An innovative approach for resource sharing and scheduling in a sustainable distributed manufacturing system. (April 2022)

• Record Type: Journal Article
• Authors: Ramakurthi, Veerababu; Manupati, V.K.; Machado, José; Varela, Leonilde; Babu, Suresh
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Blockchain technology -- Network-based distributed manufacturing systems -- Moth flame evolutionary optimization algorithm -- Smart contract -- Ethereum; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101620
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Secure, transparent, and sustainable distributed manufacturing system (DMS) is a pressing need for current Industry 4.0. In this paper, exchange of highly sensitive information in a more transparent and secure way and to avoid the misunderstandings and trust issues between the enterprises a smart contract based on blockchain technology has been proposed in case of a distributed manufacturing environment. Here, we used a public-permission less Ethereum platform to execute the smart contracts in the Blockchain to process the customer orders and to identify the right enterprise. Later, a multi-objective mixed-integer linear programming (MILP) model is formulated for optimal resource sharing and scheduling in a considered sustainable DMS. The objectives of the proposed model consist of simultaneously improvement of the performance measures such as makespan, machine utilization, energy consumption, and reliability. To solve this MILP model, a new Multi-objective-based Hybridized Moth Flame Evolutionary Optimization Algorithm (HMFEO) is developed and then the effectiveness of the proposed algorithm is validated with the Non-dominated Sorting Genetic Algorithm (NSGA-III). The results obtained from implementing the model using experimental data along with different cases show the efficiency and the validity of the proposed model and solution approach. Moreover, several performance indicators like hyper volume are increased by nearly 15–20 % that shows the superiority of the proposed algorithm with the NSGA-III.