuse, Recycling and Value Recovery Pathways

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