Paper Info
Title
Hierarchical planning method for product supply based on multi objective Genetic Algorithm
Abstract
Manufacturers have to make dexterous supply plans in order to cope with the demand fluctuations. The demands will change day to day, and also the predicted demand values should be modeled by stochastic variables. Under these uncertain demand conditions, we formulate product supply planning into multi-objective problem pursuing profit maximization and risk minimization. We will propose a method that can generate Pareto optimal solutions based on multi-objective Genetic Algorithm and Monte Carlo simulation. The method adopts searching mode switching strategy to obtain quality Pareto optimal solution, and the method consists of two layers; the upper layer is a global search stage and the lower one is a local search stage to search better solutions around global solutions. Since the local search stage is time consuming, we also propose a fast calculation method of evaluating individuals for Genetic Algorithm.
Year
DOI
Venue
2010
10.1109/ETFA.2010.5641164
ETFA
Keywords
Field
DocType
pareto optimal solutions,monte carlo simulation,multiobjective genetic algorithm,supply chains,multi-objective problem pursuing profit maximization,hierarchical planning method,planning,pareto optimisation,product supply planning,risk management,monte carlo methods,genetic algorithms,risk minimization,dexterous supply plans,local search,gallium,histograms,simulation,genetic algorithm,mathematical model
Histogram,Mathematical optimization,Monte Carlo method,Risk management,Minification,Supply chain,Local search (optimization),Engineering,Profit maximization,Genetic algorithm
Conference
ISSN
ISBN
Citations 
1946-0740
978-1-4244-6848-5
0
PageRank 
References 
Authors
0.34
3
3
Name
Order
Citations
PageRank
Kenzo Kurihara175.23
Hirohito Maruyama200.34
Kazuaki Masuda374.21