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Direct and indirect emissions: a bi-objective model for hybrid vehicle routing problem

Journal of Business Economics 2024年第3期94卷 413-436页

作者： Eslamipoor, Reza Faculty of Business and Law De Montfort University Leicester United Kingdom

This research proposes a mathematical model for the vehicle routing problem (VRP), which involves pickup and delivery of cargos to different depots. Vehicle capacity and energy consumption that varies with load are both contributing factors. The mathematical model seeks to simultaneously reduce costs and waiting time for customers. Multi-objective particle swarm optimisation (MPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) are proposed as methods to solve the model. This is implemented and solved in the GAMS software for moderate-sized problems, and the results are compared to those obtained by two different algorithms in order to validate the model and algorithms. Then, the results are compared with those obtained by NSGA-II algorithm based on comparative metrics. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Hybrid vehicle routing problem M1 M2 multiobjective optimization problem Transportation

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Hybrid multiobjective Evolutionary Algorithm for Assembly Line Balancing problem with Stochastic Processing Time

Hybrid Multiobjective Evolutionary Algorithm for Assembly Li...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Zhang, Wenqiang Xu, Weitao Gen, Mitsuo Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Peoples R China Tokyo Univ Sci Tokyo Japan

An assembly line (AL) is a typical manufacturing process consisting of various tasks in which interchangeable parts are added to a product in a sequential manner at a station to produce a final product. Most of the work related to the ALs concentrate on the assembly line balancing (ALB) which deals with the allocation of the tasks among stations so that the precedence relations among them are not violated and a given objective function is optimized. From the view point of the real ALB systems, multiobjective ALB with stochastic processing time (S-moALB) is an important and practical topic from traditional ALB problem involving conflicting criteria such as the cycle time, variation of workload, and/or the processing cost under uncertain manufacturing environment. This paper proposes a hybrid multiobjective evolutionary algorithm (hMOEA) to deal with such S-moALB problem with stochastic processing time considering minimization of the cycle time and the processing cost, given a fixed number of stations available. The special fitness function strategy is adopted and a hybrid selection mechanism is designed to improve the convergence and distribution performance. Experimental results with various instances show that hMOEA could get the better convergence distribution performance than existing MOEAs. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： Assembly line balancing (ALB) Stochastic processing time multiobjective optimization problem multiobjective evolutionary algorithm (MOEA)

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Combining multiobjective and Single-objective Genetic Algorithms in Heterogeneous Island Model

Combining Multiobjective and Single-objective Genetic Algori...

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2010 IEEE World Congress on Computational Intelligence

作者： Pilat, Martin Neruda, Roman Charles Univ Prague Fac Math & Phys Dept Theoret Comp Sci & Math Log Prague 1 Czech Republic Acad Sci Czech Republ Inst Comp Sci Prague 8 Czech Republic

ISBN: (纸本)9781424481262

The majority of multiobjective genetic algorithms is computationally expensive, therefore they often need to be parallelized before they can be used to solve practical tasks. Parallelization of multiobjective genetic algorithms is a relatively studied area, but no clearly winning approach has appeared yet. In this paper we present a novel parallel hybrid algorithm which combines multiobjective and single-objective genetic algorithms. We show that this algorithm can be successfully used to solve multiobjective optimization problems while outperforming more traditional parallel versions of multiobjective genetic algorithms.

关键词： genetic algorithms heterogeneous island model multiobjective genetic algorithm multiobjective optimization problem parallel hybrid algorithm single-objective genetic algorithm

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Content Aware Resource Allocation in OFDM Systems For Energy Efficient Video Transmission

Content Aware Resource Allocation in OFDM Systems For Energy...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Danish, Emad De Silva, Varuna Fernando, Anil De Alwis, Chamitha Kondoz, Ahmet Univ Surrey I Lab Multimedia Commun Res Guildford GU2 5XH Surrey England

ISBN: (纸本)9781479912919

For OFDM channels, we propose a content-aware subcarrier allocation scheme for multiuser video transmission that maximizes user perceived quality while minimizing power consumption. The scheme utilizes utility function-based genetic algorithms to solve the multi-objective optimization problem. It significantly outperforms existing methods in terms of perceived quality and consumed power.

关键词： OFDM modulation genetic algorithms multimedia communication video communication OFDM channel OFDM systems content-aware resource allocation content-aware subcarrier allocation scheme energy-efficient video transmission multiobjective optimization problem multiuser video transmission power consumption minimization user perceived quality maximization utility function-based genetic algorithm Bit rate Genetic algorithms Joints OFDM Quality assessment Resource management Video recording

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Multi-Objective Tuning of Nonlinear PID Controllers for Greenhouse Environment using Evolutionary Algorithms

Multi-Objective Tuning of Nonlinear PID Controllers for Gree...

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2010 IEEE World Congress on Computational Intelligence

作者： Hu, Haigen Xu, Lihong Wei, Ruihua Zhu, Bingkun Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China Tongji Univ Dept Traff & Transportat Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781424481262

This paper investigates the issue of PID-controller parameters tuning for a greenhouse climate control system using Evolutionary Algorithms based on multiple performance measures such as good set-point tracking and smooth control signals. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is validated for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a series of simulations. The results show that the controllers by tuning the gain parameters can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Maybe it is quite an effective and promising tuning method using multi-objective algorithms in the complex greenhouse production.

关键词： control increment evolutionary algorithms evolutionary computation greenhouse climate control system greenhouse environment greenhouses integrated time square error multiobjective algorithm multiobjective optimization problem multiobjective tuning multiple performance measure nonlinear PID controller nonlinear control systems nonlinear thermodynamic law optimisation set-point tracking smooth control signals three-term control

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Extracting Information Cores with Multi-property Using a multiobjective Evolutionary Algorithm

Extracting Information Cores with Multi-property Using a Mul...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Ren, Zhenni Liu, Jing Xidian Univ Sch Artificial Intelligence Xian Shaanxi Peoples R China

ISBN: (纸本)9781728121536

Recommender systems are efficient tools to alleviate the information overload problem. The information core extraction problem aims at finding a group of users who carry reliable, objective information to represent the system to provide recommendations. Many greedy algorithms have been proposed to deal with this problem, but most algorithms only focus on finding an information core to achieve accurate recommendation results, losing sight of other recommendation objectives with equivalent importance. To this end, we model the information core extraction problem as a multiobjective optimization problem, and propose a multiobjective evolutionary algorithm to extract information cores, labeled as MOEA-IC. MOEA-IC extracts information cores with multi-property at the same time, which can provide candidate solutions with different properties for decision makers. In the experiments, the performance of MOEA-IC is validated on three datasets with varying selection rates and compared with that of two existing greedy algorithms. The experimental results demonstrate the efficiency of MOEA-IC and show that the information core extracted by MOEA-IC can not only be helpful in providing accuracy recommendation, but also provide recommendations of good performance in terms of coverage and diversity criteria. The significance of this study is that the information-core-based recommendation is timesaving, and decision makers can choose information cores with different properties provided by MOEA-IC based on practical requirements.

关键词： recommender system information core multiobjective evolutionary algorithm multiobjective optimization problem

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An Evolutionary multiobjective optimization Approach for HEV Energy Management System

An Evolutionary Multiobjective Optimization Approach for HEV...

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11th Portuguese Conference on Automatic Control

作者： Pajares Ferrando, Alberto Blasco Ferragud, Xavier Reynoso-Meza, Gilberto Herrero Dura, Juan Manuel Univ Politecn Valencia Inst Automat & Informat Ind Valencia Spain

ISBN: (纸本)9783319103792

Hybrid vehicles have become a promising solution to mitigate the negative effects of pollution and fossil fuel dependency, consequences (among other causes) of an increasing demand on mobility of people and goods. A hybrid vehicle is integrated by many subsystems, where one of the most important is the energy management system, which coordinates when to switch between energy sources to give a desired output. The energy management system needs to take into account several objectives and specifications, most of the times in conflict, to guarantee an acceptable vehicle's performance. This situation makes it a complex system to control and design. In this context, multiobjective optimization could play a significant role as a design tool, since it enables the designer to analyse the tradeoff among design alternatives. In this paper we present a multiobjective optimization design procedure by means of evolutionary multiobjective optimization in order to tune the energy management system of hybrid vehicles. To this aim, new meaningful objectives are stated and optimized. The presented results validate this approach as viable and useful for designers.

关键词： Energy Management System Hybrid Electrical Vehicle multiobjective optimization problem

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Approximate Bilevel Programming via Pareto optimization for Imputation and Control of optimization and Equilibrium models

Approximate Bilevel Programming via Pareto Optimization for ...

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European Control Conference (ECC)

作者： Thai, Jerome Hariss, Rim Bayen, Alexandre Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA MIT Ctr Operat Res Cambridge MA 02139 USA Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA 94720 USA

ISBN: (纸本)9783952426937

We consider the problem of imputing the function that describes an optimization or equilibrium process from noisy partial observations of nearly optimal (possibly noncooperative) decisions. We generalize existing inverse optimization and variational inequality problems to construct a novel class of multi-objective optimization problems: approximate bilevel programs. In this class, the "ill" nature of the complementary condition prevalent in bilevel programming is avoided, and residual functions commonly used for the design and analysis of iterative procedures, are a powerful tool to study approximate solutions to optimization and equilibrium problems. In particular, we show that duality gaps provide stronger bounds than Pp norms of KKT residuals. The weighted criterion method is in some sense equivalent to existing formulations in the case of full observations. Our novel approach allows to solve bilevel and inverse problems under a unifying framework, via block coordinate descent, and is demonstrated on 1) consumer utility estimation and pricing and 2) latency inference in the road network of Los Angeles.

关键词： Pareto optimisation duality (mathematics) inverse problems iterative methods pricing utility theory variational techniques KKT residual Pareto optimization approximate bilevel program bilevel programming block coordinate descent complementary condition consumer utility estimation duality gap equilibrium model equilibrium process imputation inverse optimization inverse problem iterative procedure latency inference multiobjective optimization problem noisy partial observation noncooperative decision residual function road network variational inequality problem weighted criterion method Approximation methods Convex functions Estimation Inverse problems optimization Programming Radio frequency

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Multi-objective input signal design for plant friendly identification of process systems

Multi-objective input signal design for plant friendly ident...

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American Control Conference

作者： Narasimhan, S Rengaswamy, R Clarkson Univ Dept Chem Engn Potsdam NY 13699 USA

ISBN: (纸本)0780383354

System Identification is the process of constructing an accurate and reliable dynamic mathematical model of the system from observed data and available knowledge. The choice of inputs used for perturbing the system is critical in the identification and model building exercise. One of the major objectives of system identification is accurate estimation of the system parameters. Identification of chemical process plants is carried out on running plants in real time. The practitioner would thus prefer a 'plant friendly' input signal. We propose unified multi-objective formulations and solution methods for the input design for two particular cases. The input can be evaluated as a solution to a multi-objective optimization problem.

关键词： chemical industry process control parameter estimation design of experiments optimisation multiobjective input signal design process system identification dynamic mathematical model chemical process plants multiobjective optimization problem experimental design

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MOEA/D Using Dynamic Weight Vectors and Stable Matching Schemes for the Deployment of Multiple Airships in the Earth Observing System

MOEA/D Using Dynamic Weight Vectors and Stable Matching Sche...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Xu, Zhouwu Liu, Jing Qiao, Baihao Cao, Yating Xidian Univ Guangzhou Inst Echnol Guangzhou Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou Peoples R China

ISBN: (纸本)9781728183923

The utilization of stratosphere in earth observing is getting more attention. For maximizing the usage of stratosphere space, it is of vital importance to deploy the platforms of which airship is the representative, properly. Under such a background, a multiobjective model for the deployment problem of multiple airships in the earth observing system (MOM_DP_MAEOS), considering the number of covered tasks and the total profits of the observable tasks, is designed. Furthermore, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) with dynamic weight vectors (DW) and stable matching (STM) schemes (MOEA/D_DW-STM) is proposed to optimize the deployment problem in this paper. DW is applied in the process of updating the individuals in the neighborhoods, playing the role of assisting local search. STM, including a bi-direction selection process, is designed to overcome the shortage that only the process of choosing solutions to subproblems exists in MOEA/D. Besides, K-means clustering operator applied in the initialization and the normalization operator for balancing the search efforts of each objective are applied. A variety of experiments are carried out on different kinds of benchmarks to verify the effectiveness of MOEA/D_DW-STM and the rationality of MOM_DP_MAEOS. Compared with the original MOEA/D and nondominated sorting genetic algorithm II (NSGA-II), the designed algorithm obtains better Pareto fronts (PF) under the same computational cost.

关键词： Earth Observing System Deployment problem multiobjective optimization problem Evolutionary Algorithm Stable Matching

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