Integer Programming Techniques for Solving Non-Linear Workforce Planning Models with Learning

M.R. Hewitt; A. Chacosky; Scott Grasman; B.W. Thomas

Integer Programming Techniques for Solving Non-Linear Workforce Planning Models with Learning

M.R. Hewitt, A. Chacosky, Scott Grasman, B.W. Thomas

Kettering University

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	European Journal of Operational Research
Volume	242
State	Published - 2015

Disciplines

Engineering

Cite this

@article{631d49226d994078a3e8b2b9e5c88912,

title = "Integer Programming Techniques for Solving Non-Linear Workforce Planning Models with Learning",

author = "M.R. Hewitt and A. Chacosky and Scott Grasman and B.W. Thomas",

note = "We address a class of production problems that model human learning with non-linear curves. * We reformulate the non-linear program to an equivalent mixed integer program (MIP). * We present a specialized algorithm for solving the MIP. * We show that the MIP is much easier to solve than the non-linear program.",

year = "2015",

language = "American English",

volume = "242",

journal = "European Journal of Operational Research",

}

TY - JOUR

T1 - Integer Programming Techniques for Solving Non-Linear Workforce Planning Models with Learning

AU - Hewitt, M.R.

AU - Chacosky, A.

AU - Grasman, Scott

AU - Thomas, B.W.

N1 - We address a class of production problems that model human learning with non-linear curves. * We reformulate the non-linear program to an equivalent mixed integer program (MIP). * We present a specialized algorithm for solving the MIP. * We show that the MIP is much easier to solve than the non-linear program.

PY - 2015

Y1 - 2015

UR - https://www.sciencedirect.com/science/article/pii/S0377221714008996?casa_token=IdObuuVoPTUAAAAA:CQkIsn7CsGVsyZO3eryLPQ6yQZjqNr_1d91UK1XDC_5

M3 - Article

VL - 242

JO - European Journal of Operational Research

JF - European Journal of Operational Research

ER -

Integer Programming Techniques for Solving Non-Linear Workforce Planning Models with Learning

Disciplines

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Cite this