A System Learning User Preferences for Multiobjective Optimization of Facility Layouts

A multiobjective optimization system based on both subjective and objective information for assisting facility layout design is proposed on this contribution. A data set is constructed based on the expert evaluation of some facility layouts generated by an interactive genetic algorithm. This dataset is used for training a classification algorithm which produces a model of user subjective preferences over the layout designs. The evaluation model obtained is integrated into a multi-objective optimization algorithm as an objective together with reducing material flow cost. In this way, the algorithm exploits the search space in order to obtain a satisfactory set of plant layouts. The proposal is applied on a design problem case where the classification algorithm demonstrated that it could fairly learn the user preferences, as the model obtained worked well guiding the search and finding good solutions, which are better in term of user evaluation with almost the same material flow cost.

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

[2]  Marc Parizeau,et al.  DEAP: a python framework for evolutionary algorithms , 2012, GECCO '12.

[3]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[4]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[5]  Genaro J. Gutierrez,et al.  Algorithms for robust single and multiple period layout planning for manufacturing systems , 1992 .

[6]  G. Galante,et al.  A multi-objective approach to facility layout problem by genetic search algorithm and Electre method , 2006 .

[7]  Xiaoming Zhang,et al.  Kernel Discriminant Learning for Ordinal Regression , 2010, IEEE Transactions on Knowledge and Data Engineering.

[8]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[9]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[10]  Norman Y. Foo,et al.  PRICAI'96: Topics in Artificial Intelligence , 1996, Lecture Notes in Computer Science.

[11]  Alice E. Smith,et al.  Unequal-area facility layout by genetic search , 1995 .

[12]  Geoffrey I. Webb.  Cost-Sensitive Specialization , 1996, PRICAI.

[13]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[14]  Eibe Frank,et al.  A Simple Approach to Ordinal Classification , 2001, ECML.

[15]  Hideyuki Takagi,et al.  User Fatigue Reduction by an Absolute Rating Data-trained Predictor in IEC , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[16]  R. Likert.  “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

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

[18]  Lorenzo Salas Morera,et al.  An Interactive Genetic Algorithm for the Unequal Area Facility Layout Problem , 2011 .

[19]  Ling Li,et al.  Ordinal Regression by Extended Binary Classification , 2006, NIPS.

[20]  Meghna Babbar-Sebens,et al.  Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design , 2012, Appl. Soft Comput..

[21]  Aboul Ella Hassanien,et al.  Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, 6-8 April, 2011, Salamanca, Spain , 2011, SOCO.

[22]  Ashutosh Tiwari,et al.  An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization , 2007, Comput. Ind..

[23]  Giuseppe Aiello,et al.  A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding , 2012, Expert Syst. Appl..

[24]  Elwood S. Buffa,et al.  A Heuristic Algorithm and Simulation Approach to Relative Location of Facilities , 1963 .

[25]  Xavier Llorà,et al.  Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness , 2005, GECCO '05.