Healthcare is indeed a complex service system, one requiring the technobiology approach of systems engineering to underpin its development as an integrated and adaptive system. In general, healthcare services are carried out with knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Indeed, the engineering design of a healthcare system must recognize the fact that it is actually a complex integration of human-centered activities that is increasingly dependent on information technology and knowledge. Like any service system, healthcare can be considered to be a combination or recombination of three essential components — people (characterized by behaviors, values, knowledge, etc.), processes (characterized by collaboration, customization, etc.) and products (characterized by software, hardware, infrastructures, etc.). Thus, a healthcare system is an integrated and adaptive set of people, processes and products. It is, in essence, a system of systems which objectives are to enhance its efficiency (leading to greater interdependency) and effectiveness (leading to improved health). Integration occurs over the physical, temporal, organizational and functional dimensions, while adaptation occurs over the monitoring, feedback, cybernetic and learning dimensions. In sum, such service systems as healthcare are indeed complex, especially due to the uncertainties associated with the human-centered aspects of these systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation.

Most of the spare ordering policies treated up to now have assumed that preventive and corrective replacement costs are equal, which implies in essential that there is no significant need for preventive replacement. This paper presents an ordering policy for preventive age replacement with minimal repair. Introducing the replacement, repair, inventory holding and shortage costs, the expected cost rate is derived. A procedure to determine jointly the ordering time for a spare and the preventive replacement time for the operating unit so as to minimize the expected cost rate is proposed. To explain the ordering policy and the optimization procedure, a numerical example is also included.

Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how could such accuracy be improved by using pairs of classifier ensembles. Benchmarking results on five credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.

In this paper the process of knowledge accumulation for a particular technology is studied. Two countries, say the technology follower and the technology frontier, are considered. The frontier’s knowledge growth is determined by its R&D efforts on the technology. The level of knowledge stock for the follower country is augmented by its R&D activities for the technology and absorbing some of the external knowledge through spillover from the frontier. The extent to which the follower is able to exploit the external knowledge depends on technological gap, absorptive capacity, absorption time and degree of spillover. New concepts such as natural and enhanced degree of spillover, background and innovative knowledge and absorption speed are introduced in the present work to deeply explore the process of knowledge spillover. The factors influencing the knowledge development in the long term are simultaneously studied in an integrated structure provided by the System Dynamics approach. This framework shows the responses to the changes and provides the basis for examining the interactions among the variables over time.

This paper presents a comparison mechanism for a simple procurement auction and generalized Nash bargaining solution in a procurement circumstance. The buyer who hopes to fulfill a project has two options. First, she may award the project to the old provider with an efficient quality by bargaining. Second, she may give the project to the firm who submits the lowest bid through a simple procurement second-price sealed-bid auction with a pre-fixed quality. We compare the expected revenue in the auction with the deterministic profit of the bargaining. In different parameters cases, we find the borderline functions between the two options with respect to the buyer’s bargaining power and the amount of bidders. We also compare our simple procurement auction (SPA) with the optimal multidimensional bid auction by Che (1993), and find our model protects the buyer’s private valuation information by forfeiting some profits. Some properties of the difference between the two auctions are proposed, especially there is no revenue difference when the amount of bidders approaches infinity.

In this paper, we analyze a bulk input M^[X] /M/1 queue with multiple working vacations. A quasi upper triangle transition probability matrix of two-dimensional Markov chain in this model is obtained, and with the matrix analysis method, highly complicated probability generating function(PGF) of the stationary queue length is firstly derived, from which we got the stochastic decomposition result for the stationary queue length which indicates the evident relationship with that of the classical M^[X] /M/1 queue without vacation. It is important that we find the upper and the lower bounds of the stationary waiting time in the Laplace transform order using the properties of the conditional Erlang distribution. Furthermore, we gain the mean queue length and the upper and the lower bounds of the mean waiting time.

Cascading failures often occur in congested complex networks. Cascading failures can be expressed as a three-phase process: generation, diffusion, and dissipation of congestion. Different from the betweenness centrality, a congestion function is proposed to represent the extent of congestion on a given node. Inspired by the restart process of a node, we introduce the concept of “delay time,” during which the overloaded node cannot receive or forward any traffic, so an intergradation between permanent removal and nonremoval is built and the flexibility of the presented model is demonstrated. Considering the connectivity of a network before and after cascading failures is not cracked because the overloaded node are not removed from network permanently in our model, a new evaluation function of network efficiency is also proposed to measure the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability, and traffic generation speed on congestion propagation. Cascading processes composed of three phases and some factors affecting cascade propagation are uncovered as well.