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Multi-Objective Optimization of Slow Moving Inventory System Using Cuckoo Search

Achin Srivastav, Sunil Agrawal

Department of Mechanical Engineering, Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India

* Corresponding Authors: Achin Srivastav, email, email

Intelligent Automation & Soft Computing 2018, 24(2), 343-350. https://doi.org/10.1080/10798587.2017.1293891

Abstract

This paper focuses on the development of a multi-objective lot size–reorder point backorder inventory model for a slow moving item. The three objectives are the minimization of (1) the total annual relevant cost, (2) the expected number of stocked out units incurred annually and (3) the expected frequency of stockout occasions annually. Laplace distribution is used to model the variability of lead time demand. The multi-objective Cuckoo Search (MOCS) algorithm is proposed to solve the model. Pareto curves are generated between cost and service levels for decision-makers. A numerical problem is considered on a slow moving item to illustrate the results. Furthermore, the performance of the MOCS algorithm is evaluated in comparison to multi-objective particle swarm optimization (MOPSO) using metrics, such as error ratio, maximum spread and spacing.

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Cite This Article

A. Srivastav and S. Agrawal, "Multi-objective optimization of slow moving inventory system using cuckoo search," Intelligent Automation & Soft Computing, vol. 24, no.2, pp. 343–350, 2018.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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