A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems

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 project is funded by Department of Science and Technology, Science and Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT–Portuguese Foundation for Science and Technology within the R&D Units Projects Scopes: UIDB/00319/2020, UIDP/04077/2020, and UIDB/04077/2020

Leveraging Blockchain to Support Collaborative Distributed Manufacturing Scheduling

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

Leveraging Blockchain to Support Collaborative Distributed Manufacturing Scheduling

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