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Inventory model with fuzzy lead-time and dynamic demand over finite time horizon using a multi-objective genetic algorithm

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2012年第6期64卷 1822-1838页

作者： Bera, U. K. Maiti, M. K. Maiti, M. Natl Inst Technol Dept Math Jirania 799055 West Tripura India Mahishadal Raj Coll Dept Math Purba Medinipur 721628 WB India Vidyasagar Univ Dept Math Paschim Medinipur 700114 WB India

The real-world inventory control problems are normally imprecisely defined and human interventions are often required in solving these decision-making problems. In this paper, a realistic inventory problem with an infinite rate of replenishment over a prescribed finite but imprecise time horizon is formulated considering time dependent ramp type demand, which increases with time. Lead time is also assumed as fuzzy in nature. Shortages are allowed and backlogged partially. Two models are considered depending upon the ordering policies of the decision maker (DM). The imprecise parameters are first transformed to corresponding nearest interval numbers depending upon some distance metric on fuzzy numbers and then following the interval mathematics, the objective function for total profit from the planning horizon is obtained (which is an interval function). Then interval objective decision making problem is reduced to multi-objective problems using different approaches. Finally a fast and elitist multi-objective genetic algorithm (FEMOGA) is used for solving these multi-objective models to find pareto-optimal decisions for the DM. The models are illustrated numerically. As a particular case, the results due to linear trended and constant demands have been presented. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Fuzzy lead-time Interval number multi-objective genetic algorithm

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Integration of life cycle assessment, artificial neural networks, and metaheuristic optimization algorithms for optimization of tomato-based cropping systems in Iran

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INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT 2020年第3期25卷 620-632页

作者： Pishgar-Komleh, Seyyed Hassan Akram, Asadollah Keyhani, Alireza Sefeedpari, Paria Shine, Philip Brandao, Miguel Univ Tehran Fac Agr Engn & Technol Dept Agr Machinery POB 4111 Karaj Iran State Res Inst Inst Soil Sci & Plant Cultivat Dept Bioecon & Syst Anal Pulawy Poland Czech Univ Life Sci Fac Econ & Management Dept Econ Prague Czech Republic Cork Inst Technol Dept Proc Energy & Transport Engn Cork Ireland KTH Royal Inst Technol Dept Sustainable Dev Environm Sci & Engn Seed Div Sustainabil Assessment & Management S-10044 Stokholm Sweden

Purpose The main purpose of this study was to evaluate the use of an integrated life cycle assessment (LCA), artificial neural network, and metaheuristic optimization model to improve the sustainability of tomato-based cropping systems in Iran. The model outputs the combination of input usage in a tomato cropping system, which leads to the highest economic output and the least environmental impact. Methods The LCA inventory was created using data from 114 open-field tomato farms in the Alborz Province of Iran during one growing period in 2015. Among all management components, the main focus was on irrigation management systems. The optimization problem was designed by integrating three indicators: carbon footprint (CF), benefit-cost ratio (BCR), and energy use efficiency (EUE) as the objective of field tomato production. The functional unit was 1 kg of tomato aligned with the system boundary of the cradle to market life cycle. Three artificial neural networks (ANNs) were applied to model relationships between the inputs and three indices (CF, BCR, and EUE) as the objective functions. multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) were used to minimize the CF and maximize the BCR and EUE indicators. The abovementioned aims have been pursued by developing codes in MATLAB software. Results and discussion CF, BCR, and EUE were calculated to be 0.26 kg CO2-eq (kg tomato)(-1), 1.8, and 0.5, respectively. MOGA results envisage the possibility of an increase of 86% and 50% in the EUE and BCR and a 43% reduction in the CF of tomato production systems. Moreover, EUE and BCR increased by 83% and 49%, and CF was reduced by 39% from the optimum results obtained from the MOPSO algorithm. It was revealed that in order to optimize field tomato production with the target objectives of this study, a large additional use for irrigation pipes, plastic, and machinery in comparison to current situation is required, while a large reduction

关键词： Artificial neural networks Benefit-cost ratio Carbon footprint Energy use efficiency Field tomato Iran Life cycle assessment multi-objective genetic algorithm multi-objective particle swarm optimization

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A new accessibility based team orienteering approach for urban tourism routes optimization (A Real Life Case)

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COMPUTERS & OPERATIONS RESEARCH 2022年 138卷 105620-105620页

作者： Heris, Fateme Sadat Moosavi Ghannadpour, Seyed Farid Bagheri, Morteza Zandieh, Fatemeh Iran Univ Sci & Technol Dept Ind Engn Tehran *** Iran Iran Univ Sci & Technol Sch Railway Engn Tehran *** Iran

One of the most crucial issues in the tourism industry that can help attract more tourists is to design optimal travel routes for tourists. The Team Orienteering Problem with Time Windows (TOPTW) is one of the approaches that can solve Tourist Trip Design Problem (TTDP). In this study, TOPTW has been applied to design a multiobjective model to determine the optimal travel routes, considering the accessibility. The present study aims 1) to maximize the profit from visiting the Points of Interest (POIs) to increase the satisfaction level of the travel agency, and 2) to maximize the number of visited POIs to improve the satisfaction level of the tourists. Two concepts of "indirect coverage" and "neighborhood radius" are applied to increase the accessibility of the tourist attractions and POIs. Thus, around each POI, a small radius and a large radius are considered, and if the points are in the radius coverage of the points located in the main routes, they can receive indirect coverage from those points. Also the time-window overlap issue for POIs is vital to determine the possibility of indirect coverage and makes the conceptual model of the research closer to reality. Also, several indicators are proposed for calculating accessibility. A multi-objective genetic algorithm (MOGA) is formulated to solve the studied problem, and the results obtained by the proposed algorithm are compared with those of CPLEX software (for small-scale) and NSGA-II algorithm (for large-scale). The findings indicate that the genetic algorithm outperforms the NSGA-II algorithm. A sensitivity analysis is also performed to evaluate the effects of the proposed model parameters on the optimization process. Finally, the model applicability in practice is examined based on a case study in Tehran, the capital of Iran.

关键词： Tourism Team orienteering problem with time windows Profit Accessibility Indirect coverage multi-objective genetic algorithm

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Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm

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NEURAL COMPUTING & APPLICATIONS 2012年第6期21卷 1281-1295页

作者： Loghmanian, Sayed Mohammad Reza Jamaluddin, Hishamuddin Ahmad, Robiah Yusof, Rubiyah Khalid, Marzuki Univ Teknol Malaysia Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Fac Mech Engn Dept Appl Mech Johor Baharu 81310 Malaysia

The problem of constructing an adequate and parsimonious neural network topology for modeling non-linear dynamic system is studied and investigated. Neural networks have been shown to perform function approximation and represent dynamic systems. The network structures are usually guessed or selected in accordance with the designer's prior knowledge. However, the multiplicity of the model parameters makes it troublesome to get an optimum structure. In this paper, an alternative algorithm based on a multi-objective optimization algorithm is proposed. The developed neural network model should fulfil two criteria or objectives namely good predictive accuracy and minimum model structure. The result shows that the proposed algorithm is able to identify simulated examples correctly, and identifies the adequate model for real process data based on a set of solutions called the Pareto optimal set, from which the best network can be selected.

关键词： Artificial neural network multi-objective genetic algorithm NSGA-II System identification Model structure selection

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Integration of a fuzzy neural network and multi-objective genetic algorithm for optimisation of BLU light guide plate injection moulding parameters

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INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY 2012年第1-4期45卷 83-95页

作者： Wang, Shen-Tsu Natl Pingtung Inst Commerce Commerce Automat & Management Dept Pingtung 900 Taiwan

Backlight module unit (BLU) is a key component of LCD, and thinning of the BLU light guide plate is the focus of development. Most light guide plates are manufactured using the injection moulding process. The experimental design in this study is the most common research approach applied in the setting of light guide plate parameters. However, the parameter interaction and system non-linearity can easily result in prediction errors of parameters. Meanwhile, since the optimal moulding conditions have not been achieved, the best effects of display illumination and brightness cannot be achieved. Hence, this study first applied the grey relational sorting method to determine the major control factors influencing the BLU light guide plate, and then built the approximate model of a fuzzy neural network, according to experimental planning samples, before using the multi-objective genetic algorithm to determine the optimal manufacturing parameter design. The results suggested that the process capability index C-pk value of the light guide plate X-axis size is improved from 0.43 to 1.56, and has stabilised product quality and enhanced process capability. This study proved that the prediction model of process optimisation, as obtained by using this research method, is effective.

关键词： fuzzy neural network multi-objective genetic algorithm process capability index BLU light guide plate

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multi-objective optimization design for the blade angles of hydraulic torque converter with adjustable pump

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第13期234卷 2523-2536页

作者： Zheng, Zhi-Ying Liu, Quan-Zhong Deng, Yong-Kang Li, Biao Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China

To improve the efficiency of a hydraulic torque converter with adjustable pump at low load and thus increase the operation scope of high efficiency, multi-objective optimization design is carried out for the blade angles by incorporating three-dimensional steady computational fluid dynamics numerical simulation, design of experiments, Kriging surrogate model and multi-objective genetic algorithm. The results show that the angle of blade trailing edge in first-stage stator is the main influencing factor of the efficiency of hydraulic torque converter with adjustable pump. All the peak efficiencies of hydraulic torque converter with adjustable pump at three openings of the pump are improved after optimization, and the increased extent increases with decreasing opening of the pump. The operation scope of high efficiency consequently increases from 2.46 to 2.67. Besides, the improvement for the efficiency of hydraulic torque converter with adjustable pump is achieved by increasing the efficiency of the pump. The increase of angle of blade trailing edge in first-stage stator and the decrease of angle of blade leading edge in second-stage turbine after optimization induce the positive angle of attack at the inlet of second-stage turbine, thus realizing the performance optimization of hydraulic torque converter with adjustable pump. This also explains the increased proportion of the torque of second-stage turbine at larger speed ratios after optimization and the fact that the angle of blade trailing edge in first-stage stator is the main influencing factor of the efficiency of hydraulic torque converter with adjustable pump. The established multi-objective optimization method provides a reference solution for the optimization design of blade angles and for the improvement of integrated efficiency of hydraulic torque converter.

关键词： Hydraulic torque converter adjustable pump design of experiments Kriging surrogate model multi-objective genetic algorithm CFD numerical simulation

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Application of entransy theory on structure optimization of serrated fin in plate-fin heat exchanger

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APPLIED THERMAL ENGINEERING 2020年 173卷 114809-114809页

作者： Li, Ke Wen, Jian Liu, Yuce Liu, Huaqing Wang, Simin Tu, Jiyuan Xi An Jiao Tong Univ Sch Energy & Power Engn Dept Refrigerat & Cryogen Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Dept Proc Equipment & Control Engn Xian 710049 Peoples R China Tsinghua Univ Inst Nucl & New Energy Technol Key Lab Adv Reactor Engn & Safety Minist Educ Beijing 100084 Peoples R China

The effects of geometrical parameters of serrated fin in plate-fin heat exchanger (PFHE) on entransy-dissipationbased thermal resistance (EDTR) caused by heat transfer (R-ht) and fluid friction (R-ff ) are investigated by design of experiment(DOE) and response surface methodology (RSM) in this paper. The results show that the fin height, fin interrupted length and fin space are positively correlated with R-ht, while the fin thickness and PFHE length are negatively correlated with R-ht. R-ff is far larger than R-ht, for about an order of magnitude, and R-ff increases almost linearly with the increase of Re when geometric parameters are fixed. R-ff increases with fin thickness and PFHE length, and decreases with fin height, fin interrupted length and fin space. Furthermore, multi-objective genetic algorithm (MOGA) is carried out. Compared with the original design, R-ht of optimal structure 1 and 2 obtained based on two objectives of minimizing R-ff and minimizing R-ht reduces by 27.9% and 41.2%, respectively, and R-ff reduces by 65.3% and 55.8% respectively. Compared with the optimal results based on traditional objective functions (maximize j and minimize f), R-ht and R-ff obtained based on EDTR objective functions reduce by an average of 37.6% and 30.3% respectively, and the JF factor of EDTR optimal results increases by an average of 10.1%.The results show that the optimization results based on EDTR objective functions are better than that based on traditional objective functions for serrated fin in PFHE.

关键词： Plate-fin heat exchanger Serrated fin Response surface multi-objective genetic algorithm Entransy-dissipation-based thermal resistance

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Sounding rocket SS-520 as a CubeSat launch vehicle

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ACTA ASTRONAUTICA 2020年 170卷 206-223页

作者： Ito, T. Yamamoto, T. Nakamura, T. Habu, H. Ohtsuka, H. Japan Aerosp Explorat Agcy Chuo Ku 3-1-1 Yoshinodai Sagamihara Kanagawa 2525210 Japan IHI AEROSP Co Ltd 900 Fujiki Tomioka Gunma 3702398 Japan

This paper investigates the launch capability of the SS-520 as a CubeSat launch vehicle. The SS-520 was developed by JAXA originally as a two-stage, spin-stabilized, solid-propellant sounding rocket. With less than 2.6 tons in total mass and 10 m in length, the SS-520-5 successfully launched a single 3U-sized CubeSat into orbit on February 3, 2018. The SS-520-5 obtained its capability as a CubeSat launch vehicle by installing a 3rd stage solid motor in addition to the RCS between the 1st and 2nd stages. However, its launch capability was limited due to its rocket system configuration. In order to pursue the SS-520's launch capability, two effective modifications from the SS-520-5 are proposed: thrust enhancement of the 1st stage motor and installation of an additional RCS between the 2nd and 3rd stages. The framework of launch capability analysis is established by a multi-objective genetic algorithm (MOGA), where its two objectives are selected as the altitudes of perigee and apogee. The analysis reveals that the two proposed modifications to the SS-520-5 work effectively but differently. The 10% increase of the 1st stage enhancement is particularly effective when the target altitude of perigee is low (e.g., 200 km), whereas the installment of the additional RCS with 30 kg increases accessibility to a much higher altitude of perigee, even to circular orbit reaching altitudes of 550 km for a 1U-sized CubeSat and 280 km for a 6U-sized CubeSat. The simultaneous application of both modifications would result in launch capability able to deliver a 10-kg payload. From a more general perspective, the results in this paper suggest that it is possible for a very small launch vehicle (VSLV) of the 3-ton class and 10m in length to deliver a 10-kg-class payload into low Earth orbit.

关键词： SS-520 Very small launch vehicle (VSLV) Mission design and analysis multi-objective genetic algorithm Attitude control for spinning spacecraft

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An efficient hybrid multi-objective memetic algorithm for the frequency assignment problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 87卷 103265-000页

作者： Kiouche, Abd Errahmane Bessedik, Malika Benbouzid-SiTayeb, Fatima Keddar, Mohamed Reda Ecole Natl Super Informat ESI LMCS BP 68M 16309 Algiers Algeria

This paper investigates hybridization of multi-objective memetic algorithm and artificial immune system (AIS) for the Frequency Assignment Problem (FAP) in cellular mobile networks. The considered objectives to minimize are the total interference, the maximal interference, and the number of used frequencies. The proposed approach integrates FAP-specific local search into the evolutionary process to overcome the shortcoming of the multi-objective genetic algorithm, as well as clonal selection and receptor editing, which aims to improve the algorithm exploration and exploitation abilities. Based on the hypervolume metric, the proposed hybrid multi-objective algorithm produces high quality solutions as proved by the tests performed over COST259 instances and corroborated by the comparisons with the most frequently referred algorithms in the related literature. Furthermore, the effect and the behaviour of the main parameters of our algorithm and the interaction between them are analysed using the Design of Experiment (DOE).

关键词： multi-objective genetic algorithm Memetic algorithm Local search Clonal selection Receptor editing Frequency assignment problem

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COSMO: A dynamic programming algorithm for multicriteria codon optimization

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 1811-1818页

作者： Taneda, Akito Asai, Kiyoshi Hirosaki Univ Grad Sch Sci & Technol Hirosaki Aomori 0368561 Japan Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba 2778562 Japan

Codon optimization in protein-coding sequences (CDSs) is a widely used technique to promote the heterologous expression of target genes. In codon optimization, a combinatorial space of nucleotide sequences that code a given amino acid sequence and take into account user-prescribed forbidden sequence motifs is explored to optimize multiple criteria. Although evolutionary algorithms have been used to tackle such complex codon optimization problems, evolutionary codon optimization tools do not provide guarantees to find the optimal solutions for these multicriteria codon optimization problems. We have developed a novel multicriteria dynamic programming algorithm, COSMO. By using this algorithm, we can obtain all Pareto-optimal solutions for the multiple features of CDS, which include codon usage, codon context, and the number of hidden stop codons. User-prescribed forbidden sequence motifs are rigorously excluded from the Pareto-optimal solutions. To accelerate CDS design by COSMO, we introduced constraints that reduce the number of Pareto-optimal solutions to be processed in a branch-andbound manner. We benchmarked COSMO for run-time and the number of generated solutions by adapting selected human genes to yeast codon usage frequencies, and found that the constraints effectively reduce the run-time. In addition to the benchmarking of COSMO, a multi-objective genetic algorithm (MOGA) for CDS design was also benchmarked for the same two aspects and their performances were compared. In this comparison, (i) MOGA identified significantly fewer Pareto-optimal solutions than COSMO, and (ii) the MOGA solutions did not achieve the same mean hypervolume values as those provided by COSMO. These results suggest that generating the whole set of the Pareto-optimal solutions of the codon optimization problems is a difficult task for MOGA. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Constraints Codon adaptation index Codon pair bias Hidden stop codon multi-objective genetic algorithm Codon optimization/deoptimization

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