| Environmental life-cycle analysis (LCA) provides a systematic approach for the identification, quantification, and prioritization of environmental impacts and damages of a product, process, or activity over its life cycle. And, numerous specific methods, including associated models and databases, are currently in popular use for performance of the life-cycle impact assessment (LCIA) step of LCA. Frequently, these methods are used for decision-making purposes, for example, comparing alternatives to determine which one is “best” environmentally. However, there are numerous decision-theoretic issues associated with current LCIA methods, including uncertainty in relating LCIA results to actual environmental damages that might be expected to accrue. Because of issues such as these, it has been suggested within the LCA technical community that results from multiple LCIA methods and levels of analysis might be used together to facilitate more informed decision-making (Bare, Hofstetter, Pennington, & Udo de Haes, 2000), although no formal methodology by which to do this has yet appeared in the literature. In this dissertation, we develop such a methodology based on utility theory, which we then apply to optimize the operation of a demand-responsive transit system.; When examined on a life-cycle basis, the environmental impacts of transportation are significant; and, many of the life-cycle impacts can be directly or indirectly attributed to vehicle operation. Moreover, in the case of vehicle fleet operation, many of the controllable environmental impacts are influenced by vehicle routing and scheduling decisions, in particular, in the case of a heterogeneous fleet. The routing and scheduling of demand-responsive transit vehicles is a problem in combinatorial optimization that has been studied by operations researchers over the years. However, there has been no prior work that has attempted to jointly optimize cost, service, and life-cycle environmental performance in demand-responsive routing and scheduling.; In this dissertation, we illustrate the joint optimization of cost, service, and life-cycle environmental performance in demand-responsive vehicle scheduling utilizing the decision model described above. We demonstrate, through simulation of paratransit system operation, that as a result of our methodology, it is possible to reduce environmental impacts substantially with only minimal negative impacts on cost and service performance in certain circumstances. |
