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A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates

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JOURNAL OF INTELLIGENT MANUFACTURING 2013年第4期24卷 763-774页

作者： Nie, Li Gao, Liang Li, Peigen Li, Xinyu Shanghai Second Polytech Univ Sch Mech & Elect Engn Shanghai 201209 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Flexible job shop scheduling problem (FJSSP) is generalization of job shop scheduling problem (JSSP), in which an operation may be processed on more than one machine each of which has the same function. Most previous researches on FJSSP assumed that all jobs to be processed are available at the beginning of scheduling horizon. The assumption, however, is always violated in practical industries because jobs usually arrive over time and can not be predicted before their arrivals. In the paper, dynamic flexible job shop scheduling problem (DFJSSP) with job release dates is studied. A heuristic is proposed to implement reactive scheduling for the dynamic scheduling problem. An approach based on gene expression programming (GEP) is also proposed which automatically constructs reactive scheduling policies for the dynamic scheduling. In order to evaluate the performance of the reactive scheduling policies constructed by the proposed GEP-based approach under a variety of processing conditions three factors, such as the shop utilization, due date tightness, problem flexibility, are considered in the simulation experiments. The scheduling performance measure considered in the simulation is the minimization of makespan, mean flowtime and mean tardiness, respectively. The results show that GEP-based approach can construct more efficient reactive scheduling policies for DFJSSP with job release dates under a big range of processing conditions and performance measures in the comparison with previous approaches.

关键词： Reactive scheduling Dynamic scheduling Flexible job shop scheduling Job release dates gene expression programming

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Development of Corresponding States Model for Estimation of the Surface Tension of Chemical Compounds

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AICHE JOURNAL 2013年第2期59卷 613-621页

作者： Gharagheizi, Farhad Eslamimanesh, Ali Sattari, Mehdi Mohammadi, Amir H. Richon, Dominique Islamic Azad Univ Dept Chem Engn Buinzahra Branch Buinzahra Iran MINES ParisTech CEP TEP Ctr Energet & Procedes F-77305 Fontainebleau France Saman Energy Giti Co Tehran *** Iran Univ KwaZulu Natal Thermodynam Res Unit Sch Chem Engn ZA-4041 Durban South Africa Tech Univ Denmark CERE Dept Chem & Biochem Engn DK-2800 Lyngby Denmark

The gene expression programming (GEP) strategy is applied for presenting two corresponding states models to represent/predict the surface tension of about 1,700 compounds (mostly organic) from 75 chemical families at various temperatures collected from the DIPPR 801 database. The models parameters include critical temperature or temperature/critical volume/acentric factor/critical pressure/reduced temperature/reduced normal boiling point temperature/molecular weight of the compounds. Around 1,300 surface tension data of 118 random compounds are used for developing the first model (a four-parameter model) and about 20,000 data related to around 1,600 compounds are applied for checking its prediction capability. For the second one (a five-parameter model), about 10,000 random data are applied for its development, and 11,000 data are used for testing its prediction ability. The statistical parameters including average absolute relative deviations of the results form dataset values (25 and 18% for the first and second models, respectively) demonstrate the accuracy of the presented models. (C) 2012 American Institute of Chemical Engineers AIChE J, 59: 613-621, 2013

关键词： physicochemical properties gene expression programming corresponding states accurate model optimization surface tension

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Reactive scheduling in a job shop where jobs arrive over time

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第2期66卷 389-405页

作者： Nie, Li Gao, Liang Li, Peigen Shao, Xinyu Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Shanghai Second Polytech Univ Sch Mech & Elect Engn Shanghai 201209 Peoples R China

The paper considers the dynamic job shop scheduling problem (DJSSP) with job release dates which arises widely in practical production systems. The principle characteristic of DJSSP considered in the paper is that the jobs arrive continuously in time and the attributes of the jobs, such as the release dates, routings and processing times are not known in advance, whereas in the classical job shop scheduling problem (CJSSP), it is assumed that all jobs to be processed are available at the beginning of the scheduling process. Reactive scheduling approach is one of the effective approaches for DJSSP. In the paper, a heuristic is proposed to implement the reactive scheduling of the jobs in the dynamic production environment. The proposed heuristic decomposes the original scheduling problem into a number of sub problems. Each sub problem, in fact, is a dynamic single machine scheduling problem with job release dates. The scheduling technique applied in the proposed heuristic is priority scheduling, which determines the next state of the system based on priority values of certain system elements. The system elements are prioritized with the help of scheduling rules (SRs). An approach based on gene expression programming (GEP) is also proposed in the paper to construct efficient SRs for DJSSP. The rules constructed by GEP are evaluated in the comparison of the rules constructed by GP and several prominent human made rules selected from literatures on extensive problem sets with respect to various measures of performance. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Dynamic job shop scheduling gene expression programming Reactive scheduling Heuristic Scheduling rule

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Modeling rainfall-runoff process using soft computing techniques

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COMPUTERS & GEOSCIENCES 2013年 51卷 108-117页

作者： Kisi, Ozgur Shiri, Jalal Tombul, Mustafa Canik Basari Univ Fac Engn & Architecture Dept Civil Engn Samsun Turkey Anadolu Univ Fac Engn Dept Civil Engn Eskisehir Turkey

Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987-1991) of measurements of independent variables of rainfall and runoff values. The models used in the study were Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and gene expression programming (GEP) which are Artificial Intelligence (AI) approaches. The applied models were trained and tested using various combinations of the independent variables. The goodness of fit for the model was evaluated in terms of the coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and scatter index (SI). A comparison was also made between these models and traditional Multi Linear Regression (MLR) model. The study provides evidence that GEP (with RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R-2=0.978) is capable of modeling rainfall-runoff process and is a viable alternative to other applied artificial intelligence and MLR time-series methods. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Rainfall-runoff process Neural networks Neuro-fuzzy system gene expression programming

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Target Position Estimation by genetic expression programming for Mobile Robots With Vision Sensors

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2013年第12期62卷 3218-3230页

作者： Wu, Chih-Hung Lin, I-Sheng Wei, Ming-Liang Cheng, Tain-Yu Natl Univ Kaohsiung Dept Elect Engn Kaohsiung 811 Taiwan

Target location is an important task in robotics applications. For different application purposes, the positions of targets are usually described by various coordinate systems. Closed-form formulas that describe the relationships between two coordinate systems serve as a means for coordinate transformation. However, the existence of unavoidable measurement errors and uncertainty makes closed-form formulas less reliable. Besides, the closed-form formulas usually involve operations of matrix inversion and transpose that usually consume a considerable amount of computing resources. This paper defines the problem of coordinate transformation on mobile robots as a regression problem and employs the techniques of gene expression programming to discover the regression models. With such regression models, coordinate transformation can be done by simpler formulas with lower processing costs. The proposed techniques have been implemented and integrated with a four-wheeled robot equipped with vision sensors and have been verified in real environments. The experiments demonstrate the effectiveness and performance of the proposed method. To the best of our knowledge, this is the first study on the underlying problem using genetic-based techniques.

关键词： Computational intelligence coordinate transformation gene expression programming genetic algorithm measurement errors regression robotics symbolic regression

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Modeling the mean grain size of synthesized nanopowders produced by mechanical alloying

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CERAMICS INTERNATIONAL 2013年第2期39卷 1587-1596页

作者： Nazari, Ali Zakeri, Mohammad Islamic Azad Univ Saveh Branch Dept Mat Sci Saveh Iran Mat & Energy Res Ctr Ceram Dept Karaj Iran

gene expression programming (GEP) optimization tool has been utilized to predict the mean grain size of nanopowders synthesized by mechanical alloying. 86 data were collected from the literature, randomly divided into 65 and 21 sets and then, respectively, were trained and tested by 11 different GEP models. The differences between the models were in their linking functions (addition and multiplication) and sub expression trees (3, 4, 5, 6, 7 and 8). The method of calculation of the mean grain size, milling time, annealing temperature, produced phases after mechanical alloying, vial speed and ball to powder ratio were considered as input variables to predict mean grain size of nanopowders as output. The obtained results from training and testing of the different models showed that some of them are capable to predict mean grain size of the synthesized nanopowders in the considered range. However, the best results were obtained by using 7 sub expression trees addition as linking function. R-2 value of the trained and tested suggested model showed this situation. (C) 2012 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

关键词： gene expression programming Mechanical alloying Nanopowder Modeling

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An evolutionary approach for modeling of shear strength of RC deep beams

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MATERIALS AND STRUCTURES 2013年第12期46卷 2109-2119页

作者： Gandomi, Amir Hossein Yun, Gun Jin Alavi, Amir Hossein Univ Akron Dept Civil Engn Akron OH 44325 USA Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

In this study, a new variant of genetic programming, namely gene expression programming (GEP) is utilized to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters. The model was developed using 214 experimental test results obtained from previously published papers. A comparative study was conducted between the results obtained by the proposed model and those of the American Concrete Institute (ACI) and Canadian Standard Association (CSA) models, as well as an Artificial Neural Network (ANN)-based model. A subsequent parametric analysis was carried out and the trends of the results were confirmed via some previous laboratory studies. The results indicate that the GEP model gives precise estimations of the shear strength of RC deep beams. The prediction performance of the model is significantly better than the ACI and CSA models and has a very good agreement with the ANN results. The derived design equation provides a valuable analysis tool accessible to practicing engineers.

关键词： Shear strength RC deep beams gene expression programming

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Chromium carbonitride coating produced on DIN 1.2210 steel by thermo-reactive deposition technique: Thermodynamics, kinetics and modeling

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SURFACE & COATINGS TECHNOLOGY 2013年 225卷 1-10页

作者： Khalaj, Gholamreza Nazari, Ali Khoie, Seyyed Mohammad Mousavi Khalaj, Mohammad Javad Pouraliakbar, Hesam Islamic Azad Univ Dept Engn Saveh Branch Saveh Iran WT SRC Modeling & Simulat Dept Tehran Iran Amirkabir Univ Technol Dept Min & Met Tehran Iran WT SRC Adv Mat Dept Tehran Iran Sharif Univ Technol Dept Mat Sci & Engn Tehran Iran

A duplex surface treatment on DIN 1.2210 steel has been developed involving nitriding and followed by chromium thermo-reactive deposition (TRD) techniques. The TRD process was performed in molten salt bath at 550, 625 and 700 degrees C for 1-14 h. The process formed a thickness up to 9.5 mu m of chromium carbonitride coatings on a hardened diffusion zone. Characterization of the coatings by means of scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD) indicates that the compact and dense coatings mainly consist of Cr(C,N) and Cr-2(C,N) phase. All the growth processes of the chromium carbonitride obtained by TRD technique followed a parabolic kinetics. Activation energy (Q) for the process was estimated to be 185.6 kJ/mol of chromium carbonitride coating. A model based on genetic programming for predicting the layer thickness of duplex coating of the specimens has been presented. To construct the model, training and testing was conducted by using experimental results from 82 specimens. The data used as inputs in genetic programming models were five independent parameters consisting of the pre-nitriding time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. The training and testing results in genetic programming models illustrated a strong capability for predicting the layer thickness of duplex coating. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Duplex surface treatment Diffusion coatings TRD gene expression programming

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A typology of different development and testing options for symbolic regression modelling of measured and calculated datasets

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ENVIRONMENTAL MODELLING & SOFTWARE 2013年 47卷 29-41页

作者： Beriro, Darren J. Abrahart, Robert J. Nathanail, C. Paul Moreno, Jimmy Bawazir, A. Salim Univ Nottingham Sch Geog Nottingham NG7 2RD England New Mexico State Univ Dept Civil Engn Las Cruces NM 88003 USA

Data-driven modelling is used to develop two alternative types of predictive environmental model: a simulator, a model of a real-world process developed from either a conceptual understanding of physical relations and/or using measured records, and an emulator, an imitator of some other model developed on predicted outputs calculated by that source model. A simple four-way typology called Emulation Simulation Typology (EST) is proposed that distinguishes between (i) model type and (ii) different uses of model development period and model test period datasets. To address the question of to what extent simulator and emulator solutions might be considered interchangeable i.e. provide similar levels of output accuracy when tested on data different from that used in their development, a pair of counterpart pan evaporation models was created using symbolic regression. Each model type delivered similar levels of predictive skill to that other of published solutions. Input output sensitivity analysis of the two different model types likewise confirmed two very similar underlying response functions. This study demonstrates that the type and quality of data on which a model is tested, has a greater influence on model accuracy assessment, than the type and quality of data on which a model is developed, providing that the development record is sufficiently representative of the conceptual underpinnings of the system being examined. Thus, previously reported substantial disparities occurring in goodness-of-fit statistics for pan evaporation models are most likely explained by the use of either measured or calculated data to test particular models, where lower scores do not necessarily represent major deficiencies in the solution itself. (c) 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

关键词： Simulation Emulation Pan evaporation gene expression programming Data driven modelling Emulation simulation typology Symbolic regression

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Development of a general Model for Determination of Thermal Conductivity of Liquid Chemical Compounds at Atmospheric Pressure

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AICHE JOURNAL 2013年第5期59卷 1702-1708页

作者： Gharagheizi, Farhad Ilani-Kashkouli, Poorandokht Sattari, Mehdi Mohammadi, Amir H. Ramjugernath, Deresh Richon, Dominique Univ KwaZulu Natal Sch Engn Thermodynam Res Unit ZA-4041 Durban South Africa MINES ParisTech CEP TEP Ctr Energet & Proc F-77305 Fontainebleau France Tech Univ Denmark Ctr Energy Resources Engn CERE Dept Chem & Biochem Engn DK-2800 Lyngby Denmark

In this communication, a general model for representation/presentation of the liquid thermal conductivity of chemical compounds (mostly organic) at 1 atm pressure for temperatures below normal boiling point and at saturation pressure for temperatures above the normal boiling point is developed using the gene expression programming algorithm. Approximately 19,000 liquid thermal conductivity data at different temperatures related to 1636 chemical compounds collected from the DIPPR 801 database are used to obtain the model as well as to assess its predictive capability. The parameters of the model comprise temperature, acentric factor, critical pressure, normal boiling temperature, and molecular weight. Nearly 80% of the data set (15,221 data) is randomly assigned to develop the model equation, 10% of the data set (1902 data) is used to validate the model, and the remaining data (1902 data) were implemented to evaluate its predictive power. The average absolute relative deviation of the model results from the DIPPR 801 data is less than 9%. In terms of simplicity and wide range of applicability, this empirical model shows acceptable accuracy. (C) 2012 American Institute of Chemical Engineers AIChE J, 59: 1702-1708, 2013

关键词： gene expression programming liquid thermal conductivity corresponding states reliable model prediction

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