Crowd modeling and simulation technologies

As a collective and highly dynamic social group, the human crowd is a fascinating phenomenon that has been frequently studied by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This article assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the time-scale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user. We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in this work. We have also discussed some open problems in the area. This article will provide the researchers with useful information and insights on the state of the art of the technologies in crowd modeling and simulation as well as future research directions.</jats:p

Biologically inspired algorithims for job shop scheduling optimization

This project aims to explore and develop new biologically inspired algorithms for optimizing job shop scheduling problems. This project was motivated by the work carried out by Nakrani and Tovey (2004), on using a honey bee algorithm for dynamic allocation of Internet servers. In their algorithm, servers and HTTP request queues in an Internet server colony are modelled as foraging bees and flower patches respectively. In this project, we have successfully developed several bee colony optimization algorithms for the job shop scheduling, and have also extended the algorithms to other problem domains such as the travelling salesman problems as well as multi-objective simulation-based optimization for defence decision making process. The results of the finding from the project have been published in international journal and conferences. The work from the project has also led to the establishment of other research collaboration projects with external institutions in the domain of manufacturing, maritime as well as defence.SUG 10/0

Adaptive techniques for BSP Time Warp

Parallel simulation is a well developed technique for executing large and complex simulation models in order to obtain simulation output for analysis within an acceptable time frame. The main contribution of this thesis is the development of different adaptive techniques to improve the consistency, performance and resilience of the BSP Time Warp as a general purpose parallel simulation protocol. We first study the problem of risk hazards in the BSP Time Warp optimistic simulation protocols. Successive refinements to the BSP Time Warp protocol are carried out to eliminate errors in simulation execution due to different risk hazards. We show that these refinements can be incorporated into the BSP Time Warp protocol with minimal performance degradation. We next propose an adaptive scheme for the BSP Time Warp algorithm that automatically throttles the number of events to be executed per superstep. We show that the scheme, operating in a shared memory environment, can minimize computation load-imbalance and rollback overhead at the expense of incurring higher synchronization cost. The next contribution of this thesis is the study of different techniques for dynamic load-balancing and process migration for Time Warp on a cluster of workstations. We propose different dynamic load-balancing algorithms for BSP Time Warp that seek to balance both computation workload and communication workload, optimizing lookaheads between processors, as well as manage interruption from external workload. Finally, we propose an adaptive technique for BSP Time Warp that automatically varies the number of processors used for parallel computation based on the characteristics of the underlying parallel computing platform and the simulation workload.</p