Article,
Deriving inventory-control policies with genetic programming
OR Spectrum, 26 (4): 521--546 (October 2004)
DOI: doi:10.1007/s00291-004-0159-5
Abstract
One of the key areas of operations and supply chain
management is inventory control. Inventory control
determines which quantity of a product should be
ordered when to achieve some objective, such as
minimising cost. Inventory-control policies are
typically derived analytically, and this requires
advanced mathematical skills and can be quite time
consuming. In this paper, we present an alternative
approach for solving inventory-control problems that is
based on Genetic Programming. Genetic Programming is an
optimisation method that applies the principles of
natural evolution to optimization problems. One of the
key characteristics of Genetic Programming is that it
does not require the specification of how a problem
should be solved, but only the specification of what
needs to be solved. After the user has specified the
problem, GP searches for a solution without significant
human involvement. The solutions generated by GP can be
simple algorithms or closed-form expressions that
represent the decision variables, i.e., the order point
and the order quantity as a function of the problem
parameters. However, expert knowledge in inventory
control is still essential for building the inventory
models and determining the parameters of Genetic
Programming. Genetic Programming searches for both the
structure and the parameters of the optimal solution.
For simple settings, the structure and the parameters
of the optimal solution can be found. For complex
settings, near-optimal solutions that outperform
traditional heuristics can be found if the structure of
the optimal solution is known.
