A genetic algorithm approach for multi-product multi-period continuous review inventory models

Saracoglu I., Topaloglu Ş. A., Keskinturk T.

EXPERT SYSTEMS WITH APPLICATIONS, vol.41, no.18, pp.8189-8202, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 18
  • Publication Date: 2014
  • Doi Number: 10.1016/j.eswa.2014.07.003
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.8189-8202
  • Keywords: Multi-product multi-period policy, Continuous review policy, Inventory management, Variable demand, MULTI-PRODUCT, LEAD TIMES, Q POLICY, SYSTEM
  • Dokuz Eylül University Affiliated: Yes


This paper formulates an approach for multi-product multi-period (Q, r) inventory models that calculates the optimal order quantity and optimal reorder point under the constraints of shelf life, budget, storage capacity, and "extra number of products" promotions according to the ordered quantity. Detailed literature reviews conducted in both fields have uncovered no other study proposing such a multi-product (Q, r) policy that also has a multi-period aspect and which takes all the aforementioned constraints into consideration. A real case study of a pharmaceutical distributor in Turkey dealing with large quantities of perishable products, for whom the demand structure varies from product to product and shows deterministic and variable characteristics, is presented and an easily-applicable (Q,r) model for distributors operating in this manner proposed. First, the problem is modeled as an integer linear programming (ILP) model. Next, a genetic algorithm (GA) solution approach with an embedded local search is proposed to solve larger scale problems. The results indicate that the proposed approach yields high-quality solutions within reasonable computation times. (C) 2014 Elsevier Ltd. All rights reserved.