Towards a Semiconductor Supply Chain Simulation Library (SCSC-SIMLIB) Jingjing Yuan, Thomas Ponsignon, Infineon Technologies AG. Winter Simulation Conference, 2014.

Simulation is a widely used technique for analyzing and managing supply chains. Simulation software packages offer standard libraries for selected functions and application areas. However, no commercial or freeware simulation tool proposes building blocks specific to semiconductor manufacturing.
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Logistics Simulation and Optimization for Managing Disaster Responses F. Barahona, M. Ettl, M. Petrik, P.M. Rimshnick, IBM T.J. Watson Research Center, Proceedings of the 2013 Winter Simulation Conference

Catastrophic events such as hurricanes, earthquakes or floods require emergency responders to rapidly distribute emergency relief supplies to protect the health and lives of victims. In this paper we develop a simulation and optimization framework for managing the logistics of distributing relief supplies in a multi-tier supply network. The simulation model captures optimized stocking of relief supplies, distribution operations at federal or state-operated staging facilities, demand uncertainty, and the dynamic progression of disaster response operations. We apply robust optimization techniques to develop optimized stocking policies and dispatch of relief supplies between staging facilities and points of distribution. The simulation framework accommodates a wide range of disaster scenarios and stressors, and helps assess the efficacy of response plans and policies for better disaster response.
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Supply chain and hybrid modeling: the Panama Canal operations and it’s salinity diffusion Mario Marin, Yanshen Zhu (American Technologika); Luz Alba Andrade, Erwin Atencio, Carlos Boya (Universidad La Latina); Carlos Mendizabal (Universidad Tecnologica de Panama). Proceedings of the 2010 Winter Simulation Conference.

This paper deals with the simulation modeling of the service supply chain and the salinity and its diffusion in the Panama Canal. An operational supply chain model was created using discrete-event simulation. Once complete, a component based on differential equations was added to the model to investigate the intrusion of salt and the resulting salinity diffusion into the lakes of the canal. This component was implemented in the AnyLogic simulation modeling environment by taking advantage of the concept of hybrid modeling that is embedded in AnyLogic.
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Supply chain multi-structural (re)-design Ivanov D.A., International Journal of Integrated Supply Management, No. 5(1), 19-37, 2009

In the framework of supply chain (re)- design (SCD), different structures (functional, organizational, informational, etc.) are (re)- formed. These structures are interrelated and change in their dynamics. How is it possible to avoid structural incoherency and consistency and to achieve comprehensiveness by (re)- designing supply chains? This paper introduces a new approach to simultaneous multi-structural SCD with structure dynamics considerations. We elaborate a new conceptual model and propose new tools for multi-structural SCD – multi-structural macro-states and dynamical alternative multi-graphs. The research approach is theoretically based on the combined application of operations research, agent-based modelling, and control theory. The results show the multi-structural and interdisciplinary treatment allows comprehensive and realistic SCD problem formulation and solution. We emphasize the flexibility of the proposed approach and optimization-supported simulation. The proposed methodology enhances managerial insight into supply chains at the strategic and tactical levels and serves to assist decision-makers in SCD
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A hybrid simulation optimization approach for supply chains Christian Almeder, Margaretha Preusser. EUROSIM 2007, September 9-13, Ljubljana, Slovenia

The main idea of our approach is to combine discrete-event simulation and exact optimization for supply chain network models. Simulation models are constructed in order to mimic a real system including all necessary stochastic and nonlinear elements. Such simulation models are used as proving grounds for analyzing and improving a real situation on a trial-and-error basis. A traditional optimization method on top of a simulation model has major disadvantages: The optimization method uses the simulation model as a black-box. Information about the structure of the problem is not available and cannot be used for an intelligent optimization strategy
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