Integrating Replenishment Policy with GSAA-FCM Based Multi-Criteria Inventory Classification

Abstract

It is neither practical nor economic to assign a specific inventory policy for each item if there are thousands of items in one firm. This paper seeks to solve the stock problem from an integrated perspective by taking into account of both classification of items and replenishment policies for each group. The items are first classified into different groups with respect to the similarity of predefined criteria. The fuzzy clustering-means algorithm (FCM) could help conduct the multi-criteria inventory classification, which considers annual dollar usage, lead time and criticality. Genetic algorithm and simulated annealing algorithm (GSAA) are introduced to eliminate the drawbacks (initial value sensitivity and local optimal convergence) of FCM. A modified FCM algorithm, the GSAA-FCM algorithm, is therefore proposed for classification. Based on the classification, each group is then assigned an appropriate replenishment policy through optimizing both joint replenishment period and the total costs. To demonstrate the usefulness and effectiveness of our method, an illustrative example is provided with a real dataset compared to other 9 methods in previous literature.

Inventory Classification with Limitations in the Number of Changeovers and Space for Inventory

“…A continuous variable neighborhood search was then proposed to find the optimal parameter values that can minimize the total cost. Zhang et al (2018) dealt with an inventory classification problem in which the replenishment policies for each group are taken into consideration. A GSAA-FCM (Genetic and simulated annealing algorithmfuzzy clustering-means) algorithm was proposed to classify items into different groups with respect to the similarity of predefined criteria, such as annual dollar usage, lead time and criticality.…”

“…In Kao et al (2011), Douissa and Jabeur (2016a) and Zhang et al (2018) the set up cost was part of the total cost that was to be minimized. In Malindzakova et al (2022), the number of setting up production lines is the objective to be minimized.…”

A Scatter Search Algorithm for Multi-Criteria Inventory Classification considering Multi-Objective Optimization

“…The ADU, ACU and LT criteria weights used for classification were taken as (0.407, 0.037, 0.556) from the study by Kaabi et al, (2015). We compare our model with other MCIC studies in the literature (Traditional (ADU); (Douissa & Jabeur, 2019;Kaabi et al, 2015;Kaabi et al, 2018;Lolli et al, 2014;Mohammaditabar et al, 2012;Zhang et al, 2018) that is used to benchmark dataset by Reid (1987) and also evalute the TRC functions. To operate the model, firstly the MINLP model was programed with Lingo 2018 and tested with small size problems.…”

“…Ghorabaee, Zavadskas, Olfat, & Turskis (2015) developed a new MCIC method that is called EDAS (Evaluation based on Distance from average Soluiton) by using appraisal score for all inventory items. Zhang, Zhao, and Li (2018) proposed fuzzy clustering-means (FCM) method in the newest of all MCIC studies. They targeted increased speed with GA and local search with simulated annealing (SA).…”

A Scatter Search Algorithm for Multi-Criteria Inventory Classification with Maximize the Satisfaction Level

“…Ghorabaee, Zavadskas, Olfat, & Turskis (2015) developed a new MCIC method that is called EDAS (Evaluation based on Distance from average Soluiton) by using appraisal score for all inventory items. Zhang, Zhao, and Li (2018) proposed fuzzy clustering-means (FCM) method in the newest of all MCIC studies. They targeted increased speed with GA and local search with simulated annealing (SA).…”