A formal policy development framework, based on a system of systems (SoS) approach, is synthesized to systematically address, in an integrative and adaptive fashion, major global challenges, such as the current food and financial crises, and their interactions with other key natural, societal, and technological systems. A SoS approach seeks to respect the different value systems of multiple participants, to harness complexity through effective integration, and to engage the world of uncertainty and unpredictability with an adaptive response. Faced with the present global challenges, what is needed are strategic and operational methods which lead to ethical policies, enhance integrative and adaptive management practices, and are able to direct conflict resolution in a positive direction. Policy makers need tools to model and analyze complex systems which they are trying to responsibly govern, taking into account values and risks to design and evaluate different policies. A preliminary investigation into the global food system is undertaken to understand the SoS and to provide insights on how to carry out policy development using the proposed framework.

Three major threats to ocean security and coastal zone sustainability - global warming, the loss of ocean biodiversity, and pollution - are combining to threaten the ecological integrity of our marine environment and life support systems. We put forward a geomatics-based systems engineering architecture to identify the location and extent of oil spills, thereby improving the ecological integrity of the world’s oceans and helping contingency planners to determine required assets, personnel and other resources. This real-time, event-based and cost effective emergency management decision support system can aid in the classification, detection, and monitoring of oil spills in the marine environment. The developed Synthetic-Aperture Radar (SAR) processing and calibration techniques efficiently monitor environmental changes in inaccessible ocean regions, characterize oil spill scenarios, and help to identify spill sources. The system is used to improve emergency management in the Gulf of Mexico, with application to oil spills arising from Hurricane Katrina.

This paper studies the parameter design and the performance optimization of a Kanban system without stockouts in a multi-stage, mixed-model assembly line. The model of a Kanban system based on production processes is established by examining the relationship among the set-up time, the amount of work in process (WIP), the capacity indicated by a Kanban, and the takt-time ratio. A novel method for optimizing performance on the premise of no stockouts is proposed. Empirical results show that the amount of WIP is reduced remarkably after optimization.

This paper studies the operating characteristics of an M/G/1 queuing system with a randomized control policy and at most J vacations. After all the customers are served in the queue exhaustively, the server immediately takes at most J vacations repeatedly until at least N customers are waiting for service in the queue upon returning from a vacation. If the number of arrivals does not reach N by the end of the J ^th vacation, the server remains idle in the system until the number of arrivals in the queue reaches N. If the number of customers in the queue is exactly accumulated N since the server remains idle or returns from vacation, the server is activated for services with probability p and deactivated with probability (1 − p). For such variant vacation model, other important system characteristics are derived, such as the expected number of customers, the expected length of the busy and idle period, and etc. Following the construction of the expected cost function per unit time, an efficient and fast procedure is developed for searching the joint optimum thresholds (N*, J*) that minimize the cost function. Some numerical examples are also presented.

In this paper, we investigate the pth moment uniformly asymptotic stability of impulsive stochastic functional differential systems by extending some Razumikhin-type theorems. Based on the Lyapunov functions and Razumikhin techniques, some criteria are established and their applications to impulsive stochastic delay systems are proposed. An illustrative example shows the effectiveness of our results.

In this paper, we consider a newsvendor model in which a risk-averse manager faces a stochastic price-dependent demand in either an additive or a multiplicative form. An emergency purchase option is allowed after the realization of demand to satisfy the units that are short. By adopting conditional value-at-risk (CVaR) as the decision criterion, we aim to investigate the optimal pricing and ordering decisions, and the effects of parameter changes in such a setting. We provide sufficient conditions for the uniqueness of the optimal policy for both demand models. We perform comparative statics analysis to show how the optimal pricing and ordering decision behaves when changing parameters. We also compare our results with those of the newsvendor with a general utility function and with CVaR criterion under lost sales assumption. Our key results include: (i) For both demand models, the optimal selling price is decreasing in risk aversion. Hence, the optimal price of a risk-averse newsvendor is not greater than the optimal price of a risk-neutral newsvendor. (ii) In contrary to the lost sales case, for the multiplicative demand model, the optimal order quantity may not be monotonic in risk aversion. Consequently, the optimal risk-averse order quantity may be lower or higher than the optimal risk-neutral counterpart. (iii) For the additive model, the optimal order quantity is strictly increasing in the emergency purchase price, while for the multiplicative model the optimal order quantity has no such a monotonic property. Some numerical examples are conducted to verify our claims and gain more insights about the risk-averse decision-making behaviors.

Autocorrelation is prevalent in continuous production processes, such as the processes in the chemical and pharmaceutical industries. With the development of measurement technology and data acquisition technology, sampling frequency is getting higher and the existence of autocorrelation cannot be ignored. This paper analyzes five estimation schemes of process capability for autocorrelated data. Comparisons among these schemes are discussed for small sample and large sample. In conclusion, this paper gives a procedure of process capability analysis for autocorrelated data.