Waterway simulation models can be used to evaluate system performance and optimize investment and operation decisions, provided the effects of such decisions on demand are properly considered. Some simulation models have considered demand changes due to seasonal variations, economic growth, congestion effects on travel times, and user responses to service interruptions—but not all of these factors together. An elastic demand relationship embedded in a simulation model is used to estimate the effects of changing service times and lock closures during the simulation. Because traffic demand and benefits may be significantly affected by the simulated decisions, evaluation or optimization of the system merely on the basis of total costs is unreasonable. Estimation of the benefits to waterway users also is demonstrated during simulation runs while accounting for the users' responses to lock closures and other factors that affect travel times. Specifically, the present worth of net benefits for the entire analysis period can be computed and used as the objective function for optimization. This approach can account for infrastructure construction and maintenance costs, the effects of service quality and interruptions on user costs, and possible mode shifts in response to waterway service quality.
A model is developed for selecting and scheduling interrelated waterway projects. It considers multiple alternatives, which may be implemented at different times, for each lock site, subject to budget constraints. This large combinatorial problem is analyzed with a simulation-based optimization model, in which simulation is used to evaluate alternative project schedules. A genetic algorithm is developed to solve the problem efficiently, subject to constraints on project multiplicity and precedence.
A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).
Scheduled preventive maintenance is required to preserve waterway locks and ensure satisfactory service in waterway operations. Because delays to waterway users usually increase during periods of lock maintenance, shippers may change departure times or switch to a different transportation mode. The analysis of variations in waterway traffic due to lock maintenance is a challenging task. This paper analyzes the cost structures of waterway and rail transportation and develops three waterway demand models on the basis of different user behavior assumptions. Numerical testing is conducted to verify the models. The results of the analyses show that these models, grounded in economics, traffic flow theory, and queuing models, can capture the effects of demand changes over time.
This paper presents an improved scheduling model for waterway projects that can consider several complicating factors: (a) multiple project alternatives at each location, of which only one may be selected per location; (b) multiple budget sources or regional funding constraints; and (c) constrained precedence relationships between projects. To solve the problem, a simulation-based optimization model that uses simulation to evaluate alternative project schedules is developed. A genetic algorithm is also developed to solve this large investment optimization problem efficiently by means of some prescreening rules that reduce the number of simulated alternatives. The mutually exclusive alternatives at each location allow joint optimization of the sizing and timing of improvements. The multiple budget constraints realistically reflect actual funding practices, but considerably complicate the problem because project sequencing no longer uniquely determines the schedules and projects that may now be funded concurrently. The numerical example shows how the additional factors considered here can be properly incorporated into the analysis and how the quality and reliability of results from such a relatively complex model can be verified.
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