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 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

Revenue management models in the manufacturing industry

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (p. 107-110).In recent years, many manufacturing companies have started exploring innovative revenue management technologies in an effort to improve their operations and ultimately their bottom lines. Methods such as differentiating customers based on their sensitivity to price and delays are employed by firms to increase their profits. These developments call for models that have the potential to radically improve supply chain efficiencies in much the same way that revenue management has changed the airline industry. In this dissertation, we study revenue management models where customers can be separated into different classes depending on their sensitivity to price, lead time, and service. Specifically, we focus on identifying effective models to coordinate production, inventory and admission controls in face of multiple classes of demand and time- varying parameters. We start with a single-class-customer problem with both backlogged and discretionary sales. Demand may be fulfilled no later than N periods with price discounts if the inventory is not available. If profitable, demand may be rejected even if the inventory is still available.(cont.) For this problem we analyze the structure of the optimal policy and show that it is characterized by three state-independent control parameters: the produce-up-to level (S), the reserve-up-to level (R), and the backlog-up-to level (B). At the beginning of each period, the manufacturer will produce to bring the inventory level up to S or to the maximum capacity; during the period, s/he will set aside R units of inventory for the next period, and satisfy some customers with the remaining inventory, if expected future profit is higher; otherwise, s/he will satisfy customers with the inventory and backlog up to B units of demands. Then, we analyze a single-product, two-class-customer model in which demanding (high priority) customers would like to receive products immediately, while other customers are willing to wait in order to pay lower prices. For this model, we provide a heuristic policy characterized by three threshold levels: S, R, B.(cont.) In this policy, during each period, the manufacturer will set aside R units of inventory for the next period, and satisfy some high priority customers with the remaining inventory, if expected future profit is higher; otherwise, s/he will satisfy as many of the high priority customers as possible and backlog up to B units of lower priority customers. Finally, we examine production, rationing, and admission control policies in manufacturing systems with both make-to-stock(MTS) and make-to-order(MTO) products. Two models are analyzed. In the first model, which is motivated by the automobile industry, the make-to-stock product has higher priority than the make-to-order product. In the second model, which is motivated by the PC industry, the manufacturer gives higher priority to the make-to-order product over the make-to-stock product. We characterize the optimal production and order admission policies with linear threshold levels. We also extend those results to problems where low-priority backorders can be canceled by the manufacturer, as well as to problems with multiple types of make-to-order products.by Tieming Liu.Ph.D

Data-driven Decisions in Service Systems

This thesis makes contributions to help provide data-driven (or evidence-based) decision support to service systems, especially hospitals. Three selected topics are presented. First, we discuss how Little's Law, which relates average limits and expected values of stationary distributions, can be applied to service systems data that are collected over a finite time interval. To make inferences based on the indirect estimator of average waiting times, we propose methods for estimating confidence intervals and for adjusting estimates to reduce bias. We show our new methods are effective using simulations and data from a US bank call center. Second, we address important issues that need to be taken into account when testing whether real arrival data can be modeled by nonhomogeneous Poisson processes (NHPPs). We apply our method to data from a US bank call center and a hospital emergency department and demonstrate that their arrivals come from NHPPs. Lastly, we discuss an approach to standardize the Intensive Care Unit admission process, which currently lacks a well-defined criteria. Using data from nearly 200,000 hospitalizations, we discuss how we can quantify the impact of Intensive Care Unit admission on individual patient's clinical outcomes. We then use this quantified impact and a stylized model to discuss optimal admission policies. We use simulation to compare the performance of our proposed optimal policies to the current admission policy, and show that the gain can be significant

Optimal control of queueing systems with multiple heterogeneous facilities

This thesis discusses queueing systems in which decisions are made when customers arrive, either by individual customers themselves or by a central controller. Decisions are made concerning whether or not customers should be admitted to the system (admission control) and, if they are to be admitted, where they should go to receive service (routing control). An important objective is to compare the effects of "selfish" decision-making, in which customers make decisions aimed solely at optimising their own outcomes, with those of "socially optimal" control policies, which optimise the economic performance of the system as a whole. The problems considered are intended to be quite general in nature, and the resulting findings are therefore broad in scope. Initially, M/M/1 queueing systems are considered, and the results presented establish novel connections between two distinct areas of the literature. Subsequently, a more complicated problem is considered, involving routing control in a system which consists of heterogeneous, multiple-server facilities arranged in parallel. It is shown that the multiple-facility system can be formulated mathematically as a Markov Decision Process (MDP), and this enables a fundamental relationship to be proved between individually optimal and socially optimal policies which is of great theoretical and practical importance. Structural properties of socially optimal policies are analysed rigorously, and it is found that 'simple' characterisations of socially optimal policies are usually unattainable in systems with heterogeneous facilities. Finally, the feasibility of finding 'near-optimal' policies for large scale systems by using heuristics and simulation-based methods is considered