Decision-Making System for Freeway Incident Response Using Sequential Hypothesis Testing Methods

1996 ◽  
Vol 1554 (1) ◽  
pp. 228-235 ◽  
Author(s):  
Samer M. Madanat ◽  
Michael J. Cassidy ◽  
Hua-Liang Teng ◽  
Pen-Chi Liu
Keyword(s):  
Decision Making ◽  
Hypothesis Testing ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Incident Response ◽  
False Response ◽  
Sequential Hypothesis ◽  
Decision Making System ◽  
Sequential Hypothesis Testing ◽  
Response Decision

Recent research in advanced traffic management systems has emphasized incident detection and response to mitigate nonrecurring congestion. Existing incident response decision-making algorithms do not account for the expected losses associated with false alarms, undetected incidents, and delayed incident response. A freeway incident response decision-making system based on sequential hypothesis testing techniques is presented. The primary feature of this decision-making system is that it minimizes the sum of the expected losses associated with false response, nonresponse, and delayed responses to incidents through a dynamic programming algorithm. The results of simulation tests indicate that this algorithm performs better than typical Bayesian incident response algorithms for mean response time, false response rate, and nonresponse rate.

Bridge Inspection Decision Making Based on Sequential Hypothesis Testing Methods

2000 ◽  
Vol 1697 (1) ◽  
pp. 14-18 ◽  
Author(s):  
Samer Madanat ◽  
Da-Jie Lin
Keyword(s):  
Decision Making ◽  
Hypothesis Testing ◽  
Mathematical Formulation ◽  
Bridge Management ◽  
Statistical Decision ◽  
Sequential Hypothesis ◽  
Bridge Structures ◽  
Technological Developments ◽  
Sequential Hypothesis Testing ◽  
The Cost

A bridge management system (BMS) is a decision support system used by a highway agency in selecting appropriate maintenance and rehabilitation (M&R) activities and in allocating available resources effectively among facilities. BMS decision making is based on the condition of bridge components, their predicted deterioration, and the cost and effectiveness of M&R activities. Traditionally, bridge condition assessments have relied mainly on human inspectors; their results have generally been qualitative and subjective. More detailed inspections requiring some degree of destruction of the bridge, like drilling the deck to inspect for chloride contamination, have also been used. With recent technological developments, methods have been developed to evaluate the condition of bridge structures in a quantitative and objective manner. Associated with the use of these technologies are questions relating to inspection frequency, sample size, and the integration of data from the various technologies and human inspections. The application of a statistical decision-making method, sequential hypothesis testing, to these questions is presented. The mathematical formulation of the sequential hypothesis testing model, the derivation of optimal inspection policies, and the implementation of these policies in the context of bridge component inspection are discussed. A parametric analysis illustrates the sensitivity of the method to the cost structure of the problem, the precision of the technologies used, and the historical information or expert judgment regarding the condition of bridge components.

scholarly journals Redistributed manufacturing system under uncertain evaluation using multi criteria decision making

2021 ◽  
Vol 12 (3) ◽  
pp. 3895-3908
Author(s):  
Ponugupati Narendra Mohan Et.al
Keyword(s):  
Decision Making ◽  
Research Work ◽  
Dynamic Programming Algorithm ◽  
Big Data Analytics ◽  
Global Market ◽  
Programming Algorithm ◽  
Multi Criteria Decision Making ◽  
Firm Level ◽  
Data Set ◽  
Value Analysis

Man In recent day’s occurrence of a global crisis in Environmental (Emission of pollutants) and in Health (Pandemic COVID-19) created a recession in all sectors. The innovations in technology lead to heavy competition in global market forcing to develop new variants especially in the automobile sector. This creates more turbulence in demand at the production of new models, maintenance of existing models that are obsolete while implementation of Bharat Standard automobile regulatory authority BS-VI of India. In this research work developed a novel model of value analysis is integrated by multi-objective function with multi-criteria decision-making analysis by incorporating the big data analytics with green supply chain management to bridge the gap in demand to an Indian manufacturing sector using a firm-level data set using matrix chain multiplication dynamic programming algorithm and the computational results illustrates that the algorithm proposed is effective.

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