Barge Prioritization, Assignment, and Scheduling During Inland Waterway Disruption Responses

Inland waterways face natural and man-made disruptions that may affect navigation and infrastructure operations leading to barge traffic disruptions and economic losses. This dissertation investigates inland waterway disruption responses to intelligently redirect disrupted barges to inland terminals and prioritize offloading while minimizing total cargo value loss. This problem is known in the literature as the cargo prioritization and terminal allocation problem (CPTAP). A previous study formulated the CPTAP as a non-linear integer programming (NLIP) model solved with a genetic algorithm (GA) approach. This dissertation contributes three new and improved approaches to solve the CPTAP. The first approach is a decomposition based sequential heuristic (DBSH) that reduces the time to obtain a response solution by decomposing the CPTAP into separate cargo prioritization, assignment, and scheduling subproblems. The DBSH integrates the Analytic Hierarchy Process and linear programming to prioritize cargo and allocate barges to terminals. Our findings show that compared to the GA approach, the DBSH is more suited to solve large sized decision problems resulting in similar or reduced cargo value loss and drastically improved computational time. The second approach formulates CPTAP as a mixed integer linear programming (MILP) model improved through the addition of valid inequalities (MILP\u27). Due to the complexity of the NLIP, the GA results were validated only for small size instances. This dissertation fills this gap by using the lower bounds of the MILP\u27 model to validate the quality of all prior GA solutions. In addition, a comparison of the MILP\u27 and GA solutions for several real world scenarios show that the MILP\u27 formulation outperforms the NLIP model solved with the GA approach by reducing the total cargo value loss objective. The third approach reformulates the MILP model via Dantzig-Wolfe decomposition and develops an exact method based on branch-and-price technique to solve the model. Previous approaches obtained optimal solutions for instances of the CPTAP that consist of up to five terminals and nine barges. The main contribution of this new approach is the ability to obtain optimal solutions of larger CPTAP instances involving up to ten terminals and thirty barges in reasonable computational time

Planning and Scheduling Interrelated Road Network Projects by Integrating Cell Transmission Model and Genetic Algorithm

In systems with interrelated alternatives, the benefits or costs of each alternative depend on which other alternatives are selected and when they are implemented. System interrelations and uncertainties in various elements of transportation systems such as future demand, make it difficult to evaluate project impacts with analytical methods. This study proposes a general and modular framework for planning and scheduling interrelated infrastructure projects under uncertainties. The method should be general enough to address the planning problem for any interrelated system in a wide range of applications. The goal is to determine which projects should be selected and when they should be implemented to minimize the present value of total system cost, subject to a cumulative budget flow constraint. For this purpose, the scheduling problem is formulated as a non-linear integer optimization problem that minimizes the present value of system cost over a planning horizon. The first part of this dissertation employs a simple traffic assignment model to evaluate improvement alternatives. The algorithm identifies potential locations within a network that needs improvements and considers multiple improvement alternatives at each location. Accordingly, a probabilistic procedure is introduced to select the optimal improvement type for the candidate locations. The traffic assignment model is used to evaluate the objective function and implicitly compute project interrelations, with a Genetic Algorithm (GA) developed to solve the optimization problem. In the second part of the dissertation, the traffic assignment model is replaced with a more detailed evaluation model, namely a Cell Transmission Model (CTM). The use of CTM significantly improves the model by tracking queues and predicating queue build-up and dissipation, as well as backward propagation of congestion waves. Finally, since GA does not guarantee global optimum, a statistical test is employed to test the optimality of the GA solution by estimating the probability of arriving at a better solution. In effect, it is shown that the probability of finding a better solution is negligible, thus demonstrating the soundness of the GA solution

PRIORITIZING AND SCHEDULING INTERRELATED ROAD PROJECTS USING METAHEURISTIC ALGORITHMS

Projects are considered interrelated when their benefits or costs depend on which other projects are implemented. Selection and scheduling of interrelated projects is a challenging optimization problem which has applications in various fields including economics, operations research, business, management and transportation. The goal is to determine which projects should be selected and when they should be funded in order to minimize the total system cost over a planning horizon subject to a budget constraint. The budget is supplied by both external and internal sources from fuel tax revenues. This study then applies three meta-heuristic algorithms including a Genetic Algorithm (GA), Simulated Annealing (SA) and, Tabu Search (TS) in seeking efficient and consistent solutions to the selection and scheduling problem. These approaches are applied to a special case of link capacity expansion projects to showcase their functionality and compare their performance in terms of solution quality, computation time and consistency

Analysis of Interrelated Network Improvement Alternatives

69A43551747123This project developed methods for optimizing the long-term development of road networks by developing algorithms for selecting, sequencing and scheduling interrelated improvements, which change flows through the networks. It also compared how network performance can be evaluated as a network\u2019s configuration evolves, using either a fast traffic assignment algorithm or the slower but more realistically precise microscopic simulation model INTEGRATION. The results indicate when and to what extent the traffic assignment algorithm can approximate the simulation results. They demonstrate the potential value of hybrid methods in combining initial search with traffic assignment and refined search with microscopic simulation

Waterway System Maintenance Optimization

Coastal lines, harbors/ports, and inland waterways constitute the marine transportation system, a major component of the United States freight system, carrying a vast majority of foreign imports and exports and a significant amount of domestic freight. This system needs regular maintenance. US Army Corps of Engineers (USACE) is in charge of the waterway system maintenance. However, the limited maintenance budget needs to accommodate a large number of maintenance requests for dredging and dam repair, etc. The requests often exceed the budget available by much. A decision facing the USACE management is what projects to fund and how to select them. This research aims at providing the necessary models and tools to facilitate maintenance decisions at the USACE. The objective is to maximize the overall system improvement under annual limited budget. The underlying problem can be modeled as a knapsack problem with an additional constraint that increases the problem complexity. The additional constraints describe the benefit interdependency of different maintenance projects due to the waterways network effect. This research tackles the maintenance problem at different levels. First, an integer selection model is developed to find the optimal set of dredging projects (waterway sediment removal operation) and some heuristics are developed to provide near-optimal solutions in computationally guaranteed polynomial time. Next, a model is developed to allow partial dredging. Partial dredging means partially conducting the requested dredging operation. The model is able to determine the percentage of the dredging depth to fund instead of a zero-one dredging decision for each project. Further, a stochastic problem is considered regarding to the probabilistic shoaling process. To solve the probabilistic problem, two methods are designed: an analytical model that takes account of probability in terms of expected values, and a stochastic optimization approach was developed based on Monte-Carlo simulation. Finally, the problem is modeled in a multi-modal context where the maintenance decisions are made simultaneously on dredging and lock/dam improvement. In this multimodal model, the effect of landside modes’ capacity is considered comprehensively. All the developed methods are tested with real examples from US marine network and their performance is approved by comparison to real situation

A novel learning automata game with local feedback for parallel optimization of hydropower production

Master's thesis Information- and communication technology IKT590 - University of Agder 2017Hydropower optimization for multi-reservoir systems is classi ed as a combinatorial optimization problem with large state-space that is particularly di cult to solve. There exist no golden standard when solving such problems, and many proposed algorithms are domain speci c. The literature describes several di erent techniques where linear programming approaches are extensively discussed, but tends to succumb to the curse of dimensionality problem when the state vector dimensions increase. This thesis introduces LA LCS, a novel learning automata algorithm that utilizes a parallel form of local feedback. This enables each individual automaton to receive direct feedback, resulting in faster convergence. In addition, the algorithm is implemented using a parallel architecture on a CUDA enabled GPU, along with exhaustive and random search. LA LCS has been veri ed through several scenarios. Experiments show that the algorithm is able to quickly adapt and nd optimal production strategies for problems of variable complexity. The algorithm is empirically veri ed and shown to hold great promise for solving optimization problems, including hydropower production strategies

The Evolution of Transport Networks

Between 1900 and 2000, the length of paved roads in the United States increased from 240 km to 6,400,000 km (Peat 2002, BTS 2002) with virtually 100% of the U.S. population having almost immediate access to paved roadways. Similarly, in 1830 there were 37 km of railroad in the United States, but by 1920 total track mileage had increased more than ten-thousand times to 416,000 km miles, however since then, rail track mileage has shrunk to about 272,000 km (Garrison 1996, BTS 2002). The growth (and decline) of transport networks obviously affects the social and economic activities that a region can support; yet the dynamics of how such growth occurs is one of the least understood areas in transport, geography, and regional science. This is revealed time and again in the long-range planning efforts of metropolitan planning organizations (MPOs), where transport network changes are treated exclusively as the result of top-down decision-making. Changes to the transport network are rather the result of numerous small decisions (and some large ones) by property owners, firms, developers, towns, cities, counties, state department of transport districts, MPOs, and states in response to market conditions and policy initiatives. Understanding how markets and policies translate into facilities on the ground is essential for scientific understanding and improving forecasting, planning, policy-making, and evaluation.Transportation Network Growth, Transportation-Land Use Interaction, Markov Chain

SIMULATION-BASED OPTIMIZATION OF TRANSPORTATION SYSTEMS: THEORY, SURROGATE MODELS, AND APPLICATIONS

The construction of new highway infrastructure has not kept pace with the growth of travel, mainly due to the limitation of land and funding availability. To improve the mobility, safety, reliability and sustainability of the transportation system, various transportation planning and traffic operations policies have been developed in the past few decades. On the other hand, simulation is widely used to evaluate the impacts of those policies, due to its advantages in capturing network and behavior details and capability of analyzing various combinations of policies. A simulation-based optimization (SBO) method, which combines the strength of simulation evaluation and mathematical optimization, is imperative for supporting decision making in practice. The objective of this dissertation is to develop SBO methods that can be efficiently applied to transportation planning and operations problems. Surrogate-based methods are selected as the research focus after reviewing various existing SBO methods. A systematic framework for applying the surrogate-based optimization methods in transportation research is then developed. The performance of different forms of surrogate models is compared through a numerical example, and regressing Kriging is identified as the best model in approximating the unknown response surface when no information regarding the simulation noise is available. Accompanied with an expected improvement global infill strategy, regressing Kriging is successfully applied in a real world application of optimizing the dynamic pricing for a toll road in the Inter-County Connector (ICC) regional network in the State of Maryland. To further explore its capability in dealing with problems that are of more interest to planners and operators of the transportation system, this method is then extended to solve constrained and multi-objective optimization problems. Due to the observation of heteroscedasticity in transportation simulation outputs, two surrogate models that can be adapted for heteroscedastic data are developed: a heteroscedastic support vector regression (SVR) model and a Bayesian stochastic Kriging model. These two models deal with the heteroscedasticity in simulation noise in different ways, and their superiority in approximating the response surface of simulations with heteroscedastic noise over regressing Kriging is verified through both numerical studies and real world applications. Furthermore, a distribution-based SVR model which takes into account the statistical distribution of simulation noise is developed. By utilizing the bootstrapping method, a global search scheme can be incorporated into this model. The value of taking into account the statistical distribution of simulation noise in improving the convergence rate for optimization is then verified through numerical examples and a real world application of integrated corridor traffic management. This research is one of the first to introduce simulation-based optimization methods into large-scale transportation network research. Various types of practical problems (with single-objective, with multi-objective or with complex constraints) can be resolved. Meanwhile, the developed optimization methods are general and can be applied to analyze all types of policies using any simulator. Methodological improvements to the surrogate models are made to take into account the statistical characteristics of simulation noise. These improvements are shown to enhance the prediction accuracy of the surrogate models, and further enhance the efficiency of optimization. Generally, compared to traditional surrogate models, fewer simulation evaluations would be needed to find the optimal solution when these improved models are applied