Cooperative coevolution of expressions for (r, Q) inventory management policies using genetic programming. Issue 2 (17th January 2020) - Lopes, Rui L.; Figueira, Gonçalo; Amorim, Pedro; Almada-Lobo, Bernardo
Abstract : 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.