On the development of a smart architecture for a sustainable manufacturing-remanufacturing system: A literature review approach

Mejía-Moncayo, Camilo; Kenné, Jean‐Pierre; Hof, Lucas A.

doi:10.1016/j.cie.2023.109282

Cited by 14 publications

(3 citation statements)

References 112 publications

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“…These factories are designed for intelligent production, aiming to achieve efficient, high-quality, low-consumption, environmentally friendly, and safe manufacturing and services. This approach significantly contributes to the advancement of the ICE [24,25]. The impact of intelligent manufacturing on the ICE is illustrated in Figure 4.…”

Section: Intelligent Manufacturingmentioning

confidence: 99%

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Lin,

Wei

2023

JGELCD

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In the face of escalating resource scarcity driven by the consumption of non-renewable resources, the industrial circular economy (ICE) emerges as a vital paradigm shift, pivotal for fostering resource-efficient societies and ensuring national resource security. This integrative review aims to critically assess the evolution and challenges inherent within the ICE over recent years, with a specific focus on the burgeoning role of machine learning (ML) in this domain. By synthesizing extant literature, this examination reveals several key findings. Firstly, the ICE significantly contributes to cost reduction through enhanced recycling and secondary utilization, underscoring its environmental stewardship. Secondly, it is evident that ML exhibits substantial promise in the manufacturing sector, not only augmenting production processes but also elevating product precision, reducing defect rates, and minimizing the likelihood of production mishaps. Most crucially, the application of ML within the ICE is identified as a potent catalyst, driving advancements across various facets -data analysis, model development, technological innovation, and equipment refinement. This analysis further elucidates the intrinsic value of ML in resource recycling and waste management, yielding improvements in resource recycling rates and methodologies, which in turn curtails production costs and amplifies output efficiency. Despite the strides made in replacing traditional industrial models with more sustainable ICE practices, challenges persist, particularly regarding the suboptimal levels of resource recycling and the continued generation of industrial waste. The integration of ML within ICE frameworks is posited as a transformative approach, offering not only enhanced resource recycling capabilities and superior product quality but also a sustainable trajectory for future industrial development. This study, therefore, contributes to the growing discourse on sustainable industrial practices, underscoring the synergistic potential of ML in revolutionizing the ICE, thereby aligning with the broader objectives of sustainable economic development.

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Section: Intelligent Manufacturingmentioning

confidence: 99%

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Lin,

Wei

2023

JGELCD

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“…Modular product family architecture (PFA) offers several advantages in achieving product sustainability and meeting the diverse needs of consumers [7]. PFA relies on product platforms organized in family formats, where a variety of modules are selected to create product variations tailored to different market segments [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sustainable Manufacturing for Product Family Architecture in Intelligent Manufacturing: A Hierarchical Joint Optimization Approach

Ma,

Chen,

2024

Sustainability

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As consumers and governments prioritize cost-effectiveness and ecological sustainability, the limitations of traditional manufacturing paradigms become apparent in the context of constrained resources. The adverse effects of these paradigms on the environment and society hinder the achievement of a sustainable product life cycle. Intelligent manufacturing processes offer a solution by efficiently gathering meaningful data, such as usage and product recycling information, from previous product generations to enhance product design and subsequent sustainable manufacturing processes (SMPs). Modular product family architecture (PFA) design holds promise in promoting product sustainability and addressing diverse consumer needs. PFA design and SMPs are inherently interconnected within intelligent manufacturing frameworks. This paper aims to integrate the decision-making processes underlying PFA with SMPs. We model integrated PFA and SMP decisions as a Stackelberg game, which involves hierarchical joint optimization (HJO) for assessing product modularity and sustainable manufacturing fulfillment. We develop a bilevel 0–1 integer nonlinear programming model to represent the HJO decision-making process and propose a nested genetic algorithm (NGA) to solve the HJO problem. A case study with a laptop is conducted to validate the feasibility and potential of the proposed HJO model for joint optimization problems in PFA design and SMPs.

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“…By embracing the principles of remanufacturing, the low-carbon e-commerce closed-loop supply chain minimizes waste and environmental pollution [11]. It aims to transform WEEEs into functional, high-quality refurbished products that meet consumer demands [12]. This approach not only reduces the environmental impact associated with e-waste disposal but also contributes to carbon emissions reduction by decreasing the energy and resource consumption attributed to new manufacturing [13].…”

Section: Introductionmentioning

confidence: 99%

Evolution and Equilibrium of a Low Carbon E-commerce Closed-Loop Supply Chain Recycling System: An Evolutionary Game Approach

Ruimin,

Lifei

2023

Preprint

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The rapid advancement of technology has led to increased electronic consumption and a corresponding rise in e-waste generation. To address this issue, this study develops a low-carbon e-commerce closed-loop supply chain (LCE-CLSC) system with remanufacturer, platform, and the government as key stakeholders. An evolutionary game approach is employed to investigate the system's dynamics, analyzing the impact of stakeholder strategies on system equilibrium. Numerical simulations reveal important findings: Firstly, government initiatives promoting recycling, carbon taxes on traditional products, can incentivize remanufacturers to produce low-carbon items and encourage platform investment in recycling services. Secondly, higher sales revenues motivate remanufacturers to produce low-carbon products. Thirdly, consumer sensitivity to recycling services strengthens waste recovery when platforms increase investments in these services, leading to remanufacturers choosing low-carbon production. Lastly, government subsidies may become ineffective if the cost of remanufacturers' carbon reduction and platforms' recycling services exceeds their affordability. These findings offer insights for the sustainable development of obsolete electronic product recycling systems.

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On the development of a smart architecture for a sustainable manufacturing-remanufacturing system: A literature review approach

Cited by 14 publications

References 112 publications

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Optimal Sustainable Manufacturing for Product Family Architecture in Intelligent Manufacturing: A Hierarchical Joint Optimization Approach

Evolution and Equilibrium of a Low Carbon E-commerce Closed-Loop Supply Chain Recycling System: An Evolutionary Game Approach

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