The paper investigates the most critical k cut-sets to target in the context of attacks on networks. The targeted network fails if the attack successfully disables a full cut-set. The attack process is dynamic and targets one link at a time. We assume perfect information, so that the attacker picks the next link to target after identifying the outcome of the attack on the previous one. The attack may continue to reach up to k cut-sets. The problem is to identify which cut-sets to target (referred to as the k-critical cut-sets of the network) that maximize the probability of a successful attack. We distinguish both cases of disjoint and non-disjoint cut-sets. We develop an algorithm for each case with illustrations. We explore the large scale of networks and offer guidelines on the corresponding defensive strategies. INDEX TERMS cut-set, defense, interdiction networks, multiple-attacks, path-set.

The airport gate assignment problem (AGAP) is one of the most important problems operations managers face daily. Many researches have been done to solve this problem and tackle its complexity. The objective of the task is assigning each flight (aircraft) to an available gate while maximizing both conveniences to passengers and the operational efficiency of airport. This objective requires a solution that provides the ability to change and update the gate assignment data on a real time basis. In this paper, we survey the state of the art of these problems and the various methods to obtain the solution. Our survey covers both theoretical and real AGAP with the description of mathematical formulations and resolution methods such as exact algorithms, heuristic algorithms, and metaheuristic algorithms. We also provide a research trend that can inspire researchers about new problems in this area.

This paper includes a simulation model for KSU Main Student Restaurant that built using Arena simulation software. We proposed some performance measures to be evaluated for our case study, which is the average waiting time in system and the average number of students in queues. The system faced a big pressure in the first hour of serving the lunch, a slightly pressure in the second hour and we can say there is no pressure on the system in the last hour. We used Arena simulation software to build a simulation model and after that, we analyzed the output from the simulation program results and applying "what if" analysis to produce a group of alternatives (scenarios). We ranked these alternatives to choose the best alternative to improve the efficiency of our system to get better service quality during rush hours. We used Arena Process Analyzer to rank and select the best scenario beside the D&D procedures for ranking and selecting the best alternative, which we will explain it latter in details. At the end, we describe some recommendations and future work as the last part of our study.

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