A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms

作者：Fattahi Parviz^*; Hajipour Vahid; Nobari Arash

来源：Applied Soft Computing, 2015, 32: 211-223.

DOI：10.1016/j.asoc.2015.02.044

• Inventory control
• Continuous review
• Multi-objective optimization
• Pareto-based meta-heuristics

In this paper, a bi-objective multi-product (r,Q) inventory model in which the inventory level is reviewed continuously is proposed. The aim of this work is to find the optimal value for both order quantity and reorder point through minimizing the total cost and maximizing the service level of the proposed model simultaneously. It is assumed that shortage could occur and unsatisfied demand could be backordered, too. There is a budget limitation and storage space constraint in the model. With regard to complexity of the proposed model, several Pareto-based meta-heuristic approaches such as multi-objective vibration damping optimization (MOVDO), multi-objective imperialist competitive algorithm (MOICA), multiobjective particle swarm optimization (MOPSO), non-dominated ranked genetic algorithm (NRGA), and non-dominated sorting genetic algorithm (NSGA-II) are applied to solve the model. In order to compare the results, several numerical examples are generated and then the algorithms were analyzed statistically and graphically.

• 出版日期2015-7