Evolutionary Algorithms for Solving Unconstrained Multilevel Lot-Sizing Problem with Series Structure

This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning （MRP） systems. Three evolutionary algorithms （simulated annealing （SA）, particle swarm optimization （PSO） and genetic...

Full description

Saved in:
Bibliographic Details
Published in:Shanghai jiao tong da xue xue bao 2012, Vol.17 (1), p.39-44
Main Author: 韩毅 蔡建湖 IKO U Kaku 李延来 陈以增 唐加福
Format: Article
Language:English
Subjects:
Architecture
Computation
Computer Science
Electrical Engineering
Engineering
Evolutionary algorithms
Genetic algorithms
Life Sciences
Materials requirement planning
Materials Science
Multilevel
Optimization
Simulated annealing
Standard deviation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning （MRP） systems. Three evolutionary algorithms （simulated annealing （SA）, particle swarm optimization （PSO） and genetic algorithm （GA）） are provided. For evaluating the performances of algorithms, the distribution of total cost （objective function） and the average computational time are compared. As a result, both GA and PSO have better cost performances with lower average total costs and smaller standard deviations. When the scale of the multilevel lot-sizing problem becomes larger, PSO is of a shorter computational time.
ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-012-1227-7