An Automation System for Optimizing a Supply Chain Network Design under the Influence of Demand Uncertainty

This research develops and applies an integrated hierarchical framework for modeling a multi-echelon supply chain network design, under the influence of demand uncertainty. The framework is a layered integration of two levels: macro, high-level scenario planning combined with micro, low-level Monte Carlo simulation of uncertainties in demand. To facilitate rapid simulation of the effects of demand uncertainty, the integrated framework was implemented as a dashboard automation system using Microsoft Excel, Risk Solver, and Visual Basic. The integrated framework has been applied to the problem of quantifying the effects of demand uncertainty on total cost in multi-echelon supply chain network design for high-tech products

An Automation System for Optimizing a Supply Chain Network Design under the Influence of Demand Uncertainty

This research develops and applies an integrated hierarchical framework for modeling a multi-echelon supply chain network design, under the influence of demand uncertainty. The framework is a layered integration of two levels: macro, high-level scenario planning combined with micro, low-level Monte Carlo simulation of uncertainties in demand. To facilitate rapid simulation of the effects of demand uncertainty, the integrated framework was implemented as a dashboard automation system using Microsoft Excel, Risk Solver, and Visual Basic. The integrated framework has been applied to the problem of quantifying the effects of demand uncertainty on total cost in multi-echelon supply chain network design for high-tech products

Multidisciplinary system design optimization of fiber-optic networks within data centers

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 136-142).The growth of the Internet and the vast amount of cloud-based data have created a need to develop data centers that can respond to market dynamics. The role of a data center designer, whom is responsible for scoping, building, and managing the infrastructure design is becoming increasingly complex. This work presents a new analytical systems approach to modeling fiber-optic network design within data centers. Multidisciplinary system design optimization (MSDO) is utilized to integrate seven disciplines into a unified software framework for modeling 10G, 40G, and 100G multi-mode fiber-optics networks: 1) market and industry analysis, 2) fiber-optic technology, 3) data center infrastructure, 4) systems analysis, 5) multi-objective optimization using genetic algorithms, 6) parallel computing, and 7) simulation research using MATLAB and OptiSystem. The framework is applied to four theoretical data center case studies to simultaneously evaluate the Pareto optimal trade-offs of (a) minimizing life-cycle costs, (b) maximizing user capacity, and (c) maximizing optical transmission quality (Q-factor). The results demonstrate that data center life-cycle costs are most sensitive to power costs, 10G OM4 multi-mode optical fiber is Pareto optimal for long reach and low user capacity needs, and 100G OM4 multi-mode optical fiber is Pareto optimal for short reach and high user capacity needs.by Rany Polany.S.M. in Engineering and Managemen

Asteroid Deflection Campaign Design Integrating Epistemic Uncertainties

Asteroid deflection entails multiple sources of epistemic uncertainties and stochastic uncertainties. Epistemic uncertainties can be reduced by replenishing incomplete information with a better means of observation, whereas stochastic uncertainties are inherent and irreducible owing to their randomness. Our understanding of asteroid deflection is largely limited by lack of in-situ data or full-scale experiments. Reducing these epistemic uncertainties in (1) the physical properties of asteroids or (2) the consequences of human interference will drastically benefit our mitigation efforts against their hazards. Much of previous literature concentrated on single-stage missions or simple asteroid models with fixed physical attributes, making it inadequate to handle epistemic or stochastic uncertainties. To fill this gap, this paper tries to incorporate different kinds of uncertainties within a campaign design framework. In a multi-stage campaign, a precursor is deployed first to reduce epistemic uncertainties. Because stochastic uncertainties cannot be reduced by nature, the campaign should be optimized to maximize its robustness against the worst random situations. Finally, whether or not a two-stage campaign is better than a single-stage mission can be visualized as a decision map for decision-makers. The paper analyzes a hypothetical scenario of deflecting 99942 Apophis and discusses future work