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Dynamic Multi-objective optimization problem of Container Intermodal Transport: An Empirical Analysis on the Belt and Road Initiative of China 3rd

Dynamic Multi-objective Optimization Problem of Container In...

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3rd International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT)

作者： Hou, Jue Minist Transport Peoples Republ China China Waterborne Transport Res Inst Xitucheng Rd 8 Beijing Peoples R China

ISBN: (纸本)9789811079894;9789811079887

The Belt and Road Initiative of China proposes a higher request for the development of container intermodal transport. The traditional intermodal transport organization mode may change: the daily handling capacity of container railway central terminal will increase, speed of train will improve, there will be more and more double-stack container train come into use. These changes will have far-reaching consequences on the container multimodal transport development of China. This paper attempts to evaluate the economic and environmental effect on the potential change of container intermodal transport of China on the Belt and Road Initiative, with a specific focus on energy consumption and emission. Multi-objective optimization model is built to minimize transport time, cost, energy consumption, and also emissions, which is based on simulation model of locomotive, Non- Dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the multi-objective optimization model. An empirical case regarding container intermodal transport from Shanghai to Urumqi is applied to verify the validity of model in the end. The dynamic multi-objective optimization model allows the logistics enterprise to be more flexible in their decision to plan the scheme of container intermodal transport.

关键词： Intermodal transport Multi objective optimization problem Emissions Simulation Belt and Road Initiative

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A multi-layer congested facility location problem with consideration of impatient customers in a queuing system

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IFAC-PapersOnLine 2019年第13期52卷 2279-2284页

作者： Chaleshtori, Amir Eshaghi Jahani, Hamed Aghaie, Abdollah Ivanov, Dmitry K. N.Toosi University of Technology Tehran Iran School of Business IT and Logistics RMIT University Melbourne Australia HWR Berlin Global Supply Chain and Operations Management Germany

This study presents a novel multi-objective model for a facility location-allocation problem with congestion in a multi-layer network. The proposed model contemplates the existence of impatient customers within a classical queuing system, namely M/M/1. The network is configured by determining the optimal number of facilities in each layer and allocation of customers to the facilities in a hierarchy facilities problem. Regarding the first objective function of the model, we minimize the sum of aggregate costs related to the customers' waiting times, as well as the costs imposed by the impatience of the customers. In the second objective function, the costs associated with the sum of aggregate idleness of facilities are minimized. The proposed multi-objective multi-layer facility location-allocation (MLFLA) problem is solved by an evolutionary approach according to the Pareto envelope-based selection algorithm II (PESA-II). Parameters of the algorithm are tuned by Taguchi method. Then, the performance of the algorithm is reported by four performance metrics introduced for multi-objective optimization problems. The computational run times of 10 test problems demonstrate that the solution methodology is capable of solving the problem with large network sizes. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Network layers Aggregates Location Multiobjective optimization Queueing networks Queueing theory Sales Taguchi methods Evolutionary approach Facility location allocations Facility location problem Impatient customers Multi objective modeling Multi objective optimization problem PESA II Queuing systems

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Multi-objective optimization for the train load planning problem with two cranes in seaport terminals

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IFAC-PapersOnLine 2016年第3期49卷 383-388页

作者： Ambrosino, D. Bernocchi, L. Siri, S. Department of Economics and Business Studies University of Genova Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy

This paper deals with the train load planning problem arising in seaport terminals. In particular, the train load planning problem in case of two cranes is studied, corresponding to the optimal assignment of containers to the wagons on which they are loaded and to the cranes which perform the loading operations. This optimal assignment is realized by considering the simultaneous optimization of several objectives. Hence, the train load planning problem is formulated as a multi-objective optimization problem and three multi-objective optimization techniques are applied and compared to solve it. Some preliminary experimental results are shown in order to compare the three methods. © 2016

关键词： Multiobjective optimization Cranes Loading operations Multi objective optimization problem Multi objective optimization techniques Optimal assignment Simultaneous optimization Train loads

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A Least Squares Method for Ensemble-based Multi-objective Oil Production optimization

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IFAC-PapersOnLine 2018年第8期51卷 7-12页

作者： Christiansen, L.H. Hørsholt, Steen Jørgensen, J.B. Department of Applied Mathematics and Computer Science Center for Energy Resources Engineering Technical University of Denmark DK-2800 Kgs. Lyngby Denmark

Despite a significant potential to improve industrial standards, practical applications of production optimization are impeded by geological uncertainty. As a mean to handle the associated financial risks, the oil literature has devised a range of ensemble-based strategies that seek to optimize proper combinations of sample-estimated risk measures to balance the opposing objectives of risk and reward. Many of the associated optimization problems are naturally formulated in terms of multi-objective optimization (MOO). Ideally, MOO problems should be solved by generating an approximation to the efficient frontier of optimal tradeoffs between risk and return. However, the large-scale nature of real-life oil reservoirs implies that formation of the frontier often becomes computationally intractable in practice. To meet this challenge, this paper introduces a generalized least squares (LS) approach that provides an efficient and unified solution strategy for ensemble-based multi-objective optimization problems. At its core, the LS method uses an a priori characterization of desirable trade-offs that allows the method to focus on a single Pareto optimal point. Consequently, the LS approach avoids the need to generate a representative of the efficient frontier. In turn, this significantly reduces computational complexity compared to most MOO methods. As a result, the LS method poses a practical alternative to conventional strategies when the efficient frontier is unknown and computationally intractable to generate. © 2018

关键词： Multiobjective optimization Commerce Economic and social effects Least squares approximations Pareto principle Petroleum reservoir engineering Petroleum reservoirs Production control Risk assessment Risk perception Risks Stochastic control systems Stochastic models Stochastic systems Generalized least square Geological uncertainty Least squares methods Model based controls Multi objective optimization problem Optimal controls optimization problems Production optimization

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Tuning of Fractional Order PIλDμControllers using Evolutionary optimization for PID Tuned Synchronous Generator Excitation System

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IFAC-PapersOnLine 2018年第4期51卷 859-864页

作者： Kumar, Lalitesh kumar, Prawendra Satyajeet Narang, Deepak Ajay Kumar Garg Engineering College India Politechnica Gdanska India JSS Academy of Technical Education India

This paper has propounded the notion of the design of cascaded integer order (IO) PID - fractional order (FO) PIλDμcontroller by evolutionary multi-objective based optimization approach for a synchronous generator excitation system. The three contradicting performance indices have been framed in time domain as well as in frequency domain to minimize error, escalate the robust stability and to minimize the energy consumption. This paper propounded the issue of contradiction in minimizing error, escalation of robust stability and minimization of energy consumption by framing cascaded IO PID - FO PID controllers as multi-objective optimization problem. The optimization problem is solved to generate the design parameter that meets the competitive multi-objective specifications relating to performance, robust stability and to optimal control by making trade-off between them and respective weightage given to each objective function. The solution generates the non-dominated set of Pareto-optimal solutions and allows the designer to select a particular controller configuration with respective weightages. With the application of this proposed design to the excitation system of synchronous generator to a power plant's, the dynamic robust stability enhanced explicitly with minimum energy consumption. © 2018

关键词： Multiobjective optimization Calculations Controllers Economic and social effects Electric control equipment Electric generators Energy utilization Frequency domain analysis Integer programming Optimal systems Pareto principle Proportional control systems Stability Synchronous generators Three term control systems Evolutionary Multi objectives Fractional order calculus Generator excitation system Minimum energy consumption Multi objective optimization problem Pareto front PID controllers Synchronous generator excitation

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Multi-objective optimization of Additive Manufacturing Process

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IFAC-PapersOnLine 2018年第11期51卷 152-157页

作者： Asadollahi-Yazdi, Elnaz Gardan, Julien Lafon, Pascal ICD/LASMIS UMR CNRS 6281 University of Technology of Troyes Troyes France EPF Engineering school Rosière-Près-Troyes France

This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fused Deposition Modeling (FDM) is investigated as a common AM technology. For this purpose, a multi-optimization problem is formulated according to the analysis of FDM technology. In this problem, layer thickness and part orientation are determined as the decision variables which are the important parameters of manufacturing. As objective functions, production time and material mass are considered and the surface roughness of FDM products and mechanical behavior of material are defined as the constraint functions. Different methodologies are developed to model the AM criteria according to these decision variables. To find the optimal solutions for manufacturing, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used. Finally, a case study highlighted the reliability of the proposed approach. © 2018

关键词： Multiobjective optimization 3D printers Decision making Deposition Genetic algorithms Layered manufacturing Stochastic systems Surface roughness Additive manufacturing process Fused deposition modeling Manufacturing parameters Mechanical behavior of materials Multi objective optimization problem Non dominated sorting genetic algorithm ii (NSGA II) Non dominated sorting genetic algorithm ii Stochastic algorithms

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Economic Model Predictive Control with Zone Tracking

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IFAC-PapersOnLine 2018年第20期51卷 16-21页

作者： Liu, Su Liu, Jinfeng Department of Chemical & Materials Engineering University of Alberta EdmontonAB T6G 1H9 Canada

In this work, we propose a framework for economic model predictive control (EMPC) with zone tracking. A zone tracking stage cost is incorporated into the existing EMPC framework to essentially form a multi-objective optimization problem. We provide sufficient conditions for asymptotic stability of the optimal steady state and characterize exact penalty for the zone tracking cost which prioritizes zone tracking objective over economic objective. A comprehensive study on the tradeoff between zone tracking and economic performance is carried out on a simple scalar linear system. Simulation results reveal intrinsic difficulties in parameter tuning due to the inconsistency between the zone tracking and economic objectives. A procedure to modify the target zone based on the economic performance and reachability of the optimal steady state is proposed. The modified target zone effectively decouples the dynamic zone tracking and economic objectives and simplifies parameter tuning. © 2018

关键词： Model predictive control Asymptotic stability Linear systems Multiobjective optimization optimization Economic modeling Economic objectives Economic performance Multi objective optimization problem Optimal steady state Predictive control Soft constraint Zone controls

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An Optimal Driving Strategy for the Ride Comfort Performance of Intelligent Vehicles on Uneven Roads

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IFAC-PapersOnLine 2018年第31期51卷 984-989页

作者： Sun, Jianing Liu, Zhiyuan Wu, Jian Department of Control Science and Engineering Harbin Institute of Technology Harbin China

This paper proposes an optimal driving strategy to improve the ride comfort performance of intelligent vehicles, which is different from the general researches that focus on the driving safety. Firstly, based on the establishment of a 7-DOF vehicle model and a road excitation model, the relationship between vehicle velocity and the comfort index is analyzed and described by a non-monotonic polynomial. Then, a velocity control requirement considering driving performance and comfort performance is constructed and transformed into a multi-objective optimization problem. By using the Pareto optimization theory, a set of Pareto optimal solutions is obtained. Finally, to choose a velocity from the Pareto optimal set, a strategy considering the subjective and objective comfort indexes from ISO2631 is given. This strategy can be applied to the velocity planning of intelligent vehicles on uneven roads. The simulation results show that the proposed strategy is effective and feasible. © 2018

关键词： Vehicle performance Automobile drivers Intelligent vehicle highway systems Multiobjective optimization Optimal systems Pareto principle Site selection Velocity Velocity control Control requirements Driving performance Driving strategy Multi objective optimization problem Pareto optimal solutions Pareto optimization Pareto optimal sets Ride comforts

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Optimized Retuning of PID Controllers for TITO Processses

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IFAC-PapersOnLine 2018年第4期51卷 268-273页

作者： Veronesi, Massimiliano Visioli, Antonio Yokogawa Italia srl Milan Italy Dipartimento di Ingegneria Meccanica e Industriale University of Brescia Italy

In this paper we propose a methodology to evaluate the performance of decentralized PID controllers for two-inputs-two-outputs processes and to retune the parameters. In particular, the model of the process is estimated based on a technique that exploits the final value theorem. Then, an evolutionary algorithm is applied in order to find the Pareto front by considering a multiobjective optimization problem. Finally, the performance obtained with the current tuning is compared with the optimal ones and, in case it is necessary, the PID parameters are retuned accordingly. A procedure to achieve the Nash optimal point is also proposed. © 2018

关键词： Process control Controllers Electric control equipment Multiobjective optimization Proportional control systems Three term control systems Decentralized PID controller Multi objective optimization problem Optimal points Performance assessment PID controllers Process parameters estimation retuning Two inputs two outputs

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Optimized PID tuning for the automatic control of neuromuscular blockade

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IFAC-PapersOnLine 2018年第4期51卷 66-71页

作者： Merigo, Luca Padula, Fabrizio Latronico, Nicola Mendonça, Teresa Paltenghi, Massimiliano Rocha, Paula Visioli, Antonio Dipartimento di Ingegneria dell'Informazione University of Brescia Brescia Italy School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Department of Medical and Surgical Specialties Radiological Sciences and Public Health University of Brescia Brescia Italy Porto Portugal Department of Anesthesiology Critical Care and Emergency Spedali Civili University Hospital Brescia Italy Porto Portugal Dipartimento di Ingegneria Meccanica e Industriale University of Brescia Brescia Italy

In this paper we propose a PID-based control scheme for the automatic regulation of the neuromuscular blockade level during surgery. In particular, we introduce an optimized tuning of the PID controller parameters based on a standard set of patient models presented in literature. The tuning procedure is based on the solution of a min-max multiobjective optimization problem that takes into account the control performance, the control effort and the robustness. A genetic algorithm is used to solve the optimization problem and to find the optimal tuning. Then, in order to evaluate the control systems robustness, the optimal PID controller is tested in simulation on a database of patient models estimated from real data. The obtained results demonstrate that the performance achieved by considering an optimized PID tuning satisfies the clinical specifications and is robust to the inter-patient variability. © 2018

关键词： Robustness (control systems) Automation Controllers Electric control equipment Genetic algorithms Multiobjective optimization Proportional control systems Robust control Three term control systems Automatic regulation Control performance Control schemes General anesthesias Multi objective optimization problem Neuromuscular blockade optimization problems PID controllers

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