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[1] Water distribution system (WDS) design naturally involves a trade-off between the cost and reliability or robustness of a design. Traditionally, WDS reliability quantification schemes have employed graph theoretic techniques or probabilistic schemes such as Monte Carlo simulation. In recent decades there has been increased interest in the application of so-called reliability surrogate measures, such as flow entropy, the resilience index, and network resilience, because of their ease of use and dramatically reduced computational burden. In this paper, these surrogate measures (as well as a mixed reliability surrogate) are employed in the multiobjective evolutionary design of a set of WDS benchmarks from the literature. The resulting Pareto-optimal sets in cost-reliability space for each surrogate measure are analyzed in terms of their ability to handle demand uncertainty and pipe failure, and a regression analysis is conducted in order to determine whether the surrogate measures are correlated to stochastic reliability and failure reliability as expressed by demand satisfaction measures. It is found that the resilience index demonstrates the best performance under pure stochastic demand variation. However, it lags when compared to the network resilience and mixed reliability measures in terms of reliability under pipe failure conditions. These are recommended as the most practical reliability surrogate measures for use in general WDS design, since they also produce designs that minimize size discontinuities between adjacent pipes. Flow entropy performs relatively poorly in terms of correlation to both stochastic reliability and failure reliability.

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In a military environment an operator is typically required to evaluate the tactical situation in real-time and protect defended assets against enemy threats by assigning available weapon systems to engage enemy craft. This environment requires rapid operational planning and decision making under severe stress conditions, and the associated responsibilities are usually divided between a number of operators and computerized decision support systems that aid these operators during the decision making processes. The aim in this paper is to review the state of the art of this kind of threat evaluation and weapon assignment decision support process as it stands within the context of a ground based air defence system (GBADS) at the turn of the twenty first century. However, much of the contents of the paper may be generalized to military environments other than a GBADS one.

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The generator maintenance scheduling (GMS) problem is the difficult combinatorial optimisation problem of finding a schedule for the planned maintenance outages of generating units in a power system. The GMS model considered in this paper is formulated as a mixed integer program, with a reliability optimality criterion, subject to a number of constraints. A new version of the simulated annealing (SA) method for solving the GMS problem is presented. Four cooling schedules (the geometric and three adaptive schedules), two neighbourhood move operators (an elementary move and an ejection chain move operator), and a hybrid local search heuristic/SA algorithm are compared. To our knowledge, this is the first study considering a different SA cooling schedule and move operator in a GMS context. A new 32-unit GMS test system is established and used in conjunction with a benchmark test system from the literature in this investigation. It is found that choosing a different cooling schedule and an ejection chain move operator yield improved results to that of the SA algorithm currently employed in the GMS literature. The hybrid SA algorithm performs very well compared to other methods on the benchmark test system from the literature, and an improved lower bound on the objective function value is presented for this test system.

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