Fuzzy Optimization of Remanufacturing Green Supply Chain Network under Incentive Compatibility Theory

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Fuzzy Optimization of Remanufacturing Green Supply Chain Network under Incentive Compatibility Theory

Introduction

The increasing global focus on environmental sustainability has brought significant attention to remanufacturing as a key strategy for achieving carbon neutrality. The 27th United Nations Climate Change Conference highlighted the importance of innovative approaches to reduce carbon emissions, with remanufacturing emerging as a sustainable production model. However, designing an efficient green supply chain network for remanufacturing involves complex challenges, including product returns, transportation inefficiencies, and environmental pollution. These issues can lead to increased costs and carbon emissions, particularly in high-tech manufacturing sectors such as electronics and electric vehicle batteries.

To address these challenges, this study explores the role of government intervention in optimizing remanufacturing supply chain networks. By leveraging incentive compatibility theory, we develop a multi-period, multi-objective optimization model that minimizes total costs, reduces carbon emissions, and maximizes returns on big data investments. The model incorporates fuzzy optimization techniques to handle uncertainties in demand and recycling volumes, while an improved hybrid algorithm is employed to solve the optimization problem efficiently.

Background and Motivation

The manufacturing industry, especially high-tech sectors, faces significant challenges in managing product returns and recycling processes. Products such as computer components, communication devices, and electric vehicle batteries are prone to damage, environmental contamination, and high recycling costs. These factors complicate the reverse logistics process, leading to inefficiencies in supply chain operations. Additionally, global economic fluctuations and stringent environmental regulations necessitate that manufacturers balance profitability with sustainability.

Traditional supply chain models often fail to account for the dynamic nature of demand and recycling volumes, leading to suboptimal performance. Furthermore, most existing research focuses on forward supply chains, neglecting the complexities of reverse logistics in remanufacturing. This study bridges this gap by integrating both forward and reverse logistics into a unified green supply chain network.

The rapid advancement of big data and artificial intelligence (AI) technologies presents new opportunities for optimizing supply chain operations. By utilizing data-driven insights, manufacturers can improve demand forecasting, optimize transportation routes, and enhance recycling efficiency. Government policies, such as subsidies and carbon taxes, can further incentivize manufacturers to adopt sustainable practices.

Model Framework

The proposed model consists of a multi-period, multi-objective optimization framework designed to address the challenges of remanufacturing supply chains. The network includes seven key nodes: suppliers, retailers, distribution centers, recycling centers, customers, data companies, and government subsidies.

Material Flow

The supply chain network is divided into forward and reverse logistics. In the forward logistics phase, suppliers transport high-tech products to distribution centers, which then deliver them to retailers. Retailers sell the products to customers, while recyclable components are sent to recycling centers for processing. Data companies collect and analyze information to optimize logistics operations.

In the reverse logistics phase, end-of-life products and defective components are returned to retailers, who then send them to distribution centers. The distribution centers forward these items to recycling centers for processing. The recycling centers transmit relevant data back to the distribution centers, enabling continuous improvement in recycling efficiency.

Key Assumptions

  1. The study focuses on high-tech manufacturing industries with multi-period and multi-objective supply chain networks.
  2. The processing capacities of suppliers, retailers, distribution centers, recycling centers, and data companies are fixed.
  3. Transportation costs, distances, and volumes have a linear relationship.
  4. Product recycling may experience disruptions, incurring penalties.
  5. Carbon emissions primarily originate from transportation processes.
  6. Government subsidies are available to encourage sustainable practices.

Objectives

The model aims to optimize three key objectives:

  1. Minimize Total Cost: The cost function includes fixed and variable costs associated with facility establishment, transportation, inventory management, and penalties for recycling disruptions.
  2. Minimize Carbon Emissions: Emissions are calculated based on transportation distances and vehicle efficiency.
  3. Maximize Big Data Investment Returns: Investments in data analytics and AI technologies are evaluated based on their contribution to operational efficiency and cost reduction.

Government Intervention and Incentive Compatibility

A critical aspect of the model is the integration of incentive compatibility theory to align government policies with corporate sustainability goals. Government subsidies are designed to encourage manufacturers to invest in green technologies and recycling initiatives. The subsidy function is structured to reward manufacturers based on their environmental performance, ensuring that both economic and ecological objectives are met.

Methodology

Fuzzy Optimization

Uncertainties in demand and recycling volumes are addressed using fuzzy chance-constrained programming. Triangular fuzzy numbers are employed to represent uncertain parameters, allowing for flexible modeling of real-world variability. The fuzzy constraints are transformed into deterministic equivalents to facilitate optimization.

Hybrid Algorithm

The optimization problem is solved using an improved hybrid algorithm (HA) that combines the strengths of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Differential Evolution (DE). NSGA-II is effective in handling multi-objective optimization but suffers from high computational complexity. DE, on the other hand, offers faster convergence and lower computational overhead. The hybrid approach leverages the advantages of both algorithms to achieve efficient and robust solutions.

Algorithm Steps

  1. Initialization: Input parameters, constraints, and objective functions are defined. Population size, maximum iterations, crossover probability, and mutation probability are set.
  2. Population Generation: An initial population is randomly generated, reflecting possible configurations of decision variables.
  3. Evaluation and Non-dominated Sorting: Each individual’s fitness is evaluated, and non-dominated sorting is applied to identify Pareto-optimal solutions.
  4. DE-based Crossover and Mutation: DE operators are applied to enhance exploration and exploitation.
  5. Selection and Diversity Maintenance: Crowding distance metrics are used to maintain population diversity.
  6. Termination: The algorithm terminates when stopping criteria (e.g., maximum iterations) are met.

Case Study

A case study involving a high-tech manufacturing supply chain is conducted to validate the model. The network includes one supplier, two retailers, three candidate distribution centers, two candidate recycling centers, two candidate data companies, and five customers.

Results and Analysis

  1. Multi-Period vs. Single-Period Optimization: Multi-period optimization demonstrates superior performance in cost reduction, emission control, and investment returns compared to single-period approaches.
  2. Impact of Government Subsidies: Subsidies significantly improve the economic and environmental performance of the supply chain. Manufacturers achieve lower costs and higher recycling rates when subsidies are applied.
  3. Big Data Investment: Scenarios where both suppliers and retailers invest in big data analytics yield the best results, highlighting the importance of data-driven decision-making.
  4. Algorithm Performance: The hybrid algorithm outperforms standalone NSGA-II and DE in terms of solution quality, convergence speed, and computational efficiency.

Conclusion

This study presents a comprehensive framework for optimizing remanufacturing green supply chain networks under uncertainty. By integrating incentive compatibility theory, fuzzy optimization, and hybrid algorithms, the model effectively balances economic, environmental, and technological objectives. Key findings include:

• Multi-period optimization provides more flexible and cost-effective solutions than single-period approaches.

• Government subsidies play a crucial role in encouraging sustainable practices and improving supply chain performance.

• Investments in big data and AI technologies enhance operational efficiency and decision-making.

• The hybrid algorithm delivers robust and efficient solutions, making it suitable for complex supply chain optimization problems.

Future research directions include extending the model to other industries, enhancing robustness under extreme disruptions, and exploring dynamic subsidy mechanisms.

doi.org/10.19734/j.issn.1001-3695.2024.06.0156

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