Pandemic-Resilient Recovery Networks Optimize Economic, Environmental, and Social Trade-offs

Why It Matters

The COVID-19 pandemic highlighted vulnerabilities in global supply chains, particularly for end-of-life product management. This research offers a framework for creating more robust and sustainable recovery networks that can adapt to unforeseen disruptions, ensuring continued resource efficiency and minimizing negative impacts.

Key Finding

The study developed a mathematical model that helps businesses make decisions about collecting, repairing, and recycling products at the end of their life, considering not just cost but also environmental and social well-being, especially during challenging times like a pandemic.

Key Findings

• A mathematical model can effectively integrate economic, environmental, and social objectives for recovery network design.
• The model demonstrates trade-offs between cost minimization, environmental impact reduction, and social impact minimization.
• A sustainable and hygienic design approach is feasible for recovery networks during pandemics.

Research Evidence

Aim: How can a mathematical model be developed to optimize the design of product recovery networks, balancing economic, environmental, and social factors during a pandemic?

Method: Mathematical Modelling and Case Study

Procedure: A multi-objective mixed-integer programming model was developed to represent a sustainable end-of-life product management system. This model was then validated using a case study and numerical example, with optimization performed using Lingo software.

Context: Supply Chain Management, Product Recovery, Pandemic Preparedness

Design Principle

Holistic Sustainability Optimization: Design recovery networks to achieve optimal balance across economic, environmental, and social dimensions, especially under conditions of uncertainty.

How to Apply

Utilize multi-objective optimization techniques to design and evaluate product recovery networks, ensuring they are robust to supply chain disruptions and align with sustainability goals.

Limitations

The model's applicability may be constrained by the specific parameters and assumptions used in the case study, and real-world implementation might face additional complexities not captured in the mathematical formulation.

Student Guide (IB Design Technology)

Simple Explanation: This study shows how to design systems for handling old products that are good for the planet, profitable, and good for people, even when unexpected things like a pandemic happen.

Why This Matters: It helps you understand how to make your design projects more sustainable and resilient, which is increasingly important in the real world.

Critical Thinking: To what extent can a purely mathematical model capture the nuanced complexities and human factors involved in real-world product recovery and waste management, especially during a crisis?

IA-Ready Paragraph: This research by Abbasi et al. (2022) provides a valuable framework for designing sustainable product recovery networks, emphasizing the integration of economic, environmental, and social objectives. Their work demonstrates how mathematical modeling can be used to navigate trade-offs and build resilience into end-of-life product management, a critical consideration during disruptive events such as pandemics.

Project Tips

• Consider using optimization software to model your design choices.
• Clearly define the economic, environmental, and social factors relevant to your product's end-of-life.

How to Use in IA

• Reference this study when discussing the importance of considering multiple objectives (cost, environment, social) in your design process, especially for product end-of-life strategies.

Examiner Tips

• Demonstrate an understanding of how external factors, like pandemics, can impact design decisions and require adaptive strategies.

Independent Variable: ["Pandemic conditions (e.g., lockdown, supply chain disruptions)","Recovery network design parameters (e.g., facility locations, transportation modes)"]

Dependent Variable: ["Total cost of the recovery network","Environmental impact (e.g., emissions, waste generated)","Social impact (e.g., job creation, community well-being)"]

Controlled Variables: ["Product type","Recovery processes (e.g., repair, recycling)","Demand for recovered products"]

Strengths

• Addresses a timely and critical issue of supply chain resilience.
• Provides a quantitative approach to balancing multiple sustainability objectives.

Critical Questions

• How can the proposed model be adapted for different types of products or industries?
• What are the practical challenges in collecting reliable data for the environmental and social impact assessments within the model?

Extended Essay Application

• Investigate the potential for designing circular economy models that are resilient to global disruptions, using optimization techniques similar to those presented.

Source

Designing Sustainable Recovery Network of End‐of‐Life Product during the COVID‐19 Pandemic: A Real and Applied Case Study · Discrete Dynamics in Nature and Society · 2022 · 10.1155/2022/6967088