Volume 53, Number 5, November-December 2019
|Page(s)||1709 - 1720|
|Published online||09 October 2019|
Optimal multi-product supplier selection under stochastic demand with service level and budget constraints using learning vector quantization neural network
Department of Economics and Management, College of Management, Shiraz Branch, Islamic Azad University, Shiraz, Iran
2 School of Engineering and Information Technology, The University of New South Wales (UNSW), Canberra, Australia
3 Department of Computer Science and Engineering, Sejong University, Seoul, Republic of Korea, Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran
* Corresponding author: firstname.lastname@example.org
Accepted: 20 October 2018
In today’s competitive marketplace demand, evaluation and selection of suppliers are pivotal for firms, and therefore decision makers need to select suppliers and the optimal order quantities when outsourcing. However, there is uncertainty and risk due to lack of precise data for supplier selection. Uncertainty can impose shortage or overstocks, because of stochastic demand, to firms; in this case, considering inventory control is essential. In this research, an appropriate spatial model is developed for a multi-product supplier selection model with service level and budget constraints. Learning Vector Quantization Neural Network is used to find the optimal number of decision variables with the goal of maximizing the expected profit of supply chains. By analyzing a practical example and conducting sensitivity analysis, we find that corporate profit will be maximized if the optimal integration of suppliers and the optimal order quantities from each supplier is determined. In addition, budget and service level should be considered in the process of finding the best result.
Mathematics Subject Classification: 90B30 / 90B15 / 90C30 / 92B20
Key words: Supply chain management / multi-supplier selection / stochastic demand / Learning Vector Quantization (LVQ) neural network / nonlinear programming optimization model
© EDP Sciences, ROADEF, SMAI 2019
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.