Abstract:I n a stochastic interdiction game a proliferator aims to minimize the expected duration of a nuclear weapons development project, and an interdictor endeavors to maximize the project duration by delaying some of the project tasks. We formulate static and dynamic versions of the interdictor's decision problem where the interdiction plan is either precommitted or adapts to new information revealed over time, respectively. The static model gives rise to a stochastic program, whereas the dynamic model is formaliz… Show more
“…Let m := max P ∈P |E(P )| denote the maximum number of tasks that can be active in parallel, then it is not difficult to show that |P| is bounded above by m i=0 n i ; see [4]. Indeed, P is completely determined by E(P ) and n i bounds the number of states having |E(P )| = i.…”
We study the problem of scheduling a project so as to maximize its expected net present value when task durations are exponentially distributed. Based on the structural properties of an optimal solution we show that, even if preemption is allowed, it is not necessary to do so. Next to its managerial importance, this result also allows for a new algorithm which improves on the current state of the art with several orders of magnitude, both in CPU time and in memory usage.
“…Let m := max P ∈P |E(P )| denote the maximum number of tasks that can be active in parallel, then it is not difficult to show that |P| is bounded above by m i=0 n i ; see [4]. Indeed, P is completely determined by E(P ) and n i bounds the number of states having |E(P )| = i.…”
We study the problem of scheduling a project so as to maximize its expected net present value when task durations are exponentially distributed. Based on the structural properties of an optimal solution we show that, even if preemption is allowed, it is not necessary to do so. Next to its managerial importance, this result also allows for a new algorithm which improves on the current state of the art with several orders of magnitude, both in CPU time and in memory usage.
“…Shortest-path network interdiction problems can be extended for various application scenarios [23]. Wei et al [24] introduced a threshold on the capacity of a network and proposed algorithms based on decomposition and duality formulation to optimize the interdictor's resource consumption.…”
Shortest-path network interdiction, where a defender strategically allocates interdiction resource on the arcs or nodes in a network and an attacker traverses the capacitated network along a shortest s-t path from a source to a terminus, is an important research problem with potential real-world impact. In this paper, based on game-theoretic methodologies, we consider a novel stochastic extension of the shortest-path network interdiction problem with goal threshold, abbreviated as SSPIT. The attacker attempts to minimize the length of the shortest path, while the defender attempts to force it to exceed a specific threshold with the least resource consumption. In our model, threshold constraint is introduced as a trade-off between utility maximization and resource consumption, and stochastic cases with some known probability p of successful interdiction are considered. Existing algorithms do not perform well when dealing with threshold and stochastic constraints. To address the NP-hard problem, SSPIT-D, a decomposition approach based on Benders decomposition, was adopted. To optimize the master problem and subproblem iteration, an efficient dual subgraph interdiction algorithm SSPIT-S and a local research based better-response algorithm SSPIT-DL were designed, adding to the SSPIT-D. Numerical experiments on networks of different sizes and attributes were used to illustrate and validate the decomposition approach. The results showed that the dual subgraph and better-response procedure can significantly improve the efficiency and scalability of the decomposition algorithm. In addition, the improved enhancement algorithms are less sensitive and robust to parameters. Furthermore, the application in a real-world road network demonstrates the scalability of our decomposition approach.
“…Harney et al (2006) describe the tasks necessary to produce "a first small batch of nuclear weapons" together with their durations and precedence constraints. The project-which has also served as an example in (Brown et al, 2009;Gutin, Kuhn, & Wiesemann, 2014;Pinker et al, 2013)-consists of 124 nondummy tasks that can be grouped into five main parts as listed in Table 5. The table's third and fourth column give each part's start and end time (in weeks) under the late-start schedule with a deadline equal to the project's minimum makespan of 468 weeks.…”
Section: Numerical Example: Developing a First Nuclear Weaponmentioning
A defender wants to detect as quickly as possible whether some attacker is secretly conducting a project that could harm the defender. Security services, for example, need to expose a terrorist plot in time to prevent it. The attacker, in turn, schedules his activities so as to remain undiscovered as long as possible. One pressing question for the defender is: which of the project's activities to focus intelligence efforts on? We model the situation as a zero‐sum game, establish that a late‐start schedule defines a dominant attacker strategy, and describe a dynamic program that yields a Nash equilibrium for the zero‐sum game. Through an innovative use of cooperative game theory, we measure the harm reduction thanks to each activity's intelligence effort, obtain insight into what makes intelligence effort more effective, and show how to identify opportunities for further harm reduction. We use a detailed example of a nuclear weapons development project to demonstrate how a careful trade‐off between time and ease of detection can reduce the harm significantly.
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