A simulation-based optimization approach for network design: The Circular Economy perspective

Author(s):  
Alperen Bal ◽  
Fazleena Badurdeen
2021 ◽  
Vol 28 (97) ◽  
pp. 284-318
Author(s):  
Michael C. Jones ◽  
Thomas A. Mazzuchi ◽  
Shahram Sarkani

The Department of Defense (DoD) operates a world-wide supply chain, which in 2017 contained nearly 5 million items collectively valued at over $90 billion. Since at least 1990, designing and operating this supply chain, and adapting it to ever-changing military requirements, are highly complex and tightly coupled problems, which the highest levels of DoD recognize as weaknesses. Military supply chains face a wide range of challenges. Decisions made at the operational and tactical levels of logistics can alter the effectiveness of decisions made at the strategic level. Decisions must be made with incomplete information. As a result, practical solutions must simultaneously incorporate decisions made at all levels as well as take into account the uncertainty faced by the logistician. The design of modern military supply chains, particularly for large networks where many values are not known precisely, is recognized as too complex for many techniques found in the academic literature. Much of the literature in supply chain network design makes simplifying assumptions, such as constant per-unit transportation costs regardless of the size of the shipment, the shipping mode selected, the time available for the delivery, or the route taken. This article avoids these assumptions to provide an approach the practitioner can use when designing and adapting supply chain networks. This research proposes a simulation-based optimization approach to find a near-optimal solution to a large supply chain network design problem of the scale faced by a theater commander, while recognizing the complexity and uncertainty that the practicing military logistician must deal with.


2017 ◽  
Vol 107 (04) ◽  
pp. 288-292
Author(s):  
M. Kück ◽  
J. Ehm ◽  
T. Hildebrandt ◽  
M. Prof. Freitag ◽  
E. M. Prof. Frazzon

Der Trend zur Fertigung individualisierter Produkte in kleinen Losgrößen erfordert hochflexible Produktionssysteme. Durch die damit verbundene Systemdynamik wird die Reihenfolgeplanung zu einem komplexen Planungsproblem. Der Beitrag beschreibt ein simulationsbasiertes Optimierungsverfahren, welches Echtzeitinformationen zur adaptiven Selektion geeigneter Prioritätsregeln verwendet. Das Potenzial des Ansatzes wird anhand eines Anwendungsfalls aus der Halbleiterindustrie demonstriert.   The trend to manufacturing individualized products in small-scale series demands highly flexible production systems. Because of the dynamic nature of such production systems, scheduling becomes a complex planning problem with frequent need for rescheduling. This article describes a data-driven simulation-based optimization approach using real-time information for adaptive job shop scheduling. The potential of the approach is demonstrated by a use case from semiconductor industry.


2018 ◽  
Vol 108 (04) ◽  
pp. 221-227
Author(s):  
T. Donhauser ◽  
L. Baier ◽  
T. Ebersbach ◽  
J. Franke ◽  
P. Schuderer

Die Kalksandsteinherstellung weist aufgrund prozesstechnisch und zeitlich divergierender Teilprozesse einen hohen Planungs- sowie Steuerungsaufwand auf. Durch Einsatz eines simulationsgestützten Optimierungsverfahrens kann diese Komplexität bewältigt werden. Um bei hoher Lösungsqualität eine Laufzeit zu erreichen, die einen operativen Einsatz des Verfahrens gestattet, wird auf Basis einer vorangegangenen Studie ein Dekompositionsansatz implementiert und dessen Eignung durch Testläufe validiert.   Calcium silicate masonry production requires a great deal of planning and control due to the fact that subprocesses vary in terms of process technology and time. To overcome this complexity, a simulation-based optimization approach is applied. As a short runtime that allows the method to be used operationally and yet still offers a high quality of solution is crucial, a decomposition approach is implemented on the basis of a previous study and its suitability is validated by means of test runs.


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