This dissertation develops mathematical models to efficiently manage the inland waterway port operations while minimizing the overall supply chain cost. In the first part, a capacitated, multi-commodity, multi-period mixed-integer linear programming model is proposed capturing diversified inland waterway transportation network related properties. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. The next study develops a two-stage stochastic mixed-integer nonlinear programming model to manage congestion in an inland waterway transportation network under stochastic commodity supply and water-level fluctuation scenarios. The model also jointly optimizes trip-wise towboat and barge assignment decisions and different supply chain decisions (e.g., inventory management, transportation decisions) in such a way that the overall system cost can be minimized. We develop a parallelized hybrid decomposition algorithm, combining Constraint Generation algorithm, Sample Average Approximation (SAA), and an enhanced variant of the L-shaped algorithm, to effectively solve our proposed optimization model in a timely fashion. While the first two parts develop models from the supply chain network design viewpoint, the next two parts propose mathematical models to emphasize the port and waterway transportation related operations. Two two-stage, stochastic, mixed-integer linear programming (MILP) models are proposed under stochastic commodity supply and water level fluctuations scenarios. The last one puts the specific focus in modeling perishable inventories. To solve the third model we propose to develop a highly customized parallelized hybrid decomposition algorithm that combines SAA with an enhanced Progressive Hedging and Nested Decomposition algorithm. Similarly, to solve the last mathematical model we propose a hybrid decomposition algorithm combining the enhanced Benders decomposition algorithm and SAA to solve the large size of test instances of this complex, NP-hard problem. Both proposed approaches are highly efficient in solving the real-life test instances of the model to desired quality within a reasonable time frame. All the four developed models are validated a real-life case study focusing on the inland waterway transportation network along the Mississippi River. A number of managerial insights are drawn for different key input parameters that impact port operations. These insights will essentially help decisions makers to effectively and efficiently manage an inland waterway-based transportation network.

Management study of inland water transport systems in the USA - includes technical aspects, mathematical models of production functions, computer simulation of equipment requirements and optimum transport flow, design of future waterways, etc. Bibliography pp. 143 and 144 and statistical tables.

Water supports our planet and its vast resources need to be fully utilized to benefit human activities and his environment in a sustainable manner, most of inland water resources has been under utilised and under maintained. Maritime industry has made use of the ocean in a more much responsible manner for cross continental transportation of good. There are currently dire needs to find sensitive ways to mitigate challenge of global warming, climate changes and its associated impact, especially within the coastline. Various research works has proven that Inland Water Transportation represents the cleanest mode of transportation. Its use could reduce and mitigate carbon footage and other Green House Gases. Past system design and operation has followed conventional method. System has been addressed through reactive behaviour that has put system on probable risk and consequence in oblivion. Likewise, complexity of sustainable water transportation development demand design and operation that require careful evaluation which can be achieved by employing proactive method. That considers holistic system analysis approach. It has become important to address system associated risk, reliability and their life cycle through assessment of accident and pollution prevention, protection, control principle. Ageing, uncertainty and operational factors are also important system variables that need to be incorporated in risk close loop system. This book account for modelling of proactive technik and application of a top down risk and reliability based design that identifies assess, analyses and employ sustainability equity comparison leading to generic safety and environmental risk reliability model (SERM). SERM is a decision support system tool developed at University Technology Malaysia for the development of efficient and sustainable Inland Water Transportation System (IWT).

We address the problem of designing a transportation network in the presence of demand uncertainty, multiple origin-destination pairs and a budget constraint for the overall construction cost, under the behavioral assumption that travelers optimize their own travel costs (i.e., the "user-equilibrium" condition). Under deterministic demand, we propose an exact integer optimization approach that leads to a quadratic objective, linear constraints optimization problem. As a result, the problem is efficiently solvable via commercial software, when the costs are linear functions of traffic flows. We then use an iterative algorithm to address the case of nonlinear cost functions. While the problem is intractable under probabilistic assumptions on demand uncertainty, we extend the previous model and propose an iterative algorithm using a robust optimization approach that models demand uncertainty. We finally report extensive numerical results to illustrate that our approach leads to tractable solutions for large scale networks.

The inland waterway transportation system of the United States (U.S.) handles 11.7 billion tons of freight annually and connects the heartland of the U.S. with the rest of the world by providing a fuel-efficient and environmentally friendly mode of transportation. This dissertation aims to create decision support tools for maritime stakeholders to measure the economic impacts of the inland waterway transportation systems under real world scenarios including disruptions, demand changes, port expansion decisions, and channel deepening investments. Monte Carlo simulation, system dynamics, discrete-event simulation, agent-based modeling, and multiregional input-output modeling techniques are utilized to analyze the complex relationships between inland waterway transportation system components and regional economic impact factors. The first research contribution illustrates that the expected duration of a disruption determines whether decision makers are better off waiting for the waterway system to reopen or switching to an alternative mode of transportation. Moreover, total disruption cost can be reduced by increasing estimation accuracy of disruption duration. The second research contribution shows that without future investment in inland waterway infrastructure, a sustainable system and associate economic impacts cannot be generated in the long-term. The third research contribution illustrates that investing in bottleneck system components results in higher economic impact than investing in non-bottleneck components. The developed models can be adapted to any inland waterway transportation system in the U.S. by utilizing data obtained by publically available sources to measure the economic impacts under various scenarios to inform capital investment decisions and support an economically sustainable inland waterway transportation system.

Motivated by the critical role of the inland waterways in the United States' transportation system, this dissertation research focuses on pre- and post- disruption response support when the inland waterway navigation system is disrupted by a natural or manmade event. Following a comprehensive literature review, four research contributions are achieved. The first research contribution formulates and solves a cargo prioritization and terminal allocation problem (CPTAP) that minimizes total value loss of the disrupted barge cargoes on the inland waterway transportation system. It is tailored for maritime transportation stakeholders whose disaster response plans seek to mitigate negative economic and societal impacts. A genetic algorithm (GA)-based heuristic is developed and tested to solve realistically-sized instances of CPTAP. The second research contribution develops and examines a tabu search (TS) heuristic as an improved solution approach to CPTAP. Different from GA's population search approach, the TS heuristic uses the local search to find improved solutions to CPTAP in less computation time. The third research contribution assesses cargo value decreasing rates (CVDRs) through a Value-focused Thinking based methodology. The CVDR is a vital parameter to the general cargo prioritization modeling as well as specifically for the CPTAP model for inland waterways developed here. The fourth research contribution develops a multi-attribute decision model based on the Analytic Hierarchy Process that integrates tangible and intangible factors in prioritizing cargo after an inland waterway disruption. This contribution allows for consideration of subjective, qualitative attributes in addition to the pure quantitative CPTAP approach explored in the first two research contributions.