Linear programming (LP), as a specific case of mathematical
programming, has been widely encountered in a broad class of
scientific disciplines and engineering applications. In view of its
fundamental role, the solution of LP has been investigated
extensively for the past decades. Due to the parallel-distributed
processing nature and circuit-implementation convenience, the
neurodynamic solvers based on recurrent neural network (RNN) have
been regarded as powerful alternatives to online computation. This
book discusses how linear programming is used to plan and schedule
the workforce in an emergency room; the neurodynamic solvers,
robotic applications, and solution non-uniqueness of linear
programming; the mathematical equivalence of simple recourse and
chance constraints in linear stochastic programming; and provides a
decomposable linear programming model for energy supply chains.
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