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...

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Bibliographic Details
Published in:Applied sciences Vol. 11; no. 14; p. 6314
Main Authors: Ramakurthi, Veera Babu, Manupati, V. K., Machado, José, Varela, Leonilde
Format: Journal Article
Language:English
Published: Basel MDPI AG 08.07.2021
Subjects:
Classification
Decision making
Employment
Energy consumption
Genetic algorithms
Integrated approach
Interoperability
Manufacturing
Mathematical models
moth flame optimization algorithm
Naive Bayes
network-based distributed manufacturing systems
Ontology
Optimization
Process planning
Product life cycle
random forest
Scheduling
Suppliers
Support vector machines
text mining
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app11146314