Abstract
Inventory management requires thousands or millions of individual transactions each year. Classification of the items influences the results of inventory management. Traditionally, this is usually classified with considering an annual dollar usage criterion but maybe other criteria such as lead time, criticality, perishability, inventory cost, and demand type can be affected on that classification. Inventory items that have more than one criterion are discussed under multi-criteria inventory classification (MCIC) methods in the literature. In this paper, the MCIC problem is considered with two objectives as follows: (1) minimization of total inventory relevant cost and (2) minimization of the dissimilarity index. The proposed Mixed Integer Nonlinear Programming (MINLP) model of the MCIC problem is formulated using Scatter Search Algorithm (SSA). The suggested multi-objective optimization problem is solved using LP-metric, ɛ-constraint and weighted sum method. Pareto optimal solutions are obtained according to these different methods and selected best method by using deviation index. Scatter Search Algorithm provides high-quality solutions within reasonable computation times. The proposed model generated a Pareto frontier solution with the maximum satisfaction level and minimum distance from ideal point. Finally, the proposed model is implemented with two numerical datasets to show the performance of its efficiency and compared our results with other studies in the previous literature.
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Saracoglu, I. A Scatter Search Algorithm for Multi-Criteria Inventory Classification considering Multi-Objective Optimization. Soft Comput 26, 8785–8806 (2022). https://doi.org/10.1007/s00500-022-07227-0
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DOI: https://doi.org/10.1007/s00500-022-07227-0