Simultaneous Facility Location and Path Optimization in Static and Dynamic Networks

Srivastava, Amber; Salapaka, Srinivasa M.

doi:10.1109/tcns.2020.2995831

Cited by 11 publications

(21 citation statements)

References 28 publications

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“…Table 2 shows the transportation distance, the transportation In addition, some other data that have an impact on the BMT planning need to be included. The complementary data include node 5, node 7, node 10, node 17 and node 18, all of which have the potential to perform as the transit station where the transportation modes can be changed; through investigation, the waterway of the path section (10,17) and (17,13) fails to transport in the dry season due to the channel depth.…”

Section: Definition Of the Transportation Networkmentioning

confidence: 99%

“…In similar research fields, such as dangerous goods transportation [9], logistics [10] and urban traffic planning [11], the research is generally carried out by path optimization. These studies transform the transportation network into a path-node topology relationship network, use the path's edge weight to reflect the optimization objectives comprehensively and adopt the optimal path algorithm to find the optimal transportation scheme [12][13][14]. For the scheme decision-making problem, the commonly used research method is to establish a decisionmaking model by selecting the main influencing factors as evaluation indicators and then adopt an appropriate MCDM solution algorithm to select the optimal scheme [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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A Two-Stage Decision-Making Method Based on WebGIS for Bulk Material Transportation of Hydropower Construction

et al. 2022

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Bulk materials are necessary for hydropower construction. The bulk materials transportation (BMT) scheme is a guiding document for material supply, and its selection has a significant influence on hydropower construction. Since the BMT problem includes transportation planning and scheme selection issues simultaneously, only a small number of studies have focused on it. This paper presents a theoretical two-stage decision-making method (TDM), which innovatively combines the path optimization method and the multi-criteria decision-making (MCDM) method to solve the BMT problem. In the first stage, a multi-source path optimization model is established to optimize the transportation network and obtain a set of alternatives from each supply point to the construction site. In the second stage, considering the factors of economy, risk and construction progress, the MCDM method is adopted to select the optimal scheme from the alternatives. In addition, web crawler technology is used to obtain the transportation network data from the public WebGIS automatically. Case results show that the TDM can effectively solve this problem, and its result keeps consistent with engineering practice; with the help of the web crawler, it can reduce the design task time from months to days. Therefore, the TDM based on WebGIS can benefit hydropower construction design efficiency.

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Section: Definition Of the Transportation Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Two-Stage Decision-Making Method Based on WebGIS for Bulk Material Transportation of Hydropower Construction

et al. 2022

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“…However, we model them in a specific way to retain the Markov property without any loss of generality, thereby making these problems tractable. Scenarios such as self organizing networks [23], 5G small cell network design [24], [25], supply chain networks, and last mile delivery problems [26] pose a parameterized MDP with a two-fold objective of determining simultaneously (a) the optimal control policy for the underlying stochastic process, and (b) the unknown parameters that the state and action variables depend upon such that the cumulative cost is minimized. The latter objective is akin to facility location problem [27]- [29], that is shown to be NP-hard [27], and where the associated cost function (non-convex) is riddled with multiple poor local minima.…”

Section: Introductionmentioning

confidence: 99%

Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework

Srivastava

Salapaka

2022

IEEE Trans. Cybern.

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We present a framework to address a class of sequential decision making problems. Our framework features learning the optimal control policy with robustness to noisy data, determining the unknown state and action parameters, and performing sensitivity analysis with respect to problem parameters. We consider two broad categories of sequential decision making problems modeled as infinite horizon Markov Decision Processes (MDPs) with (and without) an absorbing state. The central idea underlying our framework is to quantify exploration in terms of the Shannon Entropy of the trajectories under the MDP and determine the stochastic policy that maximizes it while guaranteeing a low value of the expected cost along a trajectory. This resulting policy enhances the quality of exploration early on in the learning process, and consequently allows faster convergence rates and robust solutions even in the presence of noisy data as demonstrated in our comparisons to popular algorithms such as Q-learning, Double Q-learning and entropy regularized Soft Q-learning. The framework extends to the class of parameterized MDP and RL problems, where states and actions are parameter dependent, and the objective is to determine the optimal parameters along with the corresponding optimal policy. Here, the associated cost function can possibly be non-convex with multiple poor local minima. Simulation results applied to a 5G small cell network problem demonstrate successful determination of communication routes and the small cell locations. We also obtain sensitivity measures to problem parameters and robustness to noisy environment data.

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“…Scenarios such as self organizing networks [7], 5G small cell networks [8], supply chain, UAV communcation systems [9], and last mile delivery problems [10] pose a parameterized Sequential Decision Making (para-SDM) problem. Here the main difference is that the states and actions, and the cost function themselves depend on external parameters.…”

Section: Introductionmentioning

confidence: 99%

“…These dynamic para-SDM problems belong to the class of combinatorial optimization problems where the underlying model parameters are time-varying. Prior literature such as [17,18,19] address specific instances of such time-varying combinatorial optimization problems. For instance, [17,18] address the data clustering problems where the underlying data points have associated velocity [17] or acceleration-driven [20] dynamics.…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Parameters in Sequential Decision Making Problems

Srivastava¹,

Salapaka²

2022

Preprint

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In this paper we address the class of Sequential Decision Making (SDM) problems that are characterized by time-varying parameters. These parameter dynamics are either pre-specified or manipulable. At any given time instant the decision policy -that governs the sequential decisions -along with all the parameter values determines the cumulative cost incurred by the underlying SDM. Thus, the objective is to determine the manipulable parameter dynamics as well as the time-varying decision policy such that the associated cost gets minimized at each time instant. To this end we develop a control-theoretic framework to design the unknown parameter dynamics such that it locates and tracks the optimal values of the parameters, and simultaneously determines the time-varying optimal sequential decision policy. Our methodology builds upon a Maximum Entropy Principle (MEP) based framework that addresses the static parameterized SDMs. More precisely, we utilize the resulting smooth approximation (from the above framework) of the cumulative cost as a control Lyapunov function. We show that under the resulting control law the parameters asymptotically track the local optimal, the proposed control law is Lipschitz continuous and bounded, as well as ensure that the decision policy of the SDM is optimal for a given set of parameter values. The simulations demonstrate the efficacy of our proposed methodology.

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Simultaneous Facility Location and Path Optimization in Static and Dynamic Networks

Cited by 11 publications

References 28 publications

A Two-Stage Decision-Making Method Based on WebGIS for Bulk Material Transportation of Hydropower Construction

A Two-Stage Decision-Making Method Based on WebGIS for Bulk Material Transportation of Hydropower Construction

Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework

Time-Varying Parameters in Sequential Decision Making Problems

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