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Lockheed Martin Space Systems Company spends millions of dollars on the maintenance and modernization of its infrastructure each year. Projects often involve investments that cannot be justified purely in terms of net present value or other classical investment-evaluation methods. The options are also restricted because funds that are not spent within a given time frame must be relinquished. Furthermore, some projects may be delayed and the unplanned carryover of their costs moved into the next fiscal year; this causes the postponement or cancellation of other unrelated projects because of in-budget transfers. In this paper, we used multiattribute utility theory and chance-constrained programming to optimize the selection of infrastructure projects. Our solution ensured the selection of high-value projects to maximize the company's performance. These selections were subject to the constraints that a portfolio did not exceed the available budget and that the carryover of the unspent funds to the next fiscal year did not exceed predetermined limits. We used Microsoft Excel to ensure broad accessibility, transparency, user interaction, improved data collection and asset management, and ease-of-use by managers.

A multi-stage production/inventory system with decentralized two-card kanban control policies producing multiple product types is considered. The system involves one-at-a-time processing at each stage, finite target levels in the buffers and batch transfers between production stages. The demand process for each product is assumed to be Poisson and excess demand is backordered. Products have a priority structure and the processor at each stage is shared according to a switching rule. Our objective is to obtain steady-state performance measures such as the average inventory and service levels for each product type. We propose an approximation algorithm based on: (i) characterization of the delay by a product type before receiving the processor's attention at each stage; and (ii) creation of subsystems for all the storage activity and phase-type modeling of the remaining system's behavior. Numerical examples are presented to show the accuracy of the method and its potential use in system design.