Matching Items (4)
Filtering by
- All Subjects: Optimization
- Creators: Sefair, Jorge
Description
Project portfolio selection (PPS) is a significant problem faced by most organizations. How to best select the many innovative ideas that a company has developed to deploy in a proper and sustained manner with a balanced allocation of its resources over multiple time periods is one of vital importance to a company's goals. This dissertation details the steps involved in deploying a more intuitive portfolio selection framework that facilitates bringing analysts and management to a consensus on ongoing company efforts and buy into final decisions. A binary integer programming selection model that constructs an efficient frontier allows the evaluation of portfolios on many different criteria and allows decision makers (DM) to bring their experience and insight to the table when making a decision is discussed. A binary fractional integer program provides additional choices by optimizing portfolios on cost-benefit ratios over multiple time periods is also presented. By combining this framework with an `elimination by aspects' model of decision making, DMs evaluate portfolios on various objectives and ensure the selection of a portfolio most in line with their goals. By presenting a modeling framework to easily model a large number of project inter-dependencies and an evolutionary algorithm that is intelligently guided in the search for attractive portfolios by a beam search heuristic, practitioners are given a ready recipe to solve big problem instances to generate attractive project portfolios for their organizations. Finally, this dissertation attempts to address the problem of risk and uncertainty in project portfolio selection. After exploring the selection of portfolios based on trade-offs between a primary benefit and a primary cost, the third important dimension of uncertainty of outcome and the risk a decision maker is willing to take on in their quest to select the best portfolio for their organization is examined.
ContributorsSampath, Siddhartha (Author) / Gel, Esma (Thesis advisor) / Fowler, Jown W (Thesis advisor) / Kempf, Karl G. (Committee member) / Pan, Rong (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2018
Description
Every year, millions of guests visit theme parks internationally. Within that massive population, accidents and emergencies are bound to occur. Choosing the correct location for emergency responders inside of the park could mean the difference between life and death. In an effort to provide the utmost safety for the guests of a park, it is important to make the best decision when selecting the location for emergency response crews. A theme park is different from a regular residential or commercial area because the crowds and shows block certain routes, and they change throughout the day. We propose an optimization model that selects staging locations for emergency medical responders in a theme park to maximize the number of responses that can occur within a pre-specified time. The staging areas are selected from a candidate set of restricted access locations where the responders can store their equipment. Our solution approach considers all routes to access any park location, including areas that are unavailable to a regular guest. Theme parks are a highly dynamic environment. Because special events occurring in the park at certain hours (e.g., parades) might impact the responders' travel times, our model's decisions also include the time dimension in the location and re-location of the responders. Our solution provides the optimal location of the responders for each time partition, including backup responders. When an optimal solution is found, the model is also designed to consider alternate optimal solutions that provide a more balanced workload for the crews.
ContributorsLivingston, Noah Russell (Author) / Sefair, Jorge (Thesis director) / Askin, Ronald (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Global optimization (programming) has been attracting the attention of researchers for almost a century. Since linear programming (LP) and mixed integer linear programming (MILP) had been well studied in early stages, MILP methods and software tools had improved in their efficiency in the past few years. They are now fast and robust even for problems with millions of variables. Therefore, it is desirable to use MILP software to solve mixed integer nonlinear programming (MINLP) problems. For an MINLP problem to be solved by an MILP solver, its nonlinear functions must be transformed to linear ones. The most common method to do the transformation is the piecewise linear approximation (PLA). This dissertation will summarize the types of optimization and the most important tools and methods, and will discuss in depth the PLA tool. PLA will be done using nonuniform partitioning of the domain of the variables involved in the function that will be approximated. Also partial PLA models that approximate only parts of a complicated optimization problem will be introduced. Computational experiments will be done and the results will show that nonuniform partitioning and partial PLA can be beneficial.
ContributorsAlkhalifa, Loay (Author) / Mittelmann, Hans (Thesis advisor) / Armbruster, Hans (Committee member) / Escobedo, Adolfo (Committee member) / Renaut, Rosemary (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2020
Description
Breeding seeds to include desirable traits (increased yield, drought/temperature resistance, etc.) is a growing and important method of establishing food security. However, besides breeder intuition, few decision-making tools exist that can provide the breeders with credible evidence to make decisions on which seeds to progress to further stages of development. This thesis attempts to create a chance-constrained knapsack optimization model, which the breeder can use to make better decisions about seed progression and help reduce the levels of risk in their selections. The model’s objective is to select seed varieties out of a larger pool of varieties and maximize the average yield of the “knapsack” based on meeting some risk criteria. Two models are created for different cases. First is the risk reduction model which seeks to reduce the risk of getting a bad yield but still maximize the total yield. The second model considers the possibility of adverse environmental effects and seeks to mitigate the negative effects it could have on the total yield. In practice, breeders can use these models to better quantify uncertainty in selecting seed varieties
ContributorsOzcan, Ozkan Meric (Author) / Armbruster, Dieter (Thesis advisor) / Gel, Esma (Thesis advisor) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2019