Browsing by Author "Kulkarni, Aditya Umesh"

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    Approximating Deterministic Changes to Ph(t)/Ph(t)/1/c and Ph(t)/M(t)/s/c Queueing Models
    Kulkarni, Aditya Umesh (Virginia Tech, 2012-05-25)
    A deterministic change to a time-varying queueing model is described as either changing the number of entities, the queue capacity, or the number of servers in the system at selected times. We use a surrogate distribution for N(t), the number of entities in the system at time t, to approximate deterministic changes to the Ph(t)/Ph(t)/1/c and the Ph(t)/M(t)/s/c queueing models. We develop a solution technique to minimize the number of state probabilities to be approximated.
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    Incentive Mechanism Design for Systems with Many Agents: A Multiscale Decision Theory Approach
    Kulkarni, Aditya Umesh (Virginia Tech, 2018-08-14)
    Incentives are an effective mechanism to align the interests of decision-makers. For example, employers use incentives to motivate their employees to take actions that benefit both the employers and the employees. Incentives also play a role in the interaction between firms, for example in a supply chain network. The prevalent approach to analyzing interactions between decision-makers is through principal-agent models. Due to mathematical intractability, the majority of these models are restricted to the interaction between two decision-makers. However, modern organizations have many decision-makers that interact with each other in a network. Therefore, effective incentive mechanisms for systems with many decision-makers (agents) must account for the numerous network interdependencies. The objective of this dissertation is to design incentive mechanisms for systems with many agents, with a focus on teams and multi-firm networks. Methodologically, our approach applies and builds upon multiscale decision theory (MSDT). MSDT can effectively and efficiently model the interdependencies between decision-makers and their optimal response to incentives. This dissertation consists of three parts. The first part focuses on incentives in teams, where multiple subordinates work under a single supervisor. The contribution of the team model to MSDT is the introduction of continuous decision variables; prior MSDT models have only used discrete decision variables. In the second and third part of this dissertation, we analyze a network of collaborating firms in a systems engineering project and focus on verification decisions. We introduce a belief-based model, which is a novel approach for both MSDT and verification modeling in systems engineering. Using MSDT, we determine how incentives can be used by a contractor to motivate a subcontractor to verify its design when the subcontractor prefers not to do so. We extend this two-firm model to a general multi-firm network model for verification coordination and incentivization. This firm network resembles the inter-firm collaboration present in most large-scale system engineering projects. Through better aligned verification activities, system-wide verification costs decrease, while the reliability of the final system improves.