Identifying Minimally Infeasible Subsystems of Inequalities

Gleeson, John; Ryan, Jennifer K.

doi:10.1287/ijoc.2.1.61

Cited by 96 publications

(69 citation statements)

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“…• If a linear or mixed-integer programming problem is infeasible, an irreducible infeasible subsystem (IIS) of constraints can be calculated [Gleeson andRyan, 1990, Guieu andChinneck, 1999]. Such a subset is infeasible by itself, but if any constraint is removed from the subset, the remaining set of constraints is feasible.…”

Section: Methodsmentioning

confidence: 99%

Mathematical Programming-Based Sales and Operations Planning at Vestel Electronics

et al. 2015

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We investigate the sales and operations planning (S&OP) problem at Vestel Electronics, a major television manufacturer located in Turkey. The company's product portfolio is very wide due to a large number of configuration options, and changes rapidly due to technological advances. Demand volatility is high and materials procurement requires long lead times. Hence, the S&OP process is critical for efficient management of company resources and its supply chain as well as customer satisfaction. We devise

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Section: Methodsmentioning

confidence: 99%

Mathematical Programming-Based Sales and Operations Planning at Vestel Electronics

et al. 2015

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“…On the linear system level this analysis is equivalent to discovering all irreducible infeasible subsystems (IIS) which can be exponentially many [8]. The task of finding all IISes is somewhat complicated by the fact that the linear systems are never complete, so their set of IISes may not be the same as for the complete system (despite that their objective values are optimal).…”

Section: Monotonic Probabilistic Reasoning Layermentioning

confidence: 99%

Pronto: A Practical Probabilistic Description Logic Reasoner

Klinov

Parsia

2013

Uncertainty Reasoning for the Semantic Web II

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Abstract. This paper presents a system description of Pronto -the first probabilistic Description Logic reasoner capable of processing knowledge bases containing about a thousand of probabilistic axioms. We describe the design and architecture of the reasoner with an emphasis on the components that implement algorithms which are crucial for achieving such level of scalability. Finally, we present the results of the experimental evaluation of Pronto's performance on series of propositional and non-propositional probabilistic knowledge bases.

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“…Several methods for identifying these sets using LP methods have been developed [8,12,16]. In more recent years, IISs have been used to generate valid inequalities for the maximum feasible subsystem problem [1,23].…”

Section: Preliminariesmentioning

confidence: 99%