SIMULATION: Transactions of The Society for Modeling and Simulation International

Zakerifar, Mehdi; Biles, William E; Evans, Gerald W

doi: 10.1177/0037549711411964pmid: N/A

This paper describes the application of Kriging metamodeling in multiple-objective simulation optimization. An Arena-based simulation model of an (s, S) inventory system is utilized to demonstrate the capabilities of Kriging metamodeling as a simulation tool. Response surface methodology and Kriging metamodeling are compared to determine the situations in which one approach might be preferred over the other. The optimization approaches described here have the objective of finding the optimal values of reorder point s and maximum inventory level S so as to minimize the total cost of the inventory system while maximizing customer satisfaction. This paper describes two alternative approaches to utilizing Kriging methodology with multiple-objective optimization in simulation studies.

Bruzzone, Agostino; Fadda, Paolo; Fancello, Gianfranco; Massei, Marina; Bocca, Enrico; Tremori, Alberto; Tarone, Federico; D'Errico, Gianmarco

doi: 10.1177/0037549711418688pmid: N/A

This paper focuses on the development of a new generation of interoperable simulators of micro activities in a logistics node; the proposed example focuses on a real-time full-scope virtual simulator of port activities able to simulate the activities of the whole port by having ships, cranes, trucks and containers interoperating in a federation. The technologies adopted in terms of architecture and installation were very effective in creating a mobile laboratory open to further extension by online interoperation with other simulators and with biomedical devices for assessing human capabilities within this framework. This approach allows us to face the existing challenges in extending the capabilities of a logistics node over their current capabilities limited by technological and human factors.

Huang, Chung-Yuan; Tzou, Pen-Jung; Sun, Chuen-Tsai

doi: 10.1177/0037549710387940pmid: N/A

In a continuous opinion dynamics model using a bounded confidence assumption, individuals can only influence each other’s opinions when those opinions are sufficiently close. However, we often observe real-world cases in which opinions are very different, yet individuals feel compelled to change their ideas to conform with their peers or superiors (or in rare cases, are willing to change them voluntarily). In other words, individuals tend to consider the practical value of conformity and worry about rejection if they do not adopt the opinions of the majority. To explore the influences of private acceptance of informational social influences and public compliance with normative social influences on collective opinion and attitude dynamics, we have created a model in which attitude and opinion respectively represent an agent’s private and expressed thoughts. Results from a series of simulation experiments indicate that our simplified model is as valid as previous opinion dynamics models also based on the bounded confidence assumption, but with different dynamics and outcomes regarding group opinion and attitude. To demonstrate our proposed model’s potential value and applications, we briefly discuss two issues of import to sociologists: pluralistic ignorance formation and destruction and minority influence.

Athanaileas, Theodoros; Menychtas, Andreas; Dionysiou, Dimitra; Kyriazis, Dimosthenis; Kaklamani, Dimitra; Varvarigou, Theodora; Uzunoglu, Nikolaos; Stamatakos, Georgios

doi: 10.1177/0037549710375437pmid: N/A

In silico (on the computer) oncology is a complex and multiscale combination of sciences and technologies that focuses on the study and modelling of biological mechanisms related to the phenomenon of cancer at all levels of biocomplexity. In silico oncology simulation models may be used for evaluating and comparing different therapeutic schemes, while at the same time considering different values of critical parameters which present substantial inter-patient variability. As the number of the involved parameters characterizing both the complex tumour biosystem and possible treatment schemes increases, the resulting exponential increase in computational requirements makes the use of a grid environment for the execution of the simulations a particularly attractive solution. In this paper, a grid-enabled simulation environment for the execution of in silico oncology radiotherapy simulations on grid infrastructures is presented and implementation details are discussed. The environment provides a web portal as the end-user interface and contains advanced features that facilitate the execution of in silico oncology simulations in grid environments. Special consideration has been given during the development of the environment in order to simplify the maintenance and extension of the application, while additional services for Quality of Service provisioning have been applied. The simulation environment has been employed in order to perform several scenarios of glioblastoma multiforme radiotherapy simulations on the Enabling Grids for E-sciencE (EGEE) grid infrastructure. Indicative simulation results, as well as statistics regarding execution times on the grid, are presented.