arXiv subjects

Sukanya Samanta

Publications and source records attributed to Sukanya Samanta.

6 recordsLinked to original sources

arXiv2026

MLN-EIGS: A multilayer network framework for solving Stackelberg escape interdiction games on dynamic transportation networks

Interdicting an escaping criminal with limited police resources on large-scale transportation networks is a challenging problem due to the dynamic nature of both attacker movement and defender deployment. This paper proposes \emph{MLN-EIGS}, a multilayer network-based framework for solving dynamic escape interdiction problems formulated as a Stackelberg security game. A time-expanded multilayer network is constructed to explicitly model the temporal evolution of the transportation network and the feasible movements of both the attacker and the defenders. The attacker seeks to maximize the probability of successful escape, while the defenders aim to maximize the probability of interdiction. To efficiently compute the attacker's best response, the probabilistic escape formulation is transformed into an equivalent shortest-path problem through a logarithmic transformation, enabling the use of Dijkstra's algorithm. Since the defender best-response problem is computationally intractable, an approximation defender oracle is developed to generate high-quality defender strategies on the multilayer network. The proposed MLN-EIGS framework is benchmarked against an exact mixed-integer linear programming (MILP)-based Stackelberg formulation on a large real-world transportation network. Computational experiments demonstrate that MLN-EIGS consistently achieves defender utilities that closely match those of the exact MILP approach while substantially reducing computational time. These results demonstrate that the proposed MLN-EIGS framework provides an effective, computationally efficient, and scalable alternative to exact MILP-based Stackelberg optimization for large-scale dynamic escape interdiction problems.

cs.GT↗

arXiv2025

A Dynamic Programming Approach to Evader Pathfinding in Static Pursuit Scenarios

The interdiction of escaping adversaries in urban networks is a critical security challenge. State-of-the-art game-theoretic models, such as the Escape Interdiction Game (EIG), provide comprehensive frameworks but assume a highly dynamic interaction and entail significant computational complexity, which can be prohibitive for real-time applications. This paper investigates a crucial sub-problem: an evader's optimal pathfinding calculus when faced with a static or pre-determined defender deployment. We propose the Dynamic Programming for Evader Route Optimization (DPERO) algorithm, which models the environment as a graph with probabilistic risks at various nodes. By transforming the multiplicative survival objective into an additive cost function using logarithms, we frame the task as a shortest path problem solvable with value iteration. This approach allows for the efficient computation of a path that optimally balances safety and distance. Experimental results on simulated grid networks demonstrate that DPERO identifies routes with significantly higher survival probabilities compared to naive shortest-path baselines, validating its efficacy as a practical tool for vulnerability analysis and strategic planning.

cs.DS↗

arXiv2025

Maximal Covering Location Problem: A Set Coverage Approach Using Dynamic Programming

The Maximal Covering Location Problem (MCLP) represents a fundamental optimization challenge in facility location theory, where the objective is to maximize demand coverage while operating under resource constraints. This paper presents a comprehensive analysis of MCLP using a set coverage methodology implemented through 0/1 knapsack dynamic programming. Our approach addresses the strategic placement of facilities to achieve optimal coverage of demand points within specified service distances. This research contributes to the understanding of facility location optimization by providing both theoretical foundations and practical algorithmic solutions for real-world applications in urban planning, emergency services, and supply chain management.

cs.DS↗

arXiv2025

Design of A* based heuristic algorithm for efficient interdiction in multi-Layer networks

Intercepting a criminal using limited police resources presents a significant challenge in dynamic crime environments, where the criminal's location continuously changes over time. The complexity is further heightened by the vastness of the transportation network. To tackle this problem, we propose a layered graph representation, in which each time step is associated with a duplicate of the transportation network. For any given set of attacker strategies, a near-optimal defender strategy is computed using the A-Star heuristic algorithm applied to the layered graph. The defender's goal is to maximize the probability of successful interdiction. We evaluate the performance of the proposed method by comparing it with a Mixed-Integer Linear Programming (MILP) approach used for the defender. The comparison considers both computational efficiency and solution quality. The results demonstrate that our approach effectively addresses the complexity of the problem and delivers high-quality solutions within a short computation time.

cs.SI↗

arXiv2024

Solving a Stackelberg Game on Transportation Networks in a Dynamic Crime Scenario: A Mixed Approach on Multi-Layer Networks

Interdicting a criminal with limited police resources is a challenging task as the criminal changes location over time. The size of the large transportation network further adds to the difficulty of this scenario. To tackle this issue, we consider the concept of a layered graph. At each time stamp, we create a copy of the entire transportation network to track the possible movements of both players, the attacker and the defenders. We consider a Stackelberg game in a dynamic crime scenario where the attacker changes location over time while the defenders attempt to interdict the attacker on his escape route. Given a set of defender strategies, the optimal attacker strategy is determined by applying Dijkstra's algorithm on the layered networks. Here, the attacker aims to minimize while the defenders aim to maximize the probability of interdiction. We develop an approximation algorithm on the layered networks to find near-optimal strategy for defenders. The efficacy of the developed approach is compared with the adopted MILP approach. We compare the results in terms of computational time and solution quality. The quality of the results demonstrates the need for the developed approach, as it effectively solves the complex problem within a short amount of time.

cs.AI↗

arXiv2023

Performance assessment of vehicle interdiction strategies in a simulation based environment on a complex transportation network

We consider the escape interdiction problem in a transportation network. In the absence of traffic in the network, the criminal/attacker tries to escape from the city using any of the shortest paths from the crime scene to any randomly chosen exit point. In the presence of traffic, the attacker chooses the optimal path, which takes minimum time to reach his destination. On the contrary, police/defenders try to interdict the criminal on his escape route. This is a challenging task for police with limited resources. Again, a real city road network is also complex in nature. First, we develop a simulation-based model for the optimal allocation of resources using the SUMO simulator. Next, we focus on developing a more advanced search strategy like routing with optimal resource allocation. We develop a novel meta-heuristic approach in a simulation environment to interdict the attacker in a dynamic crime scenario. Like the previous approach, the attacker follows the path with optimal time to escape from the city. In contrast, defenders try to catch the attacker regardless of the path which the attacker takes. The defenders aim is to maximize the interdiction probability. As time plays a vital role, we choose a meta-heuristic approach to provide quality solutions in a time-efficient manner. We test the developed meta-heuristic with a case study on the IIT Kharagpur map, India. We analyze the performance of the mentioned approaches using the SUMO simulator both in the presence of traffic and without traffic. We develop a linear regression model to generate optimal path in the presence of traffic. Here traffic is generated randomly in the whole network using the SUMO simulator.

physics.soc-ph↗