The biomass supply chain is a multiple-segment chain characterized by prominent complexity and uncertainty, and as such, it requires increased managerial efforts as compared to the case of a single operation management. This paper deals with the supply chain management of green (e.g. grass) biomass. Specifically, three different supply chain systems, in terms of machinery configurations, were analyzed and evaluated in terms of task times and cost performance. By using a functional modeling methodology, the structural representations of the systems, in terms of activities, actions, processes, and operations, were generated and implemented by the ExtendSim® simulation software. It was shown that the models can identify the bottlenecks of the systems and can be further used as a decision support system by testing various alternatives, in terms of the resources used and their dimensioning. Finally, the models were evaluated against the sensitivity on input parameters which are known with a level of uncertainty, i.e. the expected yield and the expected machinery performance. © 2016 Elsevier B.V..

Functional modeling for green biomass supply chains

BUSATO, Patrizia;BERRUTO, Remigio;BOCHTIS, DIONYSIS
2016-01-01

Abstract

The biomass supply chain is a multiple-segment chain characterized by prominent complexity and uncertainty, and as such, it requires increased managerial efforts as compared to the case of a single operation management. This paper deals with the supply chain management of green (e.g. grass) biomass. Specifically, three different supply chain systems, in terms of machinery configurations, were analyzed and evaluated in terms of task times and cost performance. By using a functional modeling methodology, the structural representations of the systems, in terms of activities, actions, processes, and operations, were generated and implemented by the ExtendSim® simulation software. It was shown that the models can identify the bottlenecks of the systems and can be further used as a decision support system by testing various alternatives, in terms of the resources used and their dimensioning. Finally, the models were evaluated against the sensitivity on input parameters which are known with a level of uncertainty, i.e. the expected yield and the expected machinery performance. © 2016 Elsevier B.V..
2016
122
29
40
www.elsevier.com/inca/publications/store/5/0/3/3/0/4
Biomass harvesting; Logistics; Operations management; Simulation; Agronomy and Crop Science; Horticulture; Forestry; Computer Science Applications1707 Computer Vision and Pattern Recognition; Animal Science and Zoology
Pavlou, D.; Orfanou, A.; Busato, P.; Berruto, R.; Sørensen, C.; Bochtis, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1618986
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