http://hdl.handle.net/1813/11723 Sun, 26 May 2013 01:32:18 GMT 2013-05-26T01:32:18Z http://ecommons.library.cornell.edu:80/retrieve/52080/trans_systems.jpg http://hdl.handle.net/1813/11723 http://hdl.handle.net/1813/30863 Title: Forecasting adoption of ultra-low-emission vehicles using Bayes estimates of a multinomial probit model and the GHK simulator Authors: Daziano, Ricardo; Achtnicht, Martin Abstract: In this paper we use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem and provides results that are very similar to maximum simulated likelihood estimates. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then, we define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-efficient technologies. Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/1813/30863 2013-01-01T00:00:00Z http://hdl.handle.net/1813/11724 Title: NONLINEAR OPTIMIZATION FOR PROJECT SCHEDULING AND RESOURCE ALLOCATION UNDER UNCERTAINTY Authors: Mahmoudoff, Ali Abstract: Project planning and scheduling when there are both resource constraints and uncertainty in task durations is an important and complex problem. There is a long history of work on deterministic resource-constrained project scheduling problems, but efforts directed at stochastic versions of that problem are fewer and more recent. Incorporating the ability to reallocate resources among tasks to change the characteristics of their duration probability distributions adds another important dimension to the problem, and enables integration of project planning and scheduling. Among the small number of previous works on this subject, there are two very different perspectives. Golenko-Ginzburg and Gonik (1997, 1998) have created a simulation-based approach that ?operates? the project through time and attempts to optimize locally regarding decisions on starting specific tasks at specific times. Turnquist and Nozick (2004) have formulated a nonlinear optimization model to plan resource allocations and schedule decisions a priori. This has the advantage of taking a global perspective on the project in making resource allocation decisions, but it is not adaptive to the experience with earlier tasks when making later decisions in the same way that the simulation approach is. Although the solution to their model produces a ?baseline schedule? (i.e., times when tasks are planned to start), the formulation puts much greater emphasis on resource allocation decisions. The paper by Turnquist and Nozick (2004) describes the problem formulation as a nonlinear optimization. For small problem instances (up to about 30 tasks), good solutions can be found using standard nonlinear programming packages(e.g., NPSOL). However, for larger problems, the standard packages often fail to find any solution in a reasonable amount of computational time. One major contribution of this dissertation is the development of a solution method that can solve larger problem instances efficiently and reliably. In this dissertation, we recommend using the partially augmented Lagrangian (PAL) method to solve the suggested nonlinear optimization. The test problems considered here include projects with up to 90 tasks, and solutions to the 90-task problems take about 2 minutes on a desktop PC. A second contribution of this dissertation is exploration of insights that can be gained through systematic variation of the basic parameters of the model formulation on a given problem. These insights have both computational and managerial implications for practical application of the model. Tue, 01 Aug 2006 00:00:00 GMT http://hdl.handle.net/1813/11724 2006-08-01T00:00:00Z