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Scheduling different types of vehicles in distribution centers with fixed due dates and packed shipments

APPLIED SOFT COMPUTING 2020年 94卷 106450-106450页

作者： Mohtashami, Ali Islamic Azad Univ Qazvin Branch Dept Ind Management Qazvin Iran

This paper proposes a novel non-dominated sorting genetic algorithm-based method for scheduling simultaneously different types of vehicles with different capacities in distribution centers working as cross dock. In this paper, a three-objective model is proposed, which minimizes operational time, lateness and earliness of delivering products. In many of real life cases, packs of products with different number of products must be unloaded instead of arbitrary number of products because of products' nature and/or physical limitations. It means that the optional amount of unloading is not prohibited in many of real life cases unlike to previous researches;therefore, scheduling vehicles by considering this limitation is different from previous works that allow optional amount of unloading. Another advantage of this paper is consideration of the frequent unloading pattern of inbound vehicles. This consideration better synchronizes the inbound and outbound vehicles compared to nonfrequent pattern, and as a result, it helps to reduce the operational time of cross docking as well as the shipping cycle. This paper proposes a new scheduling method for considering the mentioned novelties. Furthermore, Taguchi design is used for regulating the proposed algorithm's parameters. Several numerical examples are solved by the proposed method, and the obtained results are compared to multi-objective particle swarm optimization. The numerical results show the superiority of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Distribution center Cross docking Taguchi method non-dominated sorting genetic algorithm Particle swarm optimization Meta-heuristic

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Optimisation configuration of hybrid AC/DC microgrid containing electric vehicles based on the NSGA-II algorithm

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JOURNAL OF ENGINEERING-JOE 2019年第10期2019卷 7229-7236页

作者： Ye, Bin Shi, Xuemei Wang, Xuli Wu, Hongbin State Grid Anhui Econ Res Inst Hefei 230022 Anhui Peoples R China Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China

Based on the development of hybrid AC/DC microgrid, considering the objectives of microgrid life-cycle cost, self-balancing rate and converter losses, an optimisation configuration model of a hybrid AC/DC microgrid is established here. Based on the different model of EVs to connect with grid, the matching control strategies for the model are proposed. The elitist non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the optimisation configuration model. With the example system simulation, the demand response function of EVs is analysed, and the impacts of different EVs charging modes on optimisation configuration results of the hybrid AC/DC microgrid are studied and discussed. The rationality and effectiveness of proposed models and strategies are verified.

关键词： Pareto optimisation distributed power generation electric vehicles genetic algorithms hybrid AC/DC microgrid microgrid life-cycle cost optimisation configuration model optimisation configuration results electric vehicles NSGA-II algorithm self-balancing rate converter losses matching control strategies non-dominated sorting genetic algorithm demand response function EV charging modes

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A Multi-objective Design of In-Building Distributed Antenna System Using Evolutionary algorithms 1

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39th SGAI International Conference on Techniques and Applications of Artificial Intelligence (AI)

作者： AlShanqiti, Khawla Poon, Kin Shakya, Siddhartha Sleptchenko, Andrei Ouali, Anis Khalifa Univ Emirates ICT Innovat Ctr EBTIC Abu Dhabi U Arab Emirates Khalifa Univ Dept Ind & Syst Engn Abu Dhabi U Arab Emirates

ISBN: (数字)9783030348854

ISBN: (纸本)9783030348854;9783030348847

The increasing data traffic inside buildings requires maintaining good cellular network coverage for indoor mobile users. Passive In-building Distributed Antenna System (IB-DAS) is one of the most efficient methods to provide an indoor solution that meets the signal strength requirements. It is a network of spatially distributed antennas in a building connected to telephone rooms which are then connected to a Base Transmission Station (BTS). These connections are established through passive coaxial cables and splitters. The design of IB-DAS is considered to be challenging due to the power-sharing property resulting in two contradicting objectives: minimizing the power usage at the BTS (long-term cost) and minimizing the design components cost (short-term cost). Different attempts have been made in the literature to solve this problem. Some of them are either lacking the consideration of all necessary aspects or facing scalability issues. Additionally, most of these attempts translate the IB-DAS design into a mono-objective problem, which leads to a challenging task of determining a correct combined objective function with justified weighting factors associated with each objective. Moreover, these approaches do not produce multiple design choices which may not be satisfactory in practical scenarios. In this paper, we propose a multi-objective algorithm for designing IB-DAS. The experimental results show the success of this algorithm to achieve our industrial partner's requirement of providing different design options that cannot be achieved using mono-objective approaches.

关键词： Distributed Antenna System Multi-objective evolutionary algorithm non-dominated sorting genetic algorithm Crowding distance Normalization

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A Pareto Strategy based on Multi-Objective for Optimal Placement of Distributed Generation Considering Voltage Stability

A Pareto Strategy based on Multi-Objective for Optimal Place...

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International Conference on Innovative Trends in Computer Engineering (ITCE)

作者： Ali, Shimaa Mohamed Mohamed, Al-Attar Ali Hemeida, A. M. Aswan Univ Fac Energy Engn Aswan Egypt Aswan Univ Fac Engn Aswan Egypt

ISBN: (纸本)9781538652619

This study focuses on developing a multiobjective framework to seek out the optimal Distributed Generations (DGs) placement and sizing in large scale distribution networks. Renewable energy resources like wind turbine (WT), photovoltaic (PV) are employed as resources of Distributed Generation (DG). The well known and non-dominated sorting genetic (NSGA-III) algorithm is implemented to handle various objective functions such as active power losses, voltage deviation and voltage stability index. The proposed method is tested on standard IEEE 118-bus radial distribution networks. The proposed algorithm is used a range of non-dominant Pareto-optimal solutions that are stored in the external archive and then the 'best' compromise solution is identified by fuzzy sets technique. The simulation results show that the proposed methodology ready to provide well distributed Pareto optimum solutions for the multiobjective optimal power flow problem. Furthermore, in order to validate the obtained results Multi-objective Dragonfly (MODA) algorithm is performed also and the simulation results of two algorithms are compared with each other

关键词： Distributed generation Voltage stability Multi-objective Dragonfly algorithm non-dominated sorting genetic algorithm

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Modified Crowding Distance and Mutation for Multimodal Multi-Objective Optimization 19

Modified Crowding Distance and Mutation for Multimodal Multi...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Javadi, Mahrokh Zille, Heiner Mostaghim, Sanaz Otto von Guericke Univ Fac Comp Sci Magdeburg Germany

ISBN: (纸本)9781450367486

Many real-world applications contain complex optimization problems with several conflicting objectives. Finding a solution which can satisfy all the objectives is usually a challenging task for optimization algorithms. When dealing with these complex multiobjective problems, decision-makers want to find the best tradeoff between the conflicting objectives. Another challenge occurs in problems where multiple configurations of the input variables might yield the same objective function values. Such problems are called multimodal problems. For a decision maker, it might be of importance to obtain enough information about all the alternative optimal solutions that reach the same objective value. Traditionally, Evolutionary algorithms make use selection processes based only on objective function values, which might be a disadvantage when faced with multimodal problems. In this article, we present two operators to use in multimodal multi-objective algorithms, namely a modified crowding distance operator and a neighbourhood Polynomial mutation, which take into account the distribution of solution in the decision space at run-time. Our experimental results demonstrate that the proposed operators are able to outperform the performance of a state-of-the-art method on six current multimodal benchmark functions.

关键词： Multi-modality Multi-modal problems Multi-objective Optimization Evolutionary algorithms non-dominated sorting genetic algorithm

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A Comprehensive Study of Sequential Simulated Moving Bed: Purification of Xylo-oligosaccharides and fructose-glucose

A Comprehensive Study of Sequential Simulated Moving Bed: Pu...

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作者： Yan Li University of Western Ontario

学位级别：博士

Chromatographicseparation is a promising alternative for separation and purification of sugars in industry. Simulated moving bed (SMB) technique has been proven as an efficient chromatographic separation method due to its enhanced productivity and purity, reduced solvent consumption, convenient operating control, and improved separation performance for some systems with low resolution andselectivity. The sequential simulated moving bed (SSMB) isa modification of the conventional SMB process, which currently has some applications forsugar separation due to its low solvent consumption. This work mainly investigates the design strategy of the innovative SSMB process and explore its advantages and disadvantages over the SMB process based on the xylo-oligosaccharides (XOSs) and fructose-glucose systems. SSMB separation of XOSs, a functionalfood additive in the formof a oligomeric saccharide, was firstly conducted. DOWEX MONOSPHERE TM 99/310 resin ionized with K +, which has better selectivity compared with Ca 2+ and Na + was used as the stationary phase. Breakthrough experiments showed that XOSs and the two majorindustrial impurities, xylose and arabinose, allexhibit linear isotherms. Transport-dispersive (TD) model parameters were determined by pulse experimentscarried out at various flowrates. Finally, both the averaged and individual parameters of XOSs and XOS2-XOS7 were obtained. Lab-scale SSMB experiments and the corresponding simulations were carried out to validate the acquired TD model parameters and adsorption isotherms. After that, in order to investigate the optimal operating conditions of this process, themulti-objective optimizations were carried out for three cases with various objectivesand constraints. It was found that, for a given SSMB unit, there exist a paretocurve for simultaneous maximization of purity and unit throughput. The flowrate ratios (m values), however, exhibit some trends that are different from those of conventional SMB and cannot be exp

关键词： Simulated moving bed sequential simulated moving bed xylo-oligosaccharides transport-dispersive model equilibrium-dispersive model adsorption isotherms multi-objective optimization non-dominated sorting genetic algorithm pareto curve Fortran

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A Product Configuration Optimization Method Based on non-dominated sorting genetic algorithm

A Product Configuration Optimization Method Based on Non-dom...

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Global Conference on Digital Design and Manufacturing Technology

作者： Sun, W. H. Lu, W. C. Lin, D. Y. China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9783037850718

In order to realize the complex product rapid configuration design in the environment of mass configuration (MC), the non-dominated sorting genetic algorithm (NGSA) for product rapid configuration design is proposed in this paper. The model of multi-objective product configuration optimization is established, and hierarchical analysis is made for configuration design. By comparing the similarity and integrity of requirement and instance, the sequence of retrieval instances is given according to the reuse degree, and multi-objective optimization configuration based on NGSA is realized. Finally, the validity and practicability of the method is verified by an instance which is applied in rapid configuration design of the drive module of tuyere puncher.

关键词： Multi-objective optimization Knowledge reuse Product configuration non-dominated sorting genetic algorithm

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Modeling and optimization of cylindrical wire electro discharge machining of AISI D3 tool steel using non-dominated sorting genetic algorithm

Modeling and optimization of cylindrical wire electro discha...

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International Conference on Graphic and Image Processing (ICGIP)

作者： Golshan, Abolfazl Gohery, Scott Ayob, Amran Isfahan Univ Technol Fac Mech Engn Esfahan Iran Univ Teknol Malaysia Fac Mech Engn Skudai Malaysia

ISBN: (纸本)9780819489326

In this study the influence of Cylindrical Wire Electro Discharge Machining on surface roughness and material removal rate is conducted. From experimental DOE method and mathematical modeling, the importance and correlation between corresponding parameters and process output characterization are studied. The investigated input parameters include electrical current, pulse-off time, voltage, rotation speed and cone angle. The non-dominated sorting genetic algorithm is then applied on the outputs to generate appropriate optimized models. The optimization procedure shows that the suggested method results in a high performance problem solving.

关键词： Cylindrical Wire Electro Discharge Machining Surface roughness Material removal rate non-dominated sorting genetic algorithm

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Multi-project Time-cost Optimization in Critical Chain with Resource Constraints

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第10期22卷 3738-3752页

作者： Amiri, Mohammad Javad Taheri Haghighi, FarshidReza Eshtehardian, Ehsan Abessi, Ozeair Babol Noshirvani Univ Technol Construct Engn & Management Babol Sar *** Iran Babol Noshirvani Univ Technol Dept Civil Engn Babol Sar *** Iran Tarbiat Modares Univ Dept Project Management Tehran 14115111 Iran

The simultaneous reduction of the project cost and time has paramount importance in today's competitive world;however, it is necessary to balance the project time and cost because of the reduction asymmetry of the two factors in a project. How to balance the cost and time parameters in managing construction projects is also critical and there have always been some attempts made to provide different approaches to control it. Given the immense importance of considering resource constraints for project scheduling problems, and the proximity of the study conditions to the real world, resource constraints were also included. In a project, project managers need to be aware of resource constraints. As resource constraint scheduling problem is considered NP-hard, the meta-heuristic models were developed in this paper in order to obtain results contributing to project managers' decision-making. For this purpose, a non-dominated sorting genetic algorithm method was developed to optimize a time-cost trade-off problem. Furthermore, to solve a multi-project scheduling problem, the critical chain method was used. In order to evaluate the performance of the model, the developed model was first studied in a small scale and then simultaneous projects with 7, 8 and 10 tasks were planned. Because resource availability is essential in such problems, after solving the proposed model, a sensitivity analysis was performed for daily resources and the results were discussed. Results shows the ability of the proposed model and methodology to optimize the time-cost tradeoff considering resource constraints in sample problems. Solutions obtained showed that in some cases of scheduling without this algorithm, resource consumption exceeded above resource availability. After solving the model by proposed algorithm, resource allocation is implemented considering resource constraint. This model determined these resources as crucial and helped managers to control them.

关键词： bi-objective time-cost optimization resource constraints non-dominated sorting genetic algorithm

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The Performance Indices Optimization of a Symmetrical Fully Spherical Parallel Mechanism for Dimensional Synthesis

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2018年第3-4期90卷 305-321页

作者： Enferadi, Javad Nikrooz, Reza Islamic Azad Univ Dept Mech Engn Mashhad Branch Mashhad Iran

The 3(UPS)-S fully spherical parallel manipulator is the most famous fully spherical parallel robot. Therefore, dimensional synthesis of the manipulator to optimize kinematics and dynamic performance indices is very important. In this paper, we proposed a new kinematics index that is called global workspace conditioning index (GWCI). This index is a suitable criteria to compare workspace of the spherical parallel manipulators. Using this index, and other performance indices such as;Global Conditioning Index (GCI), Global Gradient Index (GGI) and Dynamic Dexterity Index (DDI), we optimize dimensions of the manipulator based on single and multi-objective optimizations. Two dimensionless design parameters are advised to obtain an optimal solution. The manipulator is optimized with regard to the joints constraint and range of motion actuators as functions of the design parameters. For this purpose, we elaborate kinematics analyses and also obtain the mass matrix of the manipulator using its kinetic energy. Next, single-objective optimizations based on genetic algorithm (GA) and Pattern Search (PS) are presented. The comparison between GA and PS in the single-objective optimization shows that the accuracy of both methods is rather equal, but in elapsed time PS is better than GA. Finally, multi-objective evolutionary algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the true optimal solutions and Pareto fronts. The obtained solution will be a set of optimal geometric parameters to adjust the dynamic and the kinematic performance.

关键词： Dimensional synthesis Global workspace conditioning index Performance indices genetic algorithm Pattern search non-dominated sorting genetic algorithm

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