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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh
Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh
Research Collection School Of Computing and Information Systems
Traffic congestion reduces productivity of individuals by increasing time spent in traffic and also increases pollution. To reduce traffic congestion by better handling dynamic traffic patterns, recent work has focused on online traffic signal control. Typically, the objective in traffic signal control is to minimize expected delay over all vehicles given the uncertainty associated with the vehicle turn movements at intersections. In order to ensure responsiveness in decision making, a typical approach is to compute a schedule that minimizes the delay for the expected scenario of vehicle movements instead of minimizing expected delay over the feasible vehicle movement scenarios. Such …
Decision-Making Method For Formulating Spares Reserve Scheme Based On Deep Neural Network, Yunjing Zhang, Guangming Tang, Xiaoyu Xu
Decision-Making Method For Formulating Spares Reserve Scheme Based On Deep Neural Network, Yunjing Zhang, Guangming Tang, Xiaoyu Xu
Journal of System Simulation
Abstract: Spare parts classification is important for spare parts storage and is a key part of spare parts decision-making activities. This paper analyzes the factors affecting the reserve scheme of wartime spares. Then by analyzing the inherent attributes of wartime spares, two methods of spare parts classification are proposed to determine the variety and quantity of wartime spares based on deep neural network: (1) Ranks wartime spares according to their importance. A relatively simple deep neural network is used to analyze every attribute of the wartime spares in turn; (2) Inputs all the attributes of wartime spares into a relatively …
Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau
Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We study the problem of optimizing the trajectories of agents moving over a network given their preferences over which nodes to visit subject to operational constraints on the network. In our running example, a theme park manager optimizes which attractions to include in a day-pass to maximize the pass’s appeal to visitors while keeping operational costs within budget. The first challenge in this combinatorial optimization problem is that it involves quantities (expected visit frequencies of each attraction) that cannot be expressed analytically, for which we use the Sample Average Approximation. The second challenge is that while sampling is typically done …
Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar
Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar
Research Collection School Of Computing and Information Systems
We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …