Risk-control approach for bottleneck transportation problem with randomness and fuzziness

Solving transportation problems is essential in engineering and supply chain management, where profitability depends on optimal traffic flow. This study proposes risk-control approaches for two bottleneck transportation problems with random variables and preference levels to objective functions with...

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Veröffentlicht in:Journal of global optimization Jg. 60; H. 4; S. 663 - 678
1. Verfasser: Hasuike, Takashi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.12.2014
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Bottlenecks
Computer Science
Consumers
Decision making
Logistics
Mathematical analysis
Mathematical models
Mathematical programming
Mathematics
Mathematics and Statistics
Network management systems
Normal distribution
Operations Research/Decision Theory
Optimization
Pareto optimum
Problem solving
Random variables
Real Functions
Risk
Stochastic models
Supplies
Supply chain management
Supply chains
Transformations
Transportation
Transportation industry
Transportation problem
Strict algorithm
Multiobjective programming problem
Risk-control
Bottleneck transportation problem
ISSN:0925-5001, 1573-2916
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Abstract Solving transportation problems is essential in engineering and supply chain management, where profitability depends on optimal traffic flow. This study proposes risk-control approaches for two bottleneck transportation problems with random variables and preference levels to objective functions with risk parameters. Each proposed model is formulated as a multiobjective programming problem using robust-based optimization derived from stochastic chance constraints. Since it is impossible to obtain a transportation pattern that optimizes all objective functions, our proposed models are numerically solved by introducing an aggregation function for the multiobjective problem. An exact algorithm that performs deterministic equivalent transformations and introduces auxiliary problems is also developed.
AbstractList Solving transportation problems is essential in engineering and supply chain management, where profitability depends on optimal traffic flow. This study proposes risk-control approaches for two bottleneck transportation problems with random variables and preference levels to objective functions with risk parameters. Each proposed model is formulated as a multiobjective programming problem using robust-based optimization derived from stochastic chance constraints. Since it is impossible to obtain a transportation pattern that optimizes all objective functions, our proposed models are numerically solved by introducing an aggregation function for the multiobjective problem. An exact algorithm that performs deterministic equivalent transformations and introduces auxiliary problems is also developed.
Issue Title: Special Issue: OPTIMA-2012 (5 papers); regular papers Solving transportation problems is essential in engineering and supply chain management, where profitability depends on optimal traffic flow. This study proposes risk-control approaches for two bottleneck transportation problems with random variables and preference levels to objective functions with risk parameters. Each proposed model is formulated as a multiobjective programming problem using robust-based optimization derived from stochastic chance constraints. Since it is impossible to obtain a transportation pattern that optimizes all objective functions, our proposed models are numerically solved by introducing an aggregation function for the multiobjective problem. An exact algorithm that performs deterministic equivalent transformations and introduces auxiliary problems is also developed.[PUBLICATION ABSTRACT]
Audience Academic
Author Hasuike, Takashi
Author_xml – sequence: 1
  givenname: Takashi
  surname: Hasuike
  fullname: Hasuike, Takashi
  email: thasuike@ist.osaka-u.ac.jp
  organization: Graduate School of Information Science and Technology, Osaka University
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Keywords Strict algorithm
Multiobjective programming problem
Risk-control
Bottleneck transportation problem
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Snippet Solving transportation problems is essential in engineering and supply chain management, where profitability depends on optimal traffic flow. This study...
Issue Title: Special Issue: OPTIMA-2012 (5 papers); regular papers Solving transportation problems is essential in engineering and supply chain management,...
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SubjectTerms Algorithms
Bottlenecks
Computer Science
Consumers
Decision making
Logistics
Mathematical analysis
Mathematical models
Mathematical programming
Mathematics
Mathematics and Statistics
Network management systems
Normal distribution
Operations Research/Decision Theory
Optimization
Pareto optimum
Problem solving
Random variables
Real Functions
Risk
Stochastic models
Supplies
Supply chain management
Supply chains
Transformations
Transportation
Transportation industry
Transportation problem
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