Dynamic Capacity Control in Air Cargo Revenue Management

This book studies air cargo capacity control problems. The focus is on analyzing decision models with intuitive optimal decisions as well as on developing efficient heuristics and bounds. Three different models are studied: First, a model for steering the availability of cargo space on single legs. Second, a model that simultaneously optimizes the availability of both seats and cargo capacity. Third, a decision model that controls the availability of cargo capacity on a network of flights

Dynamic Capacity Control in Air Cargo Revenue Management

This book studies air cargo capacity control problems. The focus is on analyzing decision models with intuitive optimal decisions as well as on developing efficient heuristics and bounds. Three different models are studied: First, a model for steering the availability of cargo space on single legs. Second, a model that simultaneously optimizes the availability of both seats and cargo capacity. Third, a decision model that controls the availability of cargo capacity on a network of flights

The Military Inventory Routing Problem: Utilizing Heuristics within a Least Squares Temporal Differences Algorithm to Solve a Multiclass Stochastic Inventory Routing Problem with Vehicle Loss

Military commanders currently resupply forward operating bases (FOBs) from a central location within an area of operations mainly via convoy operations in a way that closely resembles vendor managed inventory practices. Commanders must decide when and how much inventory to distribute throughout their area of operations while minimizing soldier risk. Technology currently exists that makes utilizing unmanned cargo aerial vehicles (CUAVs) for resupply an attractive alternative due to the dangers of utilizing convoy operations. Enemy actions in wartime environments pose a significant risk to a CUAV\u27s ability to safely deliver supplies to a FOB. We develop a Markov decision process (MDP) model to examine this military inventory routing problem (MILIRP). In our first paper we examine the structure of the MILIRP by considering a small problem instance and prove value function monotonicity when a sufficient penalty is applied. Moreover, we develop a monotone least squares temporal differences (MLSTD) algorithm that exploits this structure and demonstrate its efficacy for approximately solving this problem class. We compare MLSTD to least squares temporal differences (LSTD), a similar ADP algorithm that does not exploit monotonicity. MLSTD attains a 3:05% optimality gap for a baseline scenario and outperforms LSTD by 31:86% on average in our computational experiments. Our second paper expands the problem complexity with additional FOBs. We generate two new algorithms, Index and Rollout, for the routing portion and implement an LSTD algorithm that utilized these to produce solutions 22% better than myopic generated solutions on average. Our third paper greatly increases problem complexity with the addition of supply classes. We formulate an MDP model to handle the increased complexity and implement our LSTD-Index and LSTD-Rollout algorithms to solve this larger problem instance and perform 21% better on average than a myopic policy

Dynamic Capacity Control in Air Cargo Revenue Management

This work studies air cargo capacity control problems. The focus is on analyzing decision models with intuitive optimal decisions as well as on developing efficient heuristics and bounds. Three different models are studied: First, a model for steering the availability of cargo space on single legs. Second, a model that simultaneously optimizes the availability of both seats and cargo capacity. Third, a decision model that controls the availability of cargo capacity on a network of flights

Overbooking models for air cargo yield management

Master'sMASTER OF ENGINEERIN

Modeling and Analyzing the Effect of Ground Refueling Capacity on Airfield Throughput

This thesis develops five analytical models to understand the current ground refueling process, to optimize the airfield configuration and to determine the refueling policy which maximizes throughput, the primary measure of airfield efficiency. This study models the airfield refueling process as a continuous time Markov process to adequately represent the inherent stochastic nature of the transitory ground refueling system and provide an analytical evaluation of various airfield configurations. Also, the study provides an optimal refueling policy to minimize the number of aircraft on the ground which in turn minimizes the average amount of time aircraft spend on the ground in a fifth model, a Markov decision process solved by a linear program. By accomplishing this, higher throughput rates can be achieved by allowing a higher aircraft arrival rate into the airfield

A study on air cargo revenue management

Ph.DDOCTOR OF PHILOSOPH