- author = "Rui L. Lopes and Goncalo Figueira and Pedro Amorim and Bernardo Almada-Lobo",
- title = "Cooperative coevolution of expressions for {(r,Q)} inventory management policies using genetic programming",
- journal = "International Journal of Production Research",
- year = "2020",
- volume = "58",
- number = "2",
- pages = "509--525",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "0020-7543",
- bibsource = "OAI-PMH server at oai.repec.org",
- identifier = "RePEc:taf:tprsxx:v:58:y:2020:i:2:p:509-525",
- oai = "oai:RePEc:taf:tprsxx:v:58:y:2020:i:2:p:509-525",
-
URL = "
http://hdl.handle.net/10.1080/00207543.2019.1597293",
-
DOI = "
doi:10.1080/00207543.2019.1597293",
-
abstract = "There are extensive studies in the literature about
the reorder point/order quantity policies for inventory
management, also known as $(r, Q) $(r,Q) policies. Over
time different algorithms have been proposed to
calculate the optimal parameters given the demand
characteristics and a fixed cost structure, as well as
several heuristics and meta-heuristics that calculate
approximations with varying accuracy.
We propose a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than 1 percent, while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics.",
