Sensitivity Analysis and Dynamic Programming

Tan, Chin Hon; Hartman, Joseph C.

doi:10.1002/9780470400531.eorms0759

Cited by 4 publications

(5 citation statements)

References 47 publications

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“…An important test for evaluating dynamic programming algorithms is the examination of their sensitivity to changes in various parameters of the control process [35][36][37].…”

Section: Computer Simulationmentioning

confidence: 99%

“…Figure 9a shows the safe and optimal trajectory of object 0 in conditions of good visibility, and Figure 9b in conditions of limited visibility. An important test for evaluating dynamic programming algorithms is the examination of their sensitivity to changes in various parameters of the control process [35][36][37]. The measure of sensitivity s was the relative change in the optimal time t opt of the safe and optimal trajectory of object 0 caused by a change in the selected parameter of the control process p:…”

Section: Computer Simulationmentioning

confidence: 99%

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Creating Autonomous Multi-Object Safe Control via Different Forms of Neural Constraints of Dynamic Programming

Lisowski

2024

Electronics

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The aim of this work, which is an extension of previous research, is a comparative analysis of the results of the dynamic optimization of safe multi-object control, with different representations of the constraints of process state variables. These constraints are generated with an artificial neural network and take movable shapes in the form of a parabola, ellipse, hexagon, and circle. The developed algorithm allows one to determine a safe and optimal trajectory of an object when passing other multi-objects. The obtained results of the simulation tests of the algorithm allow for the selection of the best representation of the motion of passing objects in the form of neural constraints. Moreover, the obtained characteristics of the sensitivity of the object’s trajectory to the inaccuracy of the input data make it possible to select the best representation of the motion of other objects in the form of an excessive approximation area as neural constraints of the control process.

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“…An important test for evaluating dynamic programming algorithms is the examination of their sensitivity to changes in various parameters of the control process [35][36][37].…”

Section: Computer Simulationmentioning

confidence: 99%

Section: Computer Simulationmentioning

confidence: 99%

Creating Autonomous Multi-Object Safe Control via Different Forms of Neural Constraints of Dynamic Programming

Lisowski

2024

Electronics

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“…Suppose that in group Agentsi the proportion of agents which uses Low-carbon strategy is x, then the proportion uses strategy is 1 − ; in group Agentsj the proportion of agents which uses Low-carbon strategy is y, then the proportion uses strategy is 1 − ; , respectively stand for agent i and j's profit when using strategy ; , respectively stand for agent i and j's cooperation cost when using strategy ; , respectively stand for agent i and j 's speculativerevenue when using strategy , while the other side's strategy choice is ;ℎ , ℎ respectively stand for agent i and j 's coefficient of profitability; , respectively stand for agent i and j 's coefficient of cooperative profitability. All the parameters in the game payoff matrix are positive number [2]. (2) The two equations describe the evolution of traditional industrial clusters.…”

Section: A Low-carbon Evolutionarymodel Of Chinesetraditional Industr...mentioning

confidence: 99%

Research on the Low-Carbon Evolutionary Model of Chinese Traditional Industrial Clusters Based on Evolutionary Games Theory under Low-Carbon Constraints

Fan

Zhang

2013

AMM

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The low-carbon evolution of traditional industry cluster is the key to a low-carbon economy, and also a frontier of industry cluster theory research. The paper uses evolutionary game theory to construct a low-carbon evolutionary model of Chinese traditional industrial clusters, which considers uncertain factors such as political, economic, cultural, etc. Through the analysis of the cluster low-carbon evolutionary paths and stable equilibrium strategies, the model reflects the inherent law of clusters low-carbon evolution. Finally, the paper gives advices to promote industrial cluster agents to adopt the low-carbon cooperation strategy.

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“…307-314]. In theory, MDPs can be formulated as linear programs [11] and the allowable ρ values can be obtained by applying results from parametric linear programming (see [5] and [23]) on the dual of the associated linear program (see [19]). However, the set of necessary and sufficient conditions (i.e.…”

Section: Stationary Rewardsmentioning

confidence: 99%

Sensitivity Analysis in Markov Decision Processes with Uncertain Reward Parameters

Tan

Hartman

2011

Journal of Applied Probability

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Sequential decision problems can often be modeled as Markov decision processes. Classical solution approaches assume that the parameters of the model are known. However, model parameters are usually estimated and uncertain in practice. As a result, managers are often interested in how estimation errors affect the optimal solution. In this paper we illustrate how sensitivity analysis can be performed directly for a Markov decision process with uncertain reward parameters using the Bellman equations. In particular, we consider problems involving (i) a single stationary parameter, (ii) multiple stationary parameters, and (iii) multiple nonstationary parameters. We illustrate the applicability of this work through a capacitated stochastic lot-sizing problem.

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Sensitivity Analysis and Dynamic Programming

Cited by 4 publications

References 47 publications

Creating Autonomous Multi-Object Safe Control via Different Forms of Neural Constraints of Dynamic Programming

Creating Autonomous Multi-Object Safe Control via Different Forms of Neural Constraints of Dynamic Programming

Research on the Low-Carbon Evolutionary Model of Chinese Traditional Industrial Clusters Based on Evolutionary Games Theory under Low-Carbon Constraints

Sensitivity Analysis in Markov Decision Processes with Uncertain Reward Parameters

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