Risk analysis, comprising risk assessment and risk management stages, is one of the most popular and challenging topics of our times because security and privacy, and availability and usability culminating at the trustworthiness of cybersystems and cyber information is at stake. The precautionary need derives from the existence of defenders versus adversaries, in an everlasting Darwinian scenario dating back to early human history of warriors fighting for their sustenance to survive. Fast forwarding to today's information warfare, whether in networks or healthcare or national security, the currently dire situation necessitates more than a hand calculator to optimize (maximize gains or minimize losses) risk due to prevailing scarce economic resources. This article reviews the previous works completed on this specialized topic of game-theoretic computing, its methods and applications toward the purpose of quantitative risk assessment and cost-optimal management in many diverse disciplines including entire range of informaticsrelated topics. Additionally, this review considers certain game-theoretic topics in depth historically, and those computationally resourceful such as Neumann's two-way zero-sum pure equilibrium and optimal mixed strategy solutions versus Nash equilibria with pure and mixed strategies. Computational examples are provided to highlight the significance of game-theoretic solutions used in risk assessment and management, particularly in reference to cybersystems and information security.

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The paper develops an algorithm for eliminating bottleneck parts in a cellular manufacturing setting. Bottlenecks are eliminated by determining the machine duplication pattern that minimizes total duplication costs. A unique feature of the algorithm is that it recognizes that under some conditions machine duplication costs can be minimized by increasing the number of machine cells. Examples of such situations are given.

The formation of machine‐part families is an important task in the design of cellular and flexible manufacturing systems. The formation results in the creation of many benefits for manufacturing systems. Among the many methods utilized in machine‐cells formation, the similarity coefficient method (SCM) is most widely used. When SCM is used, the rearrangement of machine and part components is required to form machine‐part families. This process of rearrangement has been considered as being subjective and difficult and may result in improper assignments to parts families, resulting in a negation of benefits promised. This paper presents an effective algorithm to identify part‐families and bottleneck‐parts, given machine groupings, rather than addressing the machine grouping problem in general.