Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.

The recent trend in collaborative distributed manufacturing scheduling (CDMS) has gained significant importance in extended, networked, and virtual manufacturing environments due to its adaptability and integration potential. In a distributed manufacturing environment, CDMS can occur within a single factory or across multiple companies in a dynamic and variable extended or virtual organization. For effective collaboration, the CDMS system must be secure, transparent, and trustworthy. This paper proposes a blockchain-based model for CDMS and discusses its implementation in the processing of manufacturing functions, specifically joint process planning and scheduling. An illustrative example is used to demonstrate the application of the proposed approach and its potential to enhance the management processes of CDMS enterprises. The results of the analysis indicate that the proposed blockchain approach can effectively facilitate communication and integration among CDMS enterprises. Additionally, the approach can be expanded to more complex manufacturing environments under different conditions.

INTRODUCTION: The significance of handling Reverse Logistics (RL) within the electronic goods sector has increased due to the emphasis on eco-friendly product disposal and the retrieval of components, driven by environmental concerns and regulatory demands. The refurbished and used mobile phone market size is valued at US$ 53.81 billion in 2022 and is expected to grow at a CAGR of 10.8% during the forecast period, reaching US$ 120.96 billion by 2030. With a lot of emphasis given on the RL aspects. OBJECTIVES: This paper proposes a framework that uses blockchain based Hyperledger that records the life cycle history of the electronic gadget on an immutable ledger. Additionally, a neural network helps to calculate the quality index of the gadget and also the price. METHODS: Quality Index (QI) considers various sensory data into account and estimates the status of the gadget with certain accuracy. Smart contract provides automated transaction options between targeted stakeholders that helps to mitigate the security issues that happened among the stakeholders of the RL process. The integrated frameworks act as a decision maker for evaluating the condition of the mobile product or further movement into the RL process. RESULTS: In all, the traceability of product life cycle history assists in ensuring the quality of the returned products therefore optimizing the traditional quality inspection processes involved in reverse logistics of the electronic goods sector. The integration of blockchain and neural networks creates a robust and private ecosystem for stakeholders involved in reverse logistics. CONCLUSION: The stakeholders of the entire mobile electronic gadget experience a transparent system that helps to achieve sustainable reverse logistics principles.