Scenario relaxation algorithm for finite scenario-based min–max regret and min–max relative regret robust optimization

Most practical decision-making problems are compounded in difficulty by the degree of uncertainty and ambiguity surrounding the key model parameters. Decision makers may be confronted with problems in which no sufficient historical information is available to make estimates of the probability distri...

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Vydáno v:	Computers & operations research Ročník 35; číslo 6; s. 2093 - 2102
Hlavní autoři:	Assavapokee, Tiravat, Realff, Matthew J., Ammons, Jane C., Hong, I-Hsuan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 01.06.2008 Elsevier Science Pergamon Press Inc
Témata:	Algorithms Applied sciences Computer science; control theory; systems Data processing. List processing. Character string processing Decision making Decision making models Decision theory. Utility theory Exact sciences and technology Integer programming Linear programming Mathematical programming Memory organisation. Data processing Min–max regret Min–max relative regret Operational research and scientific management Operational research. Management science Optimization algorithms Parameter estimation Robust optimization Scenario-based decision-making Scenarios Software Studies Uncertainty Scenarios Robust optimization Min–max regret Scenario-based decision-making Min–max relative regret Termination problem Psychology Iterative method Probability distribution Modeling Linear model Optimization Min-max regret Uncertain system Min-max relative regret Mathematical programming Script Decision support system Decision making Linear programming Mixed integer programming Long term Mixed model Optimal solution Minimax method Mixed problem Ambiguity
ISSN:	0305-0548, 1873-765X, 0305-0548
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