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Extracting New Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling with Limited Buffer Spaces

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COGNITIVE COMPUTATION 2020年第1期12卷 195-205页

作者： Teymourifar, Aydin Ozturk, Gurkan Ozturk, Zehra Kamisli Bahadir, Ozan Eskisehir Tech Univ Fac Engn TR-26555 Eskisehir Turkey Eskisehir Tech Univ CIOL TR-26555 Eskisehir Turkey

Dispatching rules are among the most widely applied and practical methods for solving dynamic flexible job shop scheduling problems in manufacturing systems. Hence, the design of applicable and effective rules is always an important subject in the scheduling literature. The aim of this study is to propose a practical approach for extracting efficient rules for a more general type of dynamic job shop scheduling problem in which jobs arrive at the shop at different times and machine breakdowns occur stochastically. Limited-buffer conditions are also considered, increasing the problem complexity. Benchmarks are selected from the literature, with some modifications. gene expression programming combined with a simulation model is used for the design of scheduling policies. The extracted rules are compared with several classic dispatching rules from the literature based on a multi-objective function. The new rules are found to be superior to the classic ones. They are robust and can be used for similar complex scheduling problems. The results prove the efficiency of gene expression programming as a nature-inspired method for dispatching rule extraction.

关键词： Dynamic flexible job shop scheduling Dispatching rules Buffer conditions Simulation gene expression programming Nature-inspired approaches

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Physical and numerical modeling of performance of detention dams

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JOURNAL OF HYDROLOGY 2020年第0期581卷 121757-000页

作者： Parsaie, Abbas Azamathulla, Hazi Mohammad Haghiabi, Amir Hamzeh Lorestan Univ Dept Water Engn Khorramabad Iran Univ Tabuk Fac Engn Dept Civil Engn Tabuk 50060 Saudi Arabia

In this study, the trap efficiency (T-E) of detention dams was investigated using a laboratory study. To express the mathematical relationship between dependent and independent parameters, nonlinear regression and soft computing techniques including multivariate adaptive regression splines (MARS), gene expression programming (GEP) and group method of data handling (GMDH) were utilized. To find out the most effective parameters on TE, analyzing the Gamma Test (GT) and reviewing the structures of GMDH, MARS and GEP were performed. The accuracy of empirical formula derived based on the nonlinear regression with (R-2 = 0.65 and RMSE - 15.17) was inappropriate. Evaluating the performances of GMDH, GP and MARS with regarding calculation of standard error indices such as (R-2 and RMSE) indicated that all of them with minimum value of R-2 = 0.95 and maximum value of RMSE = 5.79 have suitable accuracy for modeling and predicting the TE. However, the MARS model is a bit more accurate than the others. Results of GT and reviewing the structure of prepared models (GMDH, MARS and GP) declared that ratio of volume of flood to volume of sediments (V-F/V-S), ratio of mean diameter of sediment size to the volume of flood (D-50/(3)root V-F) and specific gravity (G(s) = gamma(s)/gamma(w)) are the most effective parameters on T-E. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Watershed management Sediment transport Soft computing Sediment trap efficiency gene expression programming

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Experimental study and optimization of fracture properties of epoxy-based nano-composites: Effect of using nano-silica by GEP, RSM, DTM and PSO

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ENGINEERING FRACTURE MECHANICS 2020年 232卷 107047-107047页

作者： Dadrasi, A. Farzi, Gh A. Shariati, M. Fooladpanjeh, S. Parvaneh, V Islamic Azad Univ Dept Mech Engn Shahrood Branch Shahrood Iran Hakim Sabzevari Univ Dept Mat & Polymer Engn Sabzevar *** Iran Ferdowsi Univ Mashhad Fac Engn Dept Mech Engn Mashhad Razavi Khorasan Iran

The main purpose of this study was to examine, modelling and optimization the fracture toughness and the fracture energy of bisphenol-A epoxy resin reinforced by silica nano-particles. Three different approaches including gene expression programming (GEP), Response Surface Method (RSM) and, Decision Tree Method (DTM) have been employed to predict the effects of particle size and the weight fraction of nano-particles on the mentioned parameters. Three sizes of the nano-particles with the mean diameters of 17 nm, 25 nm and 65 nm up to 6 wt% have been used. The two general series of the nano-composites consisting of unimodal and bimodal particle size systems have been investigated. Experimental and modelling results showed that the Young's modulus, the fracture toughness and the fracture energy increased in all composites by the addition of the silica nano-particles and also by increasing the silica weight percent. In addition, it was observed that the particle size had no considerable effect on the properties. Mixed use of particles with different sizes in a composite also showed a negligible synergy effect on the Young's modulus and the fracture characteristics. The addition of these nano-particles did not have a significant effect on the yield strength of composites. Moreover, the best modelling approach is selected and optimized values resulted by Particle Swarm Optimization (PSO). The fracture surface was examined to understand the role of nanoparticles on toughening mechanisms by SEM.

关键词： Fracture toughness Silica nano-particles gene expression programming Response surface method Particle swarm optimization

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Predictive compressive strength models for green concrete

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INTERNATIONAL JOURNAL OF STRUCTURAL INTEGRITY 2020年第2期11卷 169-184页

作者： Murad, Yasmin Imam, Rana Abu Hajar, Husam Habeh, Dua'a Hammad, Abdullah Shawash, Zaid Univ Jordan Amman Jordan

Purpose The purpose of this paper is to develop new predictive models using gene expression programming in order to estimate the compressive strength of green concrete, as accurate models that can predict the compressive strength of green concrete are still lacking. Design/methodology/approach To estimate the compressive strength of plain concrete, fly ash concrete, silica fume concrete and concrete with silica fume and fly ash, four predictive GEP models are developed. The GEP models are developed using a large and reliable database that is collected from the literature. The GEP models are validated using the collected experimental database. Findings The R-2 is used to statistically evaluate the performance of the GEP models wherein the R-2 values for the GEP models including all data are 85, 95, 80 and 95.3 percent for the models that predict the compressive strength of plain concrete, fly ash concrete, silica fume concrete and concrete with silica fume and fly ash, respectively. Originality/value The GEP models have high R-2 values and low RMSE and MAE, which indicates that they are capable of predicting the compressive strength of green concrete with a reasonable accuracy.

关键词： Compressive strength Fly ash Green concrete gene expression programming Silica fume

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gene expression programming Algorithms for Optimization of Water Distribution Networks

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Procedia Engineering 2012年 37卷 359-364页

作者： Huanhuan Wang Shuming Liu Fanlin Meng Mingming Li School of Enviroment Tsinghua UniversityBeijing100084China

Water distribution system (WDS), as the important and expensive infrastructure, plays a crucial role in supplying water for the citizens living in the urban areas. The huge cost of the system drives researchers to seek the optimal cost design. During the last several decades, evolutionary methods such as genetic algorithms (GA), ant colony optimization algorithms (ACO) and simulated annealing (SA) have been found to explore the optimal combination of pipe diameters for design of WDS. However, in the case of complex distribution network, those methods suffer with low computational efficiency. In this paper, the gene expression programming (GEP) algorithms, a method based on the genetic algorithms, is adopted to solve the optimal design of WDS. This method is applied to the Hanoi benchmark network, the result of 6.081 million costs and 15,000 evaluations is combined with that obtained with GA and SA. From the results, it is observed that the proposed algorithm is an advanced alternative for the design of WDS, taking the computational efficiency and the ability of finding global or near-global optimal solutions into consideration.

关键词： Water distribution network water supply gene expression programming Optimal Design

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Monthly streamflow prediction using a hybrid stochastic-deterministic approach for parsimonious non-linear time series modeling

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2020年第1期14卷 1351-1372页

作者： Wang, Zhen Attar, Nasrin Fathollahzadeh Khalili, Keivan Behmanesh, Javad Band, Shahab S. Mosavi, Amir Chau, Kwok-wing Zhejiang Gongshang Univ Sch Business Adm Hangzhou 310018 Peoples R China Urmia Univ Water Engn Dept Orumiyeh Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Taiwan Tech Univ Dresden Fac Civil Engn Dresden Germany Norwegian Univ Life Sci Sch Econ & Business As Norway Obuda Univ Kando Kalman Fac Elect Engn Budapest Hungary Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

Accurate streamflow prediction is essential in reservoir management, flood control, and operation of irrigation networks. In this study, the deterministic and stochastic components of modeling are considered simultaneously. Two nonlinear time series models are developed based on autoregressive conditional heteroscedasticity and self-exciting threshold autoregressive methods integrated with the gene expression programming. The data of four stations from four different rivers from 1971 to 2010 are investigated. For examining the reliability and accuracy of the proposed hybrid models, three evaluation criteria, namely the R-2, RMSE, and MAE, and several visual plots were used. Performance comparison of the hybrid models revealed that the accuracy of the SETAR-type models in terms of R-2 performed better than the ARCH-type models for Daryan (0.99), Germezigol (0.99), Ligvan (0.97), and Saeedabad (0.98) at the validation stage. Overall, prediction results showed that a combination of the SETAR with the GEP model performs better than ARCH-based GEP models for the prediction of the monthly streamflow.

关键词： Integrated hybrid models nonlinear time series models streamflow modeling gene expression programming Urmia Lake basin stochastic and deterministic

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RETRACTED: Modeling Ductile-to-Brittle Transition Temperature of Functionally Graded Steels by gene expression programming (Retracted article. See vol. 29, pg. 202, 2020)

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2012年第4期21卷 465-492页

作者： Milani, Amir Ali Nazari, Ali Islamic Azad Univ Dept Mat Sci & Engn Saveh Branch Saveh Iran

In this article, a model based on gene expression programming for predicting ductile-to-brittle transition temperature of functionally graded steels in both crack divider and crack arrester configurations has been presented. Functionally, graded steels containing graded ferritic and austenitic regions together with bainite and martensite intermediate layers were produced by electroslag remelting. For the purpose of building the model, training and testing using experimental results from 140 specimens produced from two basic composites were conducted. The data used as input in gene expression programming models are arranged in a format of six parameters that cover the FGS type, the crack tip configuration, the thickness of graded ferritic region, the thickness of graded austenitic region, the distance of the notch from bainite or martensite intermediate layer, and temperature. According to these input parameters, in the gene expression programming, the ductile-to-brittle transition temperature of each FGS specimen was predicted. The training and testing results in the gene expression programming model have shown strong potential for predicting the ductile-to-brittle transition temperature of each FGS specimens.

关键词： crack arrester crack divider DBTT ESR functionally graded steels gene expression programming

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RETRACTED: Prediction of Compressive Strength of Geopolymers with Seeded Fly Ash and Rice Husk-Bark Ash by gene expression programming (Retracted article. See vol. 29, pg. 202, 2020)

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2012年第8期21卷 1202-1226页

作者： Nazari, Ali Riahi, Shadi Khalaj, Gholamreza Bohlooli, Hamid Kaykha, Mohammad Mehdi Islamic Azad Univ Dept Mech Engn Birjand Branch Birjand Iran Univ Zabol Dept Mech Engn Zabol Iran

In the present work, compressive strength of inorganic polymers (geopolymers) made from seeded fly ash and rice husk-bark ash has been predicted by gene expression programming. Different specimens were subjected to compressive strength tests at 7 and 28 days of curing. One set of the specimens were cured at room temperature until reaching to 7 and 28 days and the other sets were oven cured for 36 h at the range of 40-90 degrees C and then room cured until 7 and 28 days. A model based on gene expression programming for predicting the compressive strength of the specimens was presented. To build the model, training and testing using experimental results from 120 specimens were conducted. According to the input parameters, in the gene expression programming models, the compressive strength of each specimen was predicted. The training and testing results in the gene expression programming models have shown a strong potential for predicting the compressive strength of the geopolymer specimens.

关键词： compressive strength fly ash FTIR gene expression programming geopolymer particle size rice husk-bark ash seeded mixture

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Shape Optimization of Marine Propeller Based On P-System Diffusion Algorithm and Approximate Model Technology 2021

Shape Optimization of Marine Propeller Based On P-System Dif...

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2021 International Conference on Mechanical, Aerospace and Automotive Engineering

作者： JunJun Wu Long Zheng ShunHuai Chen Hao Wang Wuhan Rules & Research Institute CCS China Wuhan University of Technology China

ISBN: (纸本)9781450385206

Based on a novel optimization algorithm called Diffusion Algorithm Based on P Systems (DAPS) and a new type of approximate called gene expression programming (GEP), we present an optimization method of marine propeller. In this work, the hydrodynamic performance of the marine propeller under different geometrical and physical conditions are evaluated by GEP, the final model is found to be of reasonable accuracy between observed and GEP-predicted values. On the basis of obtaining an approximate model with good response, we use the DAPS algorithm to optimize the shape of the propeller, the profile shape of the propeller is considered as an optimization variable. Diffusion Algorithm Based on P Systems (DAPS) is a novel optimization algorithm, this algorithm utilizes four different search strategies and a P system to explore the search space and find the global minima, through the benchmark functions test, DAPS is highly competitive and can provide better solution. Through calculation verification, it can be seen that the final GEP model has a good prediction of the propeller hydrodynamic performance, and the proposed optimization method can improve the open water efficiency of the propeller.

关键词： Optimization Diffusion Algorithm Based on P Systems Propeller gene expression programming

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The Research on Evolution Schema Theorem on gene expression programming

The Research on Evolution Schema Theorem on Gene Expression ...

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International Conference on Emerging Computation and Information Technologies for Education (ECICE 2012)

作者： Cheng, Huifang Xue, Jingshun Hebei Univ Engn Sch Informat & Elect Engn Handan Peoples R China Xingtai Coll Elementary Educ Coll Xingtai Peoples R China

ISBN: (纸本)9783642284656

gene expression programming (GEP) from the proposed date, no complete theoretical system, a serious impediment to the development of GEP. To solve this problem, first studied in depth from a theoretical calculation model GEP: GEP gene model is defined and related concepts, the use of probabilistic methods detailed analysis of the single-gene GEP application examples in the evolution of the role of each operator, based on an analysis the results derived GEP schema theorem, proved by experiment GEP schema theorem is correct. GEP model theorem proposed improvements for the GEP algorithm provides quantitative assessment basis.

关键词： gene expression programming GEP model GEP schema theorem

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