A Markov chain location-allocation meta-model for hurricane relief

An emergency is a deviation from a planned course of events that endangers people, properties, or the environment. It can be described as an unexpected event that causes economic damage, destruction, and human suffering. When a disaster happens, Emergency Managers are expected to have a response plan to most likely disaster scenarios. Unlike earthquakes and terrorist attacks, a hurricane response plan can be activated ahead of time, since a hurricane is predicted at least five days before it makes landfall. This research looked into the logistics aspects of the problem, in an attempt to develop a hurricane relief distribution network model. We addressed the problem of how to efficiently and effectively deliver basic relief goods to victims of a hurricane disaster. Specifically, where to preposition State Staging Areas (SSA), which Points of Distributions (PODs) to activate, and the allocation of commodities to each POD. Previous research has addressed several of these issues, but not with the incorporation of the random behavior of the hurricane's intensity and path. This research presents a stochastic meta-model that deals with the location of SSAs and the allocation of commodities. The novelty of the model is that it treats the strength and path of the hurricane as stochastic processes, and models them as Discrete Markov Chains. The demand is also treated as stochastic parameter because it depends on the stochastic behavior of the hurricane. However, for the meta-model, the demand is an input that is determined using Hazards United States (HAZUS), a software developed by the Federal Emergency Management Agency (FEMA) that estimates losses due to hurricanes and floods. A solution heuristic has been developed based on simulated annealing. Since the meta-model is a multi-objective problem, the heuristic is a multi-objective simulated annealing (MOSA), in which the initial solution and the cooling rate were determined via a Design of Experiments. The experiment showed that the initial temperature (T0) is irrelevant, but temperature reduction (δ) must be very gradual. Assessment of the meta-model indicates that the Markov Chains performed as well or better than forecasts made by the National Hurricane Center (NHC). Tests of the MOSA showed that it provides solutions in an efficient manner. Thus, an illustrative example shows that the meta-model is practical.

A methodological framework for modeling pavement maintenance costs for projects with performance-based contracts

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

A Methodological Framework for Modeling Pavement Maintenance Costs for Projects with Performance-based Contracts

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

Fair price and its effect on willingness to purchase

The purpose of the present research is to demonstrate the influence of a fair price (independent of the subjective evaluation of the price magnitude) on buyers' willingness to purchase. The perceived fairness of a price is conceived to have three components: perceived equity, perceived need, and inferred compliance of the seller to the process rules of pricing. These components reflect the Theories of Distributive Justice (as adjusted for conditions of need) and Procedural Justice.^ The effect of the three components of a fair price on willingness to purchase is depicted in a theoretically causal chain model. Based on the Theories of Dissonance and Attribution, conditions of inequity and need activate concerns for Procedural Justice. Under conditions of inequity and need, buyers tend to infer that the seller has not complied with the generally accepted pricing practices, thus violating the social norms of Procedural justice. Inferred violations of Procedural Justice influence the buyer's attitude toward the seller. As predicted by the Theory of Reasoned Action, attitude is then positively related to willingness to purchase.^ The model was tested with a survey-based experiment conducted with 408 respondents. Two levels of both equity and need were manipulated with scenarios, a common research method in studies of Distributive and Procedural Justice. The data were analyzed with a structural equation model using LISREL. Although the effect of the "need" manipulation was insignificant, the results indicated a good fit of the model (Chi-square = 281, Degrees of Freedom = 104, Goodness of Fit Index =.924). The conclusion is that the fairness of a price does have a significant effect on willingness to purchase, independent of the subjective evaluation of the objective price. ^

Essays on Emissions Trading Markets

This dissertation is a collection of three economics essays on different aspects of carbon emission trading markets. The first essay analyzes the dynamic optimal emission control strategies of two nations. With a potential to become the largest buyer under the Kyoto Protocol, the US is assumed to be a monopsony, whereas with a large number of tradable permits on hand Russia is assumed to be a monopoly. Optimal costs of emission control programs are estimated for both the countries under four different market scenarios: non-cooperative no trade, US monopsony, Russia monopoly, and cooperative trading. The US monopsony scenario is found to be the most Pareto cost efficient. The Pareto efficient outcome, however, would require the US to make side payments to Russia, which will even out the differences in the cost savings from cooperative behavior. The second essay analyzes the price dynamics of the Chicago Climate Exchange (CCX), a voluntary emissions trading market. By examining the volatility in market returns using AR-GARCH and Markov switching models, the study associates the market price fluctuations with two different political regimes of the US government. Further, the study also identifies a high volatility in the returns few months before the market collapse. Three possible regulatory and market-based forces are identified as probable causes of market volatility and its ultimate collapse. Organizers of other voluntary markets in the US and worldwide may closely watch for these regime switching forces in order to overcome emission market crashes. The third essay compares excess skewness and kurtosis in carbon prices between CCX and EU ETS (European Union Emission Trading Scheme) Phase I and II markets, by examining the tail behavior when market expectations exceed the threshold level. Dynamic extreme value theory is used to find out the mean price exceedence of the threshold levels and estimate the risk loss. The calculated risk measures suggest that CCX and EU ETS Phase I are extremely immature markets for a risk investor, whereas EU ETS Phase II is a more stable market that could develop as a mature carbon market in future years.

A capability maturity model to assess supply chain performance

In an overcapacity world, where the customers can choose from many similar products to satisfy their needs, enterprises are looking for new approaches and tools that can help them not only to maintain, but also to increase their competitive edge. Innovation, flexibility, quality, and service excellence are required to, at the very least, survive the on-going transition that industry is experiencing from mass production to mass customization. In order to help these enterprises, this research develops a Supply Chain Capability Maturity Model named S(CM)2. The Supply Chain Capability Maturity Model is intended to model, analyze, and improve the supply chain management operations of an enterprise. The Supply Chain Capability Maturity Model provides a clear roadmap for enterprise improvement, covering multiple views and abstraction levels of the supply chain, and provides tools to aid the firm in making improvements. The principal research tool applied is the Delphi method, which systematically gathered the knowledge and experience of eighty eight experts in Mexico. The model is validated using a case study and interviews with experts in supply chain management. The resulting contribution is a holistic model of the supply chain integrating multiple perspectives, and providing a systematic procedure for the improvement of a company’s supply chain operations.

A Capability Maturity Model to Assess Supply Chain Performance

In an overcapacity world, where the customers can choose from many similar products to satisfy their needs, enterprises are looking for new approaches and tools that can help them not only to maintain, but also to increase their competitive edge. Innovation, flexibility, quality, and service excellence are required to, at the very least, survive the on-going transition that industry is experiencing from mass production to mass customization. In order to help these enterprises, this research develops a Supply Chain Capability Maturity Model named S(CM)2. The Supply Chain Capability Maturity Model is intended to model, analyze, and improve the supply chain management operations of an enterprise. The Supply Chain Capability Maturity Model provides a clear roadmap for enterprise improvement, covering multiple views and abstraction levels of the supply chain, and provides tools to aid the firm in making improvements. The principal research tool applied is the Delphi method, which systematically gathered the knowledge and experience of eighty eight experts in Mexico. The model is validated using a case study and interviews with experts in supply chain management. The resulting contribution is a holistic model of the supply chain integrating multiple perspectives, and providing a systematic procedure for the improvement of a company’s supply chain operations.