Paper Info
Title
Particle Swarm Optimization and Pure Adaptive Search in Finish Goods' Inventory Management.
Abstract
Inventory management deals with a tradeoff between the benefits of keeping stocks of goods that allows fulfillment of the customer's demand, and the cost of carrying inventory. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents new inventory management model based on particle swarm optimization and pure adaptive search global optimization algorithm in production-inventory system. The proposed model is focusing on planned level of demand for finished goods, production and raw materials cost, production capacity as the norm, change of the production cost and inventory capital cost, all of which are typical factors in automobile manufacture industry. The model determines different factors such as the minimizing inventory quantity, minimizing inventory value, and minimizing production cost based on demand for production items. The model is tested with original real-world dataset obtained from the automotive company Lear from US and its factory in Novi Sad, Serbia.
Year
DOI
Venue
2019
10.1080/01969722.2018.1558014
CYBERNETICS AND SYSTEMS
Keywords
Field
DocType
Inventory control,inventory quantity,inventory value,particle swarm optimization,production cost,pure adaptive search algorithm
Finished good,Particle swarm optimization,Mathematical optimization,Capital cost,Raw material,Production cost,Inventory control,Inventory valuation,Stock (geology),Mathematics
Journal
Volume
Issue
ISSN
50.0
SP1
0196-9722
Citations 
PageRank 
References 
0
0.34
8
Authors
5
Name
Order
Citations
PageRank
Dragan Simic14012.78
Vasa Svircevic2275.65
Vladimir Ilin332.52
Svetislav Simic424.13
Svetlana Simic54012.78