Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.

[1]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[2]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[3]  Willem H.M. Zijm,et al.  Models for warehouse management: Classification and examples , 1999 .

[4]  Xiwen Lu,et al.  On-line supply chain scheduling for single-machine and parallel-machine configurations with a single customer: Minimizing the makespan and delivery cost , 2015, Eur. J. Oper. Res..

[5]  Fan Yang,et al.  Multi-objective Rule Discovery Using the Improved Niched Pareto Genetic Algorithm , 2011, 2011 Third International Conference on Measuring Technology and Mechatronics Automation.

[6]  Milorad Vidović,et al.  Application of genetic algorithms for sequencing of AS/RS with a triple-shuttle module in class-based storage , 2014 .

[7]  Kees Jan Roodbergen,et al.  A survey of literature on automated storage and retrieval systems , 2009, Eur. J. Oper. Res..

[8]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[9]  Taoshen Li,et al.  An Improved Genetic Algorithm on Logistics Delivery in E-business , 2007, Third International Conference on Natural Computation (ICNC 2007).

[10]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[11]  Kazuhiro Takeyasu,et al.  Optimization technique by genetic algorithms for international logistics , 2014, J. Intell. Manuf..

[12]  Marc Goetschalckx,et al.  Research on warehouse operation: A comprehensive review , 2007, Eur. J. Oper. Res..

[13]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[14]  Zhi Jia Huang,et al.  Analysis and Research of the Automated Warehouse Management and Control System , 2014 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Leonardo Maria Reyneri,et al.  GA-based solutions comparison for warehouse storage optimization , 2010, Int. J. Hybrid Intell. Syst..

[17]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[18]  Colla Valentina,et al.  Variable selection through Genetic algorithms for classification purposes , 2010 .

[19]  Mohammad Ali Abido,et al.  Multiobjective optimal power flow using Improved Strength Pareto Evolutionary Algorithm (SPEA2) , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[20]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[21]  Leonardo Maria Reyneri,et al.  GA-Based Solutions Comparison for Storage Strategies Optimization for an Automated Warehouse , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[22]  V.J. Amuso,et al.  The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi- mission waveform design , 2007, 2007 International Waveform Diversity and Design Conference.

[23]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[24]  Luís M. S. Dias,et al.  A Genetic Algorithm for the Job Shop on an ASRS Warehouse , 2012, ICCSA.

[25]  Petter Krus,et al.  A Generic Model Concept for Optimisation in Preliminary Design , 1996 .

[26]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[27]  Valentina Colla,et al.  Simulation of an Automated Warehouse for Steel Tubes , 2008, Tenth International Conference on Computer Modeling and Simulation (uksim 2008).

[28]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[29]  Valentina Colla,et al.  GADF — Genetic Algorithms for distribution fitting , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[30]  Qiang Liu,et al.  A study on facility location–allocation problem in mixed environment of randomness and fuzziness , 2011, J. Intell. Manuf..

[31]  Marco Vannucci,et al.  Model Parameters Optimisation for an Industrial Application: A Comparison between Traditional Approaches and Genetic Algorithms , 2008, 2008 Second UKSIM European Symposium on Computer Modeling and Simulation.

[32]  Andrzej Osyczka,et al.  7 – Multicriteria optimization for engineering design , 1985 .

[33]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[34]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[35]  He Guo-guang,et al.  Optimal Number and Sites of Regional Logistics Centers by Genetic Algorithm and Fuzzy C-mean Clustering , 2007, 2007 International Conference on Service Systems and Service Management.

[36]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[37]  N. C. Hiremath,et al.  Multi objective outbound logistics network design for a manufacturing supply chain , 2013, J. Intell. Manuf..

[38]  Susmita Bandyopadhyay,et al.  Applying modified NSGA-II for bi-objective supply chain problem , 2013, J. Intell. Manuf..

[39]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..