Project scheduling problem with mixed uncertainty of randomness and fuzziness

DOI10.1016/j.ejor.2006.09.055zbMath1127.90030OpenAlexW2028160886MaRDI QIDQ2370337

Baoding Liu, Hua Ke

Publication date: 25 June 2007

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2006.09.055

zbMATH Keywords

project schedulinggenetic algorithmrandom fuzzy programming

Mathematics Subject Classification ID

Deterministic scheduling theory in operations research (90B35) Fuzzy and other nonstochastic uncertainty mathematical programming (90C70)

Related Items (7)

Existence and uniqueness of solution for fuzzy random differential equations with non-Lipschitz coefficients ⋮ Multiobjective project portfolio selection with fuzzy constraints ⋮ A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics ⋮ An information axiom based decision making approach under hybrid uncertain environments ⋮ Maximizing the expected net present value of a project with phase-type distributed activity durations: an efficient globally optimal solution procedure ⋮ Nash equilibria in competitive project scheduling ⋮ Comparison of a fuzzy genetic and simulated annealing algorithm approach for project time-cost tradeoff

Cites Work

• Fuzzy project scheduling problem and its hybrid intelligent algorithm
• Depedent-chance programming: A class of stochastic optimization
• Using fuzzy set theory in a scheduling problem: A case study
• Random fuzzy dependent-chance programming and its hybrid intelligent algorithm
• Theory and practice of uncertain programming
• Uncertainty theory. An introduction to its axiomatic foundations.
• Project scheduling problem with stochastic activity duration times
• Chance-Constrained Programming
• Critical-Path Planning and Scheduling: Mathematical Basis
• Critical Path Analyses Via Chance Constrained and Stochastic Programming
• Fuzzy sets
• Expected Value Operator of Random Fuzzy Variable and Random Fuzzy Expected Value Models

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