Some Improvements of Using the NSGA-II Algorithm for the Problem of Resource Allocation and Scheduling and Its Applying to Inventory Management Strategies

Vendor-managed inventory (VMI) is an approach to prevent undesired stocking inventories and hence can lead to a cost reduction of the whole supply chain. One of the main objectives of this approach is to optimize the inventory buffer as safety stock and to optimize the scheduling of inventory and de...

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Bibliographic Details
Published in2019 11th International Conference on Knowledge and Systems Engineering (KSE) pp. 1 - 6
Main Authors Huynh, Quyet-Thang, Nguyen, Doan-Cuong, Dao, Thanh-Chung, Vu, Thanh-Trung, Vu, Thi-Huong-Giang, Nguyen, Thi-Xuan-Hoa
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
Delays
Genetic Algorithm
Inventory management
Linear programming
MOEA Framework
NSGA-II
Optimize Multiple Objectives
Resource Conflict
Resource management
Scheduling
Supply chains
Task analysis
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Summary:Vendor-managed inventory (VMI) is an approach to prevent undesired stocking inventories and hence can lead to a cost reduction of the whole supply chain. One of the main objectives of this approach is to optimize the inventory buffer as safety stock and to optimize the scheduling of inventory and delivery. Such optimization could be considered as a problem of the project's resource scheduling and allocation. In this paper, we present some experimentations for solving this problem by implementing two different algorithms: (i) the Nondominated Sorting Genetic Algorithm (NSGA-II), and (ii) the multi-objective optimization algorithm provided by the MOEA framework. Based on the experimented results, we propose some improvements in using NSGA-II to define an optimized VMI strategy. Such a strategy is implemented and demonstrated through the data collected from a real VMI project.
DOI:10.1109/KSE.2019.8919492