Konferenzbeitrag
Einbettung von Transportmodellen und diskreten Simulationsmodellen in Stoffstromnetze
Vorschaubild nicht verfügbar
Volltext URI
Dokumententyp
Text/Conference Paper
Zusatzinformation
Datum
2000
Autor:innen
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Verlag
Metropolis
Zusammenfassung
Material Flow Networks provide a wide range of options for describing and representing complex material flow systems and evaluating them efficiently for environmental management. Thus, many of the properties and much more of the scope resulting from the Material Flow Network approach can be used to gain a better insight into production, consumption, transport, waste treatment, etc. processes with regard to their effects on the environment. In Material Flow Networks these processes are represented by transitions, which are describing material transformations associated with these processes. They play a vital role in Material Flow Networks. In correspondence with the theoretical foundations of the Material Flow Network approach a material transformation can be specified using rather sophisticated models. In this sense this paper provides two examples how Material Flow Networks can be extended using methods from completely different fields of computer science. Both examples are technically based on Microsoft's Active Scripting architecture and are further using Microsoft's Component Object Model (COM). The first example describes how methods from the field of Operations Research can be used to specify a transition. It deals with a material flow analysis of a trading company and focuses on the optimisation of the route plan for the delivery of goods to the company's branches. This example shows the use of writing a script using the Python programming for specifying a transition. The second example describes the use of a discrete event simulation model (a factory where bottles are delivered for cleaning, refilling, packing and selling) within a Material Flow Network. It illustrates the combination of material and energy flow information with more dynamic performance indicators like throughput, average waiting times or mean service times.