An evolutionary multi-objective optimization system for earthworks

A novel system for dynamic optimization of earthworks is proposed.The system is based on an evolutionary multi-objective (cost-duration) approach.Results from several experiments using real-world data are presented.Results show the system is very competitive when compared to conventional design. Earthworks involve the leveling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

[1]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[2]  Krzysztof Schabowicz,et al.  Estimation of earthworks execution time cost by means of artificial neural networks , 2010 .

[3]  Jonathan Skolnik,et al.  Fuel Usage Factors in Highway and Bridge Construction , 2013 .

[4]  Ying cheng,et al.  Research on the optimization algorithm for machinery allocation of materials transportation based on evolutionary strategy , 2011 .

[5]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[6]  Chang Liu,et al.  Optimizing Earthmoving Job Planning Based on Evaluation of Temporary Haul Road Networks Design for Mass Earthworks Projects , 2015 .

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

[8]  A. Gomes Correia,et al.  Data mining applied to compaction of geomaterials , 2009 .

[9]  Manuel Parente,et al.  Artificial neural networks applied to an earthwork construction database , 2014, ICIT 2014.

[10]  Zhengyu Duan,et al.  Estimation of carbon dioxide emission in highway construction: a case study in southwest region of China , 2015 .

[11]  Piero P. Bonissone,et al.  Multicriteria decision making (mcdm): a framework for research and applications , 2009, IEEE Computational Intelligence Magazine.

[12]  Andrew S. Chang,et al.  Sustainable design indicators: Roadway project as an example , 2015 .

[13]  Liang Li,et al.  A Roadbed Earthwork Allocation Model Based on ACO Algorithm , 2010 .

[14]  Jonathan Jingsheng Shi,et al.  A neural network based system for predicting earthmoving production , 1999 .

[15]  Thomas Reinartz,et al.  CRISP-DM 1.0: Step-by-step data mining guide , 2000 .

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

[17]  Krzysztof Schabowicz,et al.  Application of artificial neural networks in predicting earthmoving machinery effectiveness ratios , 2008 .

[18]  Javad Sattarvand,et al.  Long term production planning of open pit mines by ant colony optimization , 2015, Eur. J. Oper. Res..

[19]  Olaf Mersmann,et al.  Multiple Criteria Optimization Algorithms and Related Functions , 2014 .

[20]  D. J. Edwards,et al.  Artificial intelligence approach to calculation of hydraulic excavator cycle time and output , 2000 .

[21]  Zahid Anwar,et al.  Data mining techniques and applications — A decade review , 2017, 2017 23rd International Conference on Automation and Computing (ICAC).

[22]  Turlough Guerin.  Root causes of fluid spills from earthmoving plant and equipment: Implications for reducing environmental and safety impacts , 2014 .

[23]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[24]  Robert Kliger,et al.  Knowledge reuse in the design of steel connections using 2D-CAD drawings , 2007, J. Inf. Technol. Constr..

[25]  Shu-Hsien Liao,et al.  Data mining techniques and applications - A decade review from 2000 to 2011 , 2012, Expert Syst. Appl..

[26]  Khaled Nassar,et al.  Solving the Least-Cost Route Cut and Fill Sequencing Problem Using Particle Swarm , 2012 .

[27]  Hong Zhang.  Multi-objective simulation-optimization for earthmoving operations , 2008 .

[28]  Osama Moselhi,et al.  Crew optimization in planning and control of earthmoving operations using spatial technologies , 2007, J. Inf. Technol. Constr..

[29]  Jui-Sheng Chou,et al.  Establishing expert system for prediction based on the project-oriented data warehouse , 2011, Expert Syst. Appl..

[30]  Nicola Beume,et al.  An EMO Algorithm Using the Hypervolume Measure as Selection Criterion , 2005, EMO.

[31]  Osama Moselhi,et al.  Selecting earthmoving equipment fleets using genetic algorithms , 2002, Proceedings of the Winter Simulation Conference.

[32]  Erkan Topal,et al.  A review of soft computing technology applications in several mining problems , 2014, Appl. Soft Comput..

[33]  C. M. Tam,et al.  Artificial neural networks model for predicting excavator productivity , 2002 .

[34]  Suresh Kataria,et al.  Ant Colony Optimization in Earthwork Allocation , 2005 .

[35]  Erhan Kozan,et al.  An integrated approach for earthwork allocation, sequencing and routing , 2014, Eur. J. Oper. Res..

[36]  Lawrence Mandow,et al.  Sindi: an intelligent assistant for highway design , 2004, Expert Syst. Appl..