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Cooperative coevolution of expressions for ( r,Q ) inventory management policies using genetic programming

2019; Taylor & Francis; Volume: 58; Issue: 2 Linguagem: Inglês

10.1080/00207543.2019.1597293

1366-588X

Rui L. Lopes, Gonçalo Figueira, Pedro Amorim, Bernardo Almada‐Lobo,

Scheduling and Optimization Algorithms

There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as (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.This work proposes 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%, 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.