Enhancing supply chain resilience using ontology-based decision support system

Today's scenario of manufacturing and supply chain is full of uncertainty because of numerous types of disruptions and failures such as fire, storm, machine failure, are a few names. Always supply chain disruptions present disastrous impacts, although probabilities of happening are low. In the...

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Published in:International journal of computer integrated manufacturing Vol. 32; no. 7; pp. 642 - 657
Main Authors: Singh, Sube, Ghosh, Soumava, Jayaram, Jayanth, Tiwari, Manoj Kumar
Format: Journal Article
Language:English
Published: Taylor & Francis 03.07.2019
Subjects:
hybrid particle swarm optimisation
mixed integer linear programming model
ontology
semantic web rule language
Supply chain resilience
ISSN:0951-192X, 1362-3052
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Abstract Today's scenario of manufacturing and supply chain is full of uncertainty because of numerous types of disruptions and failures such as fire, storm, machine failure, are a few names. Always supply chain disruptions present disastrous impacts, although probabilities of happening are low. In the recent robust as well as flexible supply chain has captured the focus of researchers in designing a supply chain network with consideration of disruption's risk. In this work, an ontology-based decision support system is proposed to intensify the supply chain resilience during a disruption. The concept of semantic and ontology is adopted in developing the knowledge base for the entire supply chain network including manufacturing units. Protégé is used for defining the classes and sub-classes along with numerous types of properties and expressed in a rule-based system using semantic web rule language (SWRL). Furthermore, a mixed integer linear programming model with an objective of maximising quantified resilience to fulfil the demand. A hybrid particle swarm optimisation - differential evolution (PSO-DE) is utilised as an optimisation technique for the defined problem. In performing the study, a set of simulated data is formed and then interpreted in the ontology for an optimal selection of recovery activity.
AbstractList Today's scenario of manufacturing and supply chain is full of uncertainty because of numerous types of disruptions and failures such as fire, storm, machine failure, are a few names. Always supply chain disruptions present disastrous impacts, although probabilities of happening are low. In the recent robust as well as flexible supply chain has captured the focus of researchers in designing a supply chain network with consideration of disruption's risk. In this work, an ontology-based decision support system is proposed to intensify the supply chain resilience during a disruption. The concept of semantic and ontology is adopted in developing the knowledge base for the entire supply chain network including manufacturing units. Protégé is used for defining the classes and sub-classes along with numerous types of properties and expressed in a rule-based system using semantic web rule language (SWRL). Furthermore, a mixed integer linear programming model with an objective of maximising quantified resilience to fulfil the demand. A hybrid particle swarm optimisation - differential evolution (PSO-DE) is utilised as an optimisation technique for the defined problem. In performing the study, a set of simulated data is formed and then interpreted in the ontology for an optimal selection of recovery activity.
Author Tiwari, Manoj Kumar
Jayaram, Jayanth
Ghosh, Soumava
Singh, Sube
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  organization: Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur
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SubjectTerms hybrid particle swarm optimisation
mixed integer linear programming model
ontology
semantic web rule language
Supply chain resilience
Title Enhancing supply chain resilience using ontology-based decision support system
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