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Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty
https://orcid.org/0000-0002-0424-5350
Abstract There are many processes in the business world which are dependent on Critical Infrastructure Systems (CISs) to provide important services. Hence, undesirable behaviors of these processes can provide cascading disturbances and might cause high impacts on criticality infrastructure in Automatic Closed-Loop Supply Chain (A-CLSC). Thus, this level of vulnerability needs to monitor and control the disturbances related to uncertain parameters. In order to develop the controller component inward uncertain Business Process Management System (BPMS), the authors provided adaptive controller considering uncertainty, which could be tuned with an intelligent algorithm called Improved Particle Swarm Optimization (IPSO). In addition to multi-objective optimization, the novel controller has been used to control the two most important activities includes inventory and transportation evolving the criticality infrastructure in A-CLSC at the desired behavior as a simulation and practical case study. Furthermore, the proposed IPSO algorithm has been compared against legendary methods such as adaptive PSO (APSO) and classic PSO. Also, the paper has been provided some metrics to statistical evaluation of proposed method performance. The results indicated the better set response of the new controller to control the A-CLSC as well as effective control of CISs.
Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty
https://orcid.org/0000-0002-0424-5350
Abstract There are many processes in the business world which are dependent on Critical Infrastructure Systems (CISs) to provide important services. Hence, undesirable behaviors of these processes can provide cascading disturbances and might cause high impacts on criticality infrastructure in Automatic Closed-Loop Supply Chain (A-CLSC). Thus, this level of vulnerability needs to monitor and control the disturbances related to uncertain parameters. In order to develop the controller component inward uncertain Business Process Management System (BPMS), the authors provided adaptive controller considering uncertainty, which could be tuned with an intelligent algorithm called Improved Particle Swarm Optimization (IPSO). In addition to multi-objective optimization, the novel controller has been used to control the two most important activities includes inventory and transportation evolving the criticality infrastructure in A-CLSC at the desired behavior as a simulation and practical case study. Furthermore, the proposed IPSO algorithm has been compared against legendary methods such as adaptive PSO (APSO) and classic PSO. Also, the paper has been provided some metrics to statistical evaluation of proposed method performance. The results indicated the better set response of the new controller to control the A-CLSC as well as effective control of CISs.
Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty
https://orcid.org/0000-0002-0424-5350
Abstract There are many processes in the business world which are dependent on Critical Infrastructure Systems (CISs) to provide important services. Hence, undesirable behaviors of these processes can provide cascading disturbances and might cause high impacts on criticality infrastructure in Automatic Closed-Loop Supply Chain (A-CLSC). Thus, this level of vulnerability needs to monitor and control the disturbances related to uncertain parameters. In order to develop the controller component inward uncertain Business Process Management System (BPMS), the authors provided adaptive controller considering uncertainty, which could be tuned with an intelligent algorithm called Improved Particle Swarm Optimization (IPSO). In addition to multi-objective optimization, the novel controller has been used to control the two most important activities includes inventory and transportation evolving the criticality infrastructure in A-CLSC at the desired behavior as a simulation and practical case study. Furthermore, the proposed IPSO algorithm has been compared against legendary methods such as adaptive PSO (APSO) and classic PSO. Also, the paper has been provided some metrics to statistical evaluation of proposed method performance. The results indicated the better set response of the new controller to control the A-CLSC as well as effective control of CISs.
Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty
Saraeian, Shideh (Autor:in) / Shirazi, Babak (Autor:in) / Motameni, Homayun (Autor:in)
International Journal of Critical Infrastructure Protection ; 25 ; 102-124
21.02.2019
23 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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