Dynamic process simulation for the design of complex large-scale systems with respect to the performance of multiple interdependent production processes

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1999.Includes bibliographical references (p. 247-255).This research developed a methodology to assess the design of complex large-scale products with respect to the performance of their production processes. In complex large-scale projects, physical and functional relationships among the product systems and components, along with concurrency and co-location of their production processes, generate inter-system process dependencies that drive the relative production rates among the systems. The methodology links the complexity of the product to the complexity of the production process at the level of detail of the single component and task to model the impacts of inter-system process dependencies on production performance. This detailed focus makes the methodology highly responsive to changes in design and technology and able to capture primary, secondary and tertiary impacts of change on production performance. Based on the methodology, a dynamic process simulation model has been developed to systematically assess different combinations of design and technology alternatives across multiple dimensions of production performance. Performance measures include project duration, costs, resource utilization and index of workers' exposure to dangerous conditions. Simulated scenario testing based on actual data from a construction project, the renovation of Baker House (MIT building W7), demonstrates that 1) inter-system process dependencies strongly influence production performance, 2) these links build their dynamic effects on production performance at the detailed task and component level, and 3) the nature of the links and their spatial and temporal location vary as changes are introduced in the design and in the production specifications. One important consequence is that the specification and optimization of the production processes for product systems and components as separate from one another leads to solutions that may be sub-optimal for the performance of the whole project. In addition, the specification and the representation of complex production processes at the aggregate level fails to capture important impacts of design and technology changes and, thus, leads to inconsistent duration and cost estimates.by Alessandra Orsoni.Sc.D

Forces underlying patterns of technological adaptation in multiple service environments

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.Includes bibliographical references (leaf 93).by Alessandra Orsoni.M.S

Simulation for risk assessment in LPG storage and distribution plant

This paper analyses the domino effects of LPG storage vessel rupture focusing on the downstream impacts on other plant components. For the purposes of the analysis a stochastic, discrete event simulation model was developed to account both for the heat radiation caused by BLEVE (Boiling Liquid Expanding Vapor Explosion) and for the impact of vessel fragments projected in the surrounding environment. An example analysis is proposed with respect to an actual plant layout, which provides the context for scenario-based testing of the model

Integrated AI techniques for industrial risk assessment

This paper illustrates and compares different AI-based modeling approaches for systematic risk assessment in complex industrial facilities. The computer-based tools presented in the paper are specifically designed to support and expedite the implementation of design for safety practices in industrial plant component specification and layout definition. An example analysis is proposed in the paper for the purposes of illustrating the use of the tools and of assessing their performance during development and use

AI and Simulation-Based Techniques for the Assessment of Supply Chain Logistic Performanc

The effectiveness of logistic network design and management for complex and geographically distributed production systems can be measured in terms of direct logistic costs and in terms of supply chain production performance. The management of transportation logistics, for instance, involves difficult trade-offs among capacity utilization, transportation costs, and production variability often leading to the identification of multiple logistic solutions. This paper defines and compares three different modeling approaches to systematically assess each identified logistic alternative in terms of actual transportation costs and expected production losses. The first modeling approach examined in the paper is a mathematical model which provides the statistical basis for estimating costs and risks of production losses in simple application cases. The second model is a stochastic, discrete event simulation model of bulk maritime transportation specifically designed to capture the dynamic interactions between the logistic network and the production facilities. The third one is an AI-based model implemented as a modular architecture of Artificial Neural Networks (ANNs). In such an architecture each network establishes a correlation between the logistic variables relevant to a specific sub-problem and the corresponding supply chain costs. Preliminary testing of the three models shows the relative effectiveness and flexibility of the ANN-based model; it also shows that good approximation levels may be attained when either the mathematical model or the simulation model are used to generate accurate ANN training data sets for each transportation/production sub-proble