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Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 805-829页

作者： Piroozfard, Hamed Wong, Kuan Yew Tiwari, Manoj Kumar Univ Teknol Malaysia Fac Mech Engn Dept Mfg & Ind Engn Utm Skudai 81310 Johor Malaysia Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approaches.

关键词： Job shop scheduling environmentally sustainable operations management carbon footprint late work criterion multi-objective imperialist competitive algorithm multi-objective optimization problem

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Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction

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APPLIED SOFT COMPUTING 2018年 72卷 539-551页

作者： Song, Shuangbao Ji, Junkai Chen, Xingqian Gao, Shangce Tang, Zheng Todo, Yuki Univ Toyama Fac Engn Toyama 9308555 Japan Kanazawa Univ Fac Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

The protein structure prediction (PSP) problem, i.e., predicting the three-dimensional structure of a protein from its sequence, remains challenging in computational biology. The inaccuracy of existing protein energy functions and the huge conformation search space make the problem difficult to solve. In this study, the PSP problem is modeled as a multi-objective optimization problem. A physics-based energy function and a knowledge-based energy function are combined to construct the three-objective energy function. An improved multi-objective particle swarm optimization coupled with two archives is employed to execute the conformation space search. In addition, a mechanism based on Pareto non-dominated sorting is designed to properly address the slightly worse solutions. Finally, the experimental results demonstrate the effectiveness of the proposed approach. A new perspective for solving the PSP problem by means of multi-objective optimization is given in this paper. (C) 2018 Published by Elsevier B.V.

关键词： multi-objective optimization problem Particle swarm optimization Knowledge-based energy function Non-dominated sorting Protein structure prediction

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optimization Study of Line Planning for High Speed Railway Based on an Improved multi-objective Differential Evolution Algorithm

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IEEE ACCESS 2019年 7卷 137731-137743页

作者： Tian, Huixin Shuai, Minwei Li, Kun Tianjin Polytech Univ Sch Elect Engn & Automat Key Lab Adv Elect Engn & Energy Technol Tianjin 300387 Peoples R China Tianjin Polytech Univ Sch Econ & Management Tianjin 300387 Peoples R China

The combination optimization of the train operation plan is an ongoing challenge: while computing power has improved, it is difficult to obtain a complete train operation plan system. With the aim of generating system-optimal operation strategies, a new collaborative optimization method is proposed for line planning problem. Through a set of constraints, the problem is formulated as a two-objective model with the objectives of economic benefits and market effects. An optimization approach with adaptive improvement of control parameters based on the multi-objective differential evolution (MODE) algorithm is proposed to solve the model, and a heuristic algorithm is designed to get a better initial solution. Finally, computational results on benchmark multi-objective problems show that the improvements of the strategies are positive and the optimization result of the improved algorithm has better stability. Meanwhile, based on a numerical example of a practical case study involving a 397-kilometer railway corridor to demonstrate the effectiveness of the proposed model and solution. As the basis of successive decisions, this method can adjust the number of trains according to the passenger fiow demand, which greatly saves operating costs.

关键词： Line planning high-speed railway collaborative optimization multi-objective optimization problem MODE

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A shallow water mooring system design methodology combining NSGA-II with the vessel-mooring coupled model

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OCEAN ENGINEERING 2019年 190卷 106417-000页

作者： Liang, Mingxiao Wang, Xuefeng Xu, Shengwen Ding, Aibing Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Collaborat Innovat Ctr Adv Ship & Deep Sea Explor Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China

In this paper, a mooring system design methodology combining the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with the vessel-mooring coupled model is first proposed. In the proposed methodology, the multi-objective optimization problem for mooring system design which involves several design variables, objective functions and constraints is established and solved by the combination of the NSGA-II algorithm and the vessel-mooring coupled model, and the initial feasible range of design variables is narrowed in advance by combining the NSGA-II with the quasi-static mooring system model. A case study is presented to demonstrated the proposed methodology, in which a catenary mooring system is designed from scratch for a semi-submersible very large floating structure (VLFS) module. It can be concluded from the obtained results that, owing to the combination of the NSGA-II algorithm and the vessel-mooring coupled model, the accurate dynamic responses of vessel and mooring systems can be used as the objective functions;thus, the proposed methodology is able to attain an optimal mooring system considering more practical design requirements, such as the vessel motion response, the safety factor of the mooring system, the length of mooring line lay down part and mooring system layout.

关键词： Mooring system design methodology Vessel-mooring coupled model multi-objective optimization problem NSGA-II

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Toward integrated design of additive manufacturing through a process development model and multi-objective optimization

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第9-12期96卷 4145-4164页

作者： Asadollahi-Yazdi, Elnaz Gardan, Julien Lafon, Pascal Univ Technol Troyes UMR CNRS 6281 ICD LASMIS 12 Rue Marie Curie BP2060 F-10010 Troyes France EPF Engn Sch 2 Rue Fernand SASTRE F-10430 Rosiere Pres Troyes France

This paper deals with design for manufacturing (DFM) approach for additive manufacturing (AM) to investigate simultaneously the different attributes and criteria of design and manufacturing. The integrated design approach is provided in the product definition level and it gradually maps the customer requirements to the final product model. The main contribution of this paper is an interface processing engine that is an interface between the product model and manufacturing model. This study uses the Skin-Skeleton approach to model the first definition of the product and model the material flow of AM technology as the manufacturing process. This engine is developed through analysis of all AM technologies and identification of their parameters, criteria, and drawbacks. In order to evaluate some product and process parameters, a multi-objective problem is formulated based on the analysis of all AM technologies;production time and material mass are optimized regarding mechanical behavior of the material and roughness of product. The approach is validated by a case study through a bag hook example. From its requirement specification to the proposed approach, this article defines an optimized product and its manufacturing parameters for fused deposition modeling (FDM) technology.

关键词： Additive manufacturing Design for manufacturing Skin-Skeleton approach multi-objective optimization problem Stochastic algorithm Fused deposition modeling

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An Improved Method for Scheduling Aircraft Ground Handling Operations From a Global Perspective

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2019年第4期36卷 1950020.1-1950020.25页

作者： Padron, Silvia Guimarans, Daniel TBS Business Sch Toulouse France Monash Univ Fac Informat Technol Data Sci & Artificial Intelligence Grp Melbourne Vic Australia

The turnaround is a critical airport process where a set of interrelated operations need to be performed to get an aircraft ready for its next flight. These activities are carried out by different vehicles, which need to be coordinated to guarantee an efficient utilization of resources. Due to the relations between operations, the order in which these resources are scheduled has a critical influence on the planning and performance of the turnaround. In this work, we present a novel methodology for solving the proposed bi-objective ground handling scheduling problem from a global perspective. This means solving a set of interconnected routing problems with restrictive time windows for each operation. We first explore the solution space using a fast heuristic, focusing then on the most promising solutions to intensify the search in the vicinity of the Pareto frontier. This two-step schema permits significantly reducing the required computational time, which, in turn, allows a more thorough exploration of solutions. Different experiments over real data from two Spanish airports have been conducted to assess the proposed methodology. Our results show that the new method not only outperforms previous approaches in terms of computational requirements, but can also improve the quality of scheduling solutions.

关键词： Air transportation multi-objective optimization problem Pareto solutions Vehicle Routing problem with Time Windows

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Comparative Study of Ant Colony Algorithms for multi-objective optimization

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INFORMATION 2019年第1期10卷 11页

作者： Ning, Jiaxu Zhang, Changsheng Sun, Peng Feng, Yunfei Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang 110159 Liaoning Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Liaoning Peoples R China Iowa State Univ Dept Comp Sci Ames IA 50010 USA Sams Club Technol Wal Mart Inc Bentonville AR 72712 USA

In recent years, when solving MOPs, especially discrete path optimization problems, MOACOs concerning other meta-heuristic algorithms have been used and improved often, and they have become a hot research topic. This article will start from the basic process of ant colony algorithms for solving MOPs to illustrate the differences between each step. Secondly, we provide a relatively complete classification of algorithms from different aspects, in order to more clearly reflect the characteristics of different algorithms. After that, considering the classification result, we have carried out a comparison of some typical algorithms which are from different categories on different sizes TSP (traveling salesman problem) instances and analyzed the results from the perspective of solution quality and convergence rate. Finally, we give some guidance about the selection of these MOACOs to solve problem and some research works for the future.

关键词： multi-objective optimization problem multi-objective optimization algorithm meta-heuristic algorithm multi-objective ant colony optimization

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EnLoc: Data Locality-aware Energy-efficient Scheduling Scheme for Cloud Data Centers

EnLoc: Data Locality-aware Energy-efficient Scheduling Schem...

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IEEE International Conference on Communications (ICC) / Workshop on Integrating UAVs into 5G

作者： Kaur, Kujeet Kumar, Neeraj Garg, Sahil Rodrigues, Joel J. P. C. Thapar Univ Comp Sci & Engn Dept Patiala Punjab India Natl Inst Telecommun Inatel Santa Rita Do Sapucai Brazil Inst Telecomunicacoes Aveiro Portugal ITMO Univ St Petersburg Russia Univ Fortaleza UNIFOR Fortaleza Ceara Brazil

ISBN: (纸本)9781538631805

With the rapid proliferation of big data, real-time processing of huge datasets becomes a challenging task;primarily because of their heterogeneous nature. Due to this, one of the most serious concerns of the modern cloud data centers is massive energy consumption during job execution. Hence, energy-aware task scheduling with data placement are considered as two important parameters for enhanced energy efficiency of modern cloud data centers. Moreover, considering the "pay-per-use" model of cloud computing infrastructure, it is important to maintain desirable service level agreement (SLA) while attaining improved data locality. Poor task scheduling decisions with limited focus of data locality are the prime reasons for escalated data communications and energy utilization levels. In order to deal with the aforementioned issues, data locality-aware energy-efficient (EnLoc) scheme for task scheduling and data placement has been proposed, particularly for MapReduce framework. The proposed EnLoc scheme is a multi-objective optimization problem (MOOP) and is solved using multi-objective evolutionary algorithm with "Tchebycheff decomposition";wherein the formulated MOOP is decomposed into theoretically finite number of subproblems to get optimal scheduling and placement decisions. The proposed scheme has been evaluated on real-time data traces acquired from OpenCloud Hadoop Cluster. The results obtained clearly demonstrate that the proposed EnLoc scheme outperforms the existing schemes in terms of energy efficiency, SLA assurance, and data locality.

关键词： Data locality energy efficiency MapReduce multi-objective optimization problem multi-objective evolutionary algorithm with decomposition scheduling

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optimization of Contract Distribution Based on multi-objective Estimation of Distribution Algorithm 2018

Optimization of Contract Distribution Based on Multi-objecti...

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International Conference on Computing and Artificial Intelligence (ICCAI)

作者： Hu, Laihong Yang, Xiaogang Fan, Hongdong Xian Res Inst Hi Tech Xian 710025 Shaanxi Peoples R China

ISBN: (纸本)9781450364195

Contract distribution is widely exists in modem commercial society, which mainly depends on qualitative analysis, and there still lack studies of quantitative analysis. Based on multi-objective estimation of distribution algorithm (MOEDA), quantitative research idea on contract distribution is explored in this article. First of all, multi-objective optimization model is built for contract distribution. Then, the algorithm flow base on MOEDA is designed. At last, simulations are carried out and compare with multi-objective genetic algorithm (MOGA). The simulation results show that the MOEDA performs better than MOGA, and verify the effectiveness and robustness of the proposed method in optimization of contract distribution.

关键词： Contract distribution multi-objective optimization problem Estimation of distribution algorithm Genetic algorithm

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A problem-specific Constrained multi-objective Evolutionary Algorithm 3

A Problem-specific Constrained Multi-objective Evolutionary ...

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3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Dong, Jun-jie Li, He-cheng Huang, Jing Qinghai Normal Univ Sch Math & Stat Xining Qinghai Peoples R China

ISBN: (纸本)9781538684818

multi-objective optimization problem is a kind of problem optimizing simultaneously several conflicting objectives and keeping a balance between the diversity and the convergence of solutions. In this paper, some novel techniques are designed to improve the efficiency of multi-objective evolutionary algorithms. Firstly, fitness in keeping with feasibility is designed to choose the individual with good feasibility as much as possible;secondly, a specific sub-function is separated from a series of objectives, which is applied to provide an approximate search direction and speed the convergence of the algorithm. Thirdly, differential evolution is used to make the population search to the optimal solutions;then, the crowding degree scheme, as in NSGA-II, is used to select potential promising solutions in the process of iterations such that Pareto front is uniform as much as possible. Finally, a novel multi-objective evolutionary algorithm is presented by embedding these schemes. The simulation illustrates the effectives of the proposed algorithm.

关键词： multi-objective optimization problem evolutionary algorithm problem information non-dominated solution

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