Modeling learning effects via successive linear programming
The learning effect is given by a model of the form: \[ \max imize\quad \sum^{n_ 1}_{j=1}[(p_ j-v_ j)x_ j-\sum^{m_ 1}_{i=1}(v_ ia_{ij})x_ j^{(1+b_{ij})}]+\sum^{N}_{j=n_ 1+1}C_ jy_ j \] subject to \[ \sum^{n_ 1}_{j=1}a_{ij}x_ j^{(1+b_{ij})}+\sum^{N}_{j=n_ 1+1}a_{ij}y_ j=r_ i,\quad i=1,...,m_ 1, \] \[ \sum^{n_ 1}_{j=1}a_{ij}x_ j+\sum^{N}_{j=n_ 1+1}a_{ij}y_ j=r_ i,\quad i=m_ 1+1,...,m, \] \[ \ell^ 1_ j\leq x_ j\leq u^ 1_ j,\quad \ell^ 2_ j\leq y_ j\leq u^ 2_ j\quad for\quad all\quad j. \] The problem is solved by successive linear programming approximations. A numerical example is given.
successive linear programming approximations
scientific article; zbMATH DE number 4118172