Manufacturing Networks Research Articles

Active vaccine safety surveillance in low- and middle-income countries: Challenges for vaccine manufacturers from emerging countries.

Developing Countries Vaccine Manufacturers Network (DCVMN) is an alliance of vaccine developers, manufacturers, and marketing authorization holders (MAHs) from low- and middle-income countries (LMICs) that plays a vital role in ensuring equitable, inclusive, accountable, and timely access to affordable, high-quality vaccines in these countries. Besides research and development, this network promotes manufacturing and global supply chains for effective strengthening of regulatory and pharmacovigilance activities. Traditionally, vaccine safety surveillance systems in LMICs rely on spontaneous reporting. However, especially in resource-limited settings, robust passive surveillance is lacking, and active vaccine safety surveillance (AVSS) can complement passive surveillance by actively collecting adverse events at sentinel sites or via formally designed observational (non-interventional) studies. The rapid introduction of novel vaccines during the COVID-19 pandemic with rather limited safety information at deployment accelerated the need for comprehensive AVSS in LMICs to detect potential safety concerns that may not have been identified in pre-licensure trials. In this context, national regulatory agencies (NRAs) and the World Health Organization (WHO) prequalification team requested risk management plans (RMPs) in line with Pharmacovigilance Planning guideline. The submitted RMPs contained the companies' commitments to pharmacovigilance activities encompassing both post-approval routine surveillance (passive) and additional active surveillance activities. These AVSS activities were either committed voluntarily by the manufacturers/MAHs or imposed by the NRA/WHO prequalification in case of important identified or potential risks, or important missing information. Unlike passive surveillance, AVSS relies on various epidemiological methodologies that require resources and expertise. DCVMN initiated a "learn-by-doing" project to support manufacturers/MAHs in performing AVSS. This project focused on improving the understanding of AVSS and its tools, investigated the support needs, opportunities, challenges, and barriers to performing AVSS activities in LMICs, and proposed solutions that could be used to mitigate the main challenges in performing AVSS activities in these countries.

Navigating through uncertainties: coordinating digital transformation in international manufacturing networks

PurposeCoordination of digital transformation within globally dispersed factories belonging to international manufacturing networks (IMNs) is essential for competitiveness. This paper explores how digital transformation necessitates changes in the coordination of IMNs.Design/methodology/approachA case study is conducted with three Swedish manufacturing companies, thus adding to the limited empirical research covering the examined research field. Data analysis uses the technology-organization-environment (TOE) framework.FindingsThe results highlight 15 digital transformation attributes linked by intermediate themes to 13 changes in the coordination of IMNs and provide concrete industry examples. Four major themes emerged as significant in the coordination of IMNs: increased speed of technology development and rollout, amplified emphasis on a global mindset, increased need to adapt the organizational structures to enable collaboration and a higher degree of uncertainty.Originality/valueAlthough coordination of IMNs is acknowledged as directly related to competitive advantage, the ways digital transformation necessitates changes in the coordination of IMNs have been missing in contemporary research. This research decreases this omission.

Green manufacturing electrical network scheduling using Slime Mould and Interior search algorithms

The strategic reduction of power consumption in electrical machines, particularly within manufacturing networks, is critical for achieving energy efficiency and sustainability. Bio-inspired algorithms have demonstrated remarkable potential for optimization, with their adaptability to specific problem domains being a key factor in their success. This study employs discrete optimization algorithms tailored for scheduling in an electrical manufacturing network modeled as a hybrid flow shop, where energy efficiency is paramount. Experimental evaluations were conducted on a customized hybrid flow shop benchmark comprising approximately 45 cases and 900 instances, utilizing a random distribution of hourly energy consumption and a specific tariff structure for monthly usage. The findings demonstrate the efficacy of the proposed discretized metaheuristics in significantly reducing power consumption costs while maintaining a minimized total makespan. This work highlights the potential of bio-inspired algorithms for green hybrid flow shop scheduling within electrical networks, contributing to enhanced operational efficiency, sustainability, and energy conservation in modern manufacturing environments.

Building a collaborative manufacturing system’s network resilience through an adaptability potential analysis

PurposeThis research aims to investigate how adaptability potential analysis in collaborative manufacturing networks can be used to enhance resilience when addressing innovative production challenges that require change initiatives.Design/methodology/approachA literature review on collaborative adaptive systems showed a lack of solutions to adapt production to an unpredicted product within a network. A framework is proposed for evaluating the adaptability of collaborative networks and providing inter-system adaptation recommendations. We demonstrate the applicability of this approach through an illustrative experimentation involving a cosmetic company seeking to produce a new product, hydroalcoholic gels in the context of collaborative networks.FindingsThe experimentation demonstrates that the adaptability analysis based on ontology can help different manufacturing systems make decisions based on their state and limits of capabilities. Also, our adaptation recommendations may help understand the economic impacts of collaboration for different scenarios before launching.Research limitations/implicationsThe research scope does not extend to the consideration of quantity and operational aspects. Additionally, the reconfigurability aspects within each manufacturing system, such as the reordering of layout sequences, have not been addressed yet.Practical implicationsThe results allow organizations to compare resilience states from an individual and collaborative perspective, enabling them to make informed decisions about new production opportunities and effectively navigate the changing manufacturing landscape.Originality/valueThis research combines capability-based analysis and a collaborative network perspective to streamline decision-making for systems facing new production demands. It provides new insights into effectual decision-making, empowering organizations to skilfully manage unexpected challenges and identify suitable partners accordingly.

FPGA-Based Sensors for Distributed Digital Manufacturing Systems: A State-of-the-Art Review.

The combination of distributed digital factories (D2Fs) with sustainable practices has been proposed as a revolutionary technique in modern manufacturing. This review paper explores the convergence of D2F with innovative sensor technology, concentrating on the role of Field Programmable Gate Arrays (FPGAs) in promoting this paradigm. A D2F is defined as an integrated framework where digital twins (DTs), sensors, laser additive manufacturing (laser-AM), and subtractive manufacturing (SM) work in synchronization. Here, DTs serve as a virtual replica of physical machines, allowing accurate monitoring and control of a given manufacturing process. These DTs are supplemented by sensors, providing near-real-time data to assure the effectiveness of the manufacturing processes. FPGAs, identified for their re-programmability, reduced power usage, and enhanced processing compared to traditional processors, are increasingly being used to develop near-real-time monitoring systems within manufacturing networks. This review paper identifies the recent expansions in FPGA-based sensors and their exploration within the D2Fs operations. The primary topics incorporate the deployment of eco-efficient data management and near-real-time monitoring, targeted at lowering waste and optimizing resources. The review paper also identifies the future research directions in this field. By incorporating advanced sensors, DTs, laser-AM, and SM processes, this review emphasizes a path toward more sustainable and resilient D2Fs operations.

Comparative assessment of a COVID-19 vaccine after technology transfer to Iran from critical quality attributes to clinical and immunogenicity aspects

During COVID-19 pandemic, international pharmaceutical companies put effort to build global manufacturing networks for vaccines. Soberana Plus vaccine, a recombinant protein based vaccine (RBD dimer), with the trade name of PastoCovac Plus in Iran, is based on a protein subunit platform in Cuba and completed preclinical and toxicological assessments. This study aimed at presenting the steps of vaccine technology transfer from Cuba to Iran. This study provides the first practical comparability results in Iran to ensure the quality, safety and efficacy of a protein subunit vaccine against COVID-19 after a successful technology transfer from Cuba. PastoCovac Plus was transferred to Iran at the formulation stage. The assessment of the active ingredient pharmaceutical (API) was achieved through physicochemical and clinical data collection and tests to assure if there was any adverse impact on the vaccination results. In order to assess the quality of the vaccine product after technology transfer, we sought different properties including regulatory features, physicochemical quality, vaccine potency and stability as well as its immunogenicity and safety. Following the evaluation of the clinical quality attributes (CQAs) based on the standard protocols, the results showed that the two vaccines are highly similar and comparable, with no considerable effect on safety or efficacy profiles. The CQAs were all in the acceptance limits in terms of safety and efficacy as well as clinical evaluation results. The immunogenicity evaluation also confirmed no significant differences between the vaccines regarding reinfection (P = 0.199) or vaccine breakthrough (P = 0.176). Furthermore, the level of anti-spike and neutralizing antibodies in the both vaccine groups was not significantly different indicating the equality of performance between the two vaccines. According to the results of the quality and clinical assessment of this study, we achieved an acceptable quality attributes and acceptant limits in terms of safety and efficacy of the vaccines pre and post technology transfer.

Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing

AbstractSelf-organizing manufacturing network has emerged as a viable solution for adaptive manufacturing control within the mass personalization paradigm. This approach involves three critical elements: system modeling and control architecture, interoperable communication, and adaptive manufacturing control. However, current research often separates interoperable communication from adaptive manufacturing control as isolated areas of study. To address this gap, this paper introduces Knowledge Graph-enhanced Multi-Agent Reinforcement Learning (MARL) method that integrates interoperable communication via Knowledge Graphs with adaptive manufacturing control through Reinforcement Learning. We hypothesize that implicit domain knowledge obtained from historical production job allocation records can guide each agent to learn more effective scheduling policies with accelerated learning rates. This is based on the premise that machine assignment preferences effectively could reduce the Reinforcement Learning search space. Specifically, we redesign machine agents with new observation, action, reward, and cooperation mechanisms considering the preference of machines, building upon our previous MARL base model. The scheduling policies are trained under extensive simulation experiments that consider manufacturing requirements. During the training process, our approach demonstrates improved training speed compared with individual Reinforcement Learning methods under the same training hyperparameters. The obtained scheduling policies generated by our Knowledge Graph-enhanced MARL also outperform both individual Reinforcement Learning methods and heuristic rules under dynamic manufacturing settings.

Minimizing the sum of earliness and tardiness in the multi-factory two-stage assembly scheduling problem

ABSTRACT The geographical dispersion of collaborative factories can provide cost-saving potential for manufacturers and lead them to easier fit the global markets. On the other hand, in such networks of factories, coordination is especially important for the just-in-time delivery of orders. This study investigates a new configuration of factories in a network of collaborative manufacturing. In the first stage, some independent suppliers produce and deliver the different components of the final products to the assembly factory. Each supplier can produce a particular component of a final product. In the assembly factory, the final products are assembled. The objective is to determine the optimal schedule of the jobs in each factory to minimize the sum of earliness and tardiness. A mixed-integer formulation for this problem is proposed, which can find the optimal solution for the small-size instances. The iterated local search methods are also developed to cope with larger instances. Computational experiments show that the iterated local search methods outperform the well-known iterated greedy method in literature.

Exploring the content of company-specific plant role models in international manufacturing networks

PurposeThis study explores the phenomenon of company-specific plant role models. Following the establishment of distinct lead factories, recent studies imply that multinational companies are now using more detailed plant roles to manage manufacturing networks. While multiple plant role typologies can be found in literature, this is, to our knowledge, the first article to systematically analyze the content of such company-specific plant roles.Design/methodology/approachWe rely on a multiple-case study design of 29 plant role models primarily belonging to multinational companies headquartered in German-speaking countries. Coding is performed to analyze the models for similarities and differences.FindingsThe content of company-specific plant role models seems highly dependent on a company’s context; hence, there is no one best way to build them. Companies appear to be pursuing different priorities with the application of plant roles, for example, the reorganization of manufacturing networks, the systematic allocation of products or the definition of decision-making autonomy. Compared to the primarily two-dimensional models from theory, companies rely on more dimensions to make their plant roles practical. Similar to models from literature, competence bandwidth, inter-plant knowledge/information flow and location advantage are the most relevant dimensions to differentiate plants from a headquarters’ perspective.Practical implicationsPlant role models are a powerful management tool that enables delayering the complexity and allows for proactive management of manufacturing networks. Managers fit the content of their plant role models to their company-specific context and the intended application for manufacturing networks. Managers aiming to build company-specific plant roles can use the conceptual framework based on the empirical findings as a benchmark.Originality/valueThis study complements prior research on plant roles by acknowledging that companies might develop their own version of plant role models rather than copying the ones from the literature. By doing so, we explore how plant roles look like in a real-world setting.

Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing

Purpose This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

AIoT-enabled defect detection with minimal data: A few-shot learning approach combining prototypical and relational networks for smart manufacturing

Defect detection is crucial in manufacturing processes but traditional AI-based algorithms require large datasets for accurate results. For new or customized products, the number of images with detected defects is limited. Therefore, we developed a few-shot learning approach integrating a prototypical and relation network (PRN), algorithms with meta-learning, and the Artificial Internet of Things (AIoT). For rapid defect detection with IoT sensors, such minimal data are used for a smart manufacturing ecosystem., making it ideal for dynamic production environments. We tested the AIOT-enhanced PRN on two datasets using the following data augmentation methods: random rotation and horizontal translation (RH), random rotation and vertical translation (RV), and horizontal and vertical translation (HV). The developed PRN efficiently learned from minimal data to reduce the occurrence of overfitting issues in the MVTec 3D-AD dataset which are caused by a limited number of defect sample images. When testing the AIOT-enhanced PRN with the NEU-DET dataset, accuracies in 5-way 5-shot settings using RV, RH, 15° rotation, and HV were 100 %. Under Gaussian noise, the AIOT-enhanced PRN showed an accuracy of 100 % in 5-way 5-shot and 5-way 1-shot scenarios using HV. For salt-and-pepper noise, the accuracy of the AIOT-enhanced PRN ranged from 98.49 to 99.04 %. The developed AIOT-enhanced PRN improved defect detection accuracy and real-time monitoring capability with minimal data. The developed AIOT-enhanced PRN can be used for efficient and flexible product quality control in Industry 4.0.

Manufacturing system evaluation in terms of system reliability via long short-term memory

With the development of system reliability evaluation, more and more studies are published by considering more attributes or adopting different calculation methods. However, existing deep learning (DL) approaches for predicting system reliability of a multi-state manufacturing network (MMN) only considering single attribute, such as time or machine failure. A more comprehensive consideration about an MMN is deliberated in this study. The Long Short-Term Memory is then utilized to construct the prediction model to process time series of an MMN data. Through the experimental results, the proposed prediction model outperforms than the existing method and other DL method in terms of root mean square error and mean absolute error. Moreover, the parameter of an MMN and hyperparameter of the proposed prediction model are discussed to investigate the optimized combination for system reliability prediction.

Pharmaceutical capacity expansion under uncertainty: Framework and models

This work presents a methodology for capacity planning under uncertainty in general multi-stage manufacturing networks. Multiple manufacturing lines are available and the production time on each line must be divided between a set of materials. The methodology generates mixed-integer linear programming models that represent capacity expansions with economy of scale costs functions and production planning details. A deterministic model is solved to create a baseline and the impact of uncertainty is investigated by sensitivity analysis and stochastic programming. The applicability of the methodology is exemplified through two case studies derived from industrial pharmaceutical manufacturing. The methodology identifies bottlenecks that limit supply and, where required, activates, and assigns capacity expansion projects for satisfying demand subject to uncertainty. The methodology determines the best use of existing resources and the location and size of capacity expansions thereby generating a portfolio of recommendations for decision making on integrated planning of capacity and production.

Automatic blockchain network configuration and optimisation for credible and authorised data sharing in social manufacturing networks

The management of social manufacturing networks (SMN) faces sophisticated situations due to the customer's personalisation demands, leading to reduced trust among entities. Therefore, credible and authorised data sharing among different participants becomes vital to operate SMN well. Although blockchain has been applied to SMN to ensure credibility and authority, there is little work on how to configure and optimise a blockchain network for SMN, especially when the structure of SMN changes over time. This paper discusses an automatic blockchain network configuration and optimisation mechanism to enable credible and authorised data sharing in SMN. First, a system architecture for automatic blockchain network configuration and optimisation is built. Then, the structure of SMN and blockchain network model are established using complex network and hypergraph. Then, an automatic blockchain network configuration mechanism is studied to configure blockchain networks from certain SMN structures. Afterward, an automatic blockchain network optimisation mechanism is studied to optimise blockchain networks for SMN when dynamic events occur. Finally, the mechanisms are validated through a case study of SMN to produce air conditioners. The results show that our mechanisms can help core enterprises efficiently deploy a blockchain network for credible and authorised data sharing among different participants in the SMN.

The Prospects of AfCFTA in Promoting Industrialization and Economic Diversification in Africa

African Continental Free Trade Area (AfCFTA) functions as a boost for Africa's economic development by enhancing intra-African trade. This is because trade is an engine of growth and development. For the fact that most African nations that are located within the same region engage in similar and competitive production, the ensuing economic cooperation and trade exchanges not only have the promise of increasing trade but also stimulating other ancillary economic activities, creating new job opportunities, speeding up economic growth, promote the standard of living, and promote economic development. However, AfCFTA's record has not been very impressive. The main obstacles to intra- regional commerce among African countries include heavy reliance on trade in primary goods, high product and market concentration of exports, and inadequate regional manufacturing networks. This study therefore examines continental economic trends and Intra African Trade: Prospects for African Continental Free Trade Area. The study adopted qualitative methods of data collection and analysis and the comparative theory of integration. The study found that the low level of trade diversification implicated the capacity of the African Continental Free Trade Area (AfCFTA) to achieve improved intra-regional trade performances in Africa. African countries maintain as much as 77% export specialization, with higher external/world trade at 84.6 percent of their total import and export trade. The study recommended that Governments should invest in infrastructure development, including building roads, railways, ports, and airports, as well as improving access to electricity and broadband. Governments should adopt modern trade facilitation measures such as electronic customs systems, single window platforms, and risk management techniques and should align their technical regulations and norms with global best practices and standards, as those created by appropriate international standard-setting organisations like the International Organisation for Standardisation (ISO).

Designing a resilient cloud network fulfilled by reinforcement learning

Cloud manufacturing networks operate in stochastic environments, making them susceptible to various disruptions. Designing a resilient cloud network that can withstand and recover from disruptions while maintaining an efficient service composition is crucial. This paper emphasizes the importance of developing resilient cloud manufacturing networks, particularly in the context of unprecedented challenges. It explores the complexities of service composition using reinforcement learning algorithms, specifically Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). SAC has emerged as a standout performer, demonstrating superior capabilities in enhancing network resilience. The model’s verification is conducted by investigating and comparing the convergence of the results. This research not only examines these reinforcement learning methodologies but also broadens its scope to provide ongoing development and evaluation of a robust network. The model is applied to a COVID-19 case study to validate the results. Beyond theoretical frameworks, this research highlights the need for adaptive and resilient manufacturing systems, offering insights into real-world challenges. The results emphasize the significance of using RL algorithms, particularly when customer satisfaction is the top priority. Moreover, when logistic costs and costs of changing servers are lower than customer dissatisfaction, the use of RL algorithms becomes essential for addressing disruption scenarios. The findings highlight the critical role of collaboration in intertwined supply networks in helping a disrupted network withstand and recover from a disruption.

Delivering HIV and TB services amidst the COVID-19 pandemic in Indonesia: a qualitative study of challenges and mitigation strategies

Background Indonesia has the second-highest burden of tuberculosis (TB) globally and is experiencing one of the fastest-growing HIV epidemics worldwide. The COVID-19 pandemic disrupted access to essential health services, including those for TB and HIV, due to health system overload, social distancing measures, and negative economic repercussions on the health sector and the population. An in-depth understanding of these challenges and the health system responses to mitigate the negative impact of the pandemic on TB and HIV services is crucial to building health system resilience and preparing for future emergencies. Methods This qualitative study, conducted in two cities in Indonesia, explored the impact of the COVID-19 pandemic on TB and HIV services including mitigation strategies to sustain the provision of testing and treatment in the midst of the pandemic. Between February and June 2022, semi-structured interviews were conducted with 16 healthcare workers and 16 clients at nine health centers (puskesmas), three hospitals, and one Community Center for Lung Health in Bandung and Yogyakarta. Themes were identified from transcripts using open and selective coding and then refined. Results Extraordinary measures were implemented in health facilities to sustain TB and HIV service delivery, and prevent the spread of COVID-19, including testing of clients for COVID-19 before receiving medical care, physical distancing when visiting healthcare facilities, revised schedules for medicine dispensing, involvement of community health workers and peers in community outreach activities such as the home delivery of medicines, and the use of telemedicine. Challenges encountered during the implementation of these strategies included medicine stock-outs, health worker overload, lack of sufficient client-provider interaction and technical difficulties when implementing telemedicine, and the risk or fear of exposure to COVID-19 among the community health workers and peers. Conclusions Significant efforts were made to mitigate disruptions to TB and HIV services during the COVID-19 pandemic. However, some challenges were encountered. Key policy recommendations to strengthen pandemic preparedness include investing in local manufacturing and robust drug supply networks to prevent medicine shortages, and supporting community health workers to alleviate workload issues, reduce the risk of disease exposure, and explore the potential for a financial incentive system. Equally vital is the need to invest in staff training and education, as well as implementing user-friendly telemedicine technologies.

Blockchain technology for advanced therapy medicinal products: Applications in tracking, data sharing, and supply chain automation

Advanced therapy medicinal products (ATMPs) like cell and gene therapies offer transformative treatment options for many diseases. However, coordinating the decentralized, patient-specific manufacturing of autologous ATMPs across multiple hospitals poses major supply chain challenges. This paper provides a comprehensive analysis of how blockchain technology can enhance decentralized ATMP manufacturing networks. First, background on ATMPs and complexities of decentralized production is reviewed. An overview of blockchain architecture, key attributes, and existing use cases then follows. The major opportunities for blockchain integration in ATMP manufacturing are discussed in depth, including tracking autologous products across locations, enabling data sharing between hospitals to power AI-based optimization, automating supply chain processes, and maintaining provenance records. Critical limitations around scalability, privacy, regulation, and adoption barriers are examined. Design considerations for developing blockchain ecosystems tailored to the unique ATMP environment are also explored. Blockchain shows immense promise for transforming visibility, coordination, automation, and data unification in decentralized ATMP manufacturing networks. Despite current challenges, blockchain is prepared to profoundly impact the advancement of personalized cell and gene therapies through enhanced supply chain instrumentation. This paper provides a comprehensive analysis of this emerging technological innovation and its applications to address critical needs in ATMP translation and manufacturing.

Operation risk assessment of Flexible Manufacturing Networks subject to quality-reliability coupling

In flexible manufacturing network (FMN), the growth of product types increases the diversity of interacting behaviors between machine reliability and product quality, and the product diversity and machine flexibility promote the explosion of processing routes and processing sequences, increasing the gap between current risk evaluation of manufacturing systems and engineering practice. To fill this gap, a novel operation risk assessment framework is constructed for the complex FMN. In detail, to model the reliability-quality interactions, a mathematical framework describing a complete manufacturing process involving "feedstock quality (Q1) - machine degradation (R) - product processing quality (Q2) - quality inspection status (I)" is proposed for complex FMN, solving the measurement of interacting behaviors between machine reliability and product quality in an FMN that suffers from both multiple process routes and multi-type products. Then, an effective evaluation framework is proposed to evaluate the connectivity risk of multiple process routes and the quality risk of multiple product types in a complex FMN, providing a concurrent solution for risk evaluation under both complex system structures and complex quality-reliability interrelationships. At last, a simulation experiment verifies the effectiveness of our proposed methods and reveals differences in the operation risk of different product types in an FMN.

Reverse logistics platform decisions integrating crowdsourced contracting: A three-level interactive optimization approach for product design considering remanufacturing

Currently most original equipment manufacturers are reluctant to remanufacture their end-of-life products due to lack of adequate infrastructure for remanufacturing profitable products. To solve the problem, we propose a three-level interactive decision-making framework for product design considering remanufacturing by establishing reverse logistics platform integrating crowdsourced contracting. The reverse logistics platform can incorporate a large population of manufacturer and consumer crowds in an extended manufacturing network. The crowdsourced manufacturing paradigm can maximize the utilization of external resources, thereby enhancing overall remanufacturing efficiency. We propose a three-level mixed 0–1 programming model based on above framework with a Stackelberg game to handle the complex decision-making process. A combination of analytical solutions and a nested genetic algorithm is developed to implement the model. A laptop case study is presented. To verify the effectiveness of our algorithm, we compare the results of the three algorithms in solving the model proposed in this paper. Furthermore, applying our proposed model to three additional cases of varying levels of complexity further validates the applicability of the model for our problem over traditional integrated optimization models and two-stage optimization models. Sensitivity analyses are conducted on three key parameters of the model, providing valuable insights for both industry and academia.