In many industrial environments, systems are required to perform a sequence of operations (or missions) with finite breaks between each operation. During these breaks, it may be advantageous to perform repair on some of the system’s components. However, it may be impossible to perform all desirable maintenance activities prior to the beginning of the next mission due to limitations on maintenance resources. In this paper, a mathematical programming framework is established for assisting decision‐makers in determining the optimal subset of maintenance activities to perform prior to beginning the next mission. This decision‐making process is referred to as selective maintenance. The selective maintenance models presented allow the decision‐maker to consider limitations on maintenance time and budget, as well as the reliability of the system. Selective maintenance is an open research area that is consistent with the modern industrial objective of performing more intelligent and efficient maintenance.

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In this paper, we present a two-stage mixed integer programming (MIP) interdiction model in which an interdictor chooses a limited amount of elements to attack first on a given network, and then an operator dispatches trains through the residual network. Our MIP model explicitly incorporates discrete unit flows of trains on the rail network with time-variant capacities. A real coal rail transportation network is used in order to generate scenarios to provide tactical and operational level vulnerability assessment analysis including rerouting decisions, travel and delay costs analysis, and the frequency of interdictions of facilities for the dynamic rail system.

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Increasing system complexity has provided the impetus to develop new and novel systems engineering methodologies. One of these methodologies is set‐based design (SBD), a concurrent design methodology well suited for complex systems subject to significant uncertainty. Since the 1990s, numerous private, public, and defense sector design programs have successfully implemented SBD. However, concerns regarding SBD's complexity, tendency toward qualitative methods, and lack of quantitative tools have limited its use. To address these issues, our research surveys 122 refereed journal articles and conference papers to assess SBD's state‐of‐practice and identify relevant research opportunities. To accomplish these tasks, we perform a structured literature review to identify and assess relevant and influential research. We found that SBD's state‐of‐practice relies heavily upon decision and tradespace analysis with increasing emphasis on uncertainty modeling and MBSE. We found that the majority of SBD research consists of quantitative methodologies focusing on component and small system applications. We also found that complex system applications used mostly qualitative methodologies. We identify SBD research opportunities for requirements development, MBSE, uncertainty modeling, multiresolution modeling, adversarial analysis, and program management. Finally, we recommend the development of a comprehensive SBD methodology and toolkit, suited for complex system design across all stages of the product development life cycle.

In this study, we propose constraint programming (CP) model and logic-based Benders algorithms in order to make the best decisions for scheduling non-identical jobs with availability intervals and sequence dependent setup times on unrelated parallel machines in a fixed planning horizon. In this problem, each job has a profit, cost and must be assigned to at most one machine in such a way that total profit is maximized. In addition, the total cost has to be less than or equal to a budget level. Computational tests are performed on a real-life case study prepared in collaboration with the U.S. Army Corps of Engineers (USACE). Our initial investigations show that the pure CP model is very efficient in obtaining good quality feasible solutions but, fails to report the optimal solution for the majority of the problem instances. On the other hand, the two logic-based Benders decomposition algorithms are able to obtain near optimal solutions for 86 instances out of 90 examinees. For the remaining instances, they provide a feasible solution. Further investigations show the high quality of the solutions obtained by the pure CP model.