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Consistency and consensus improvement models driven by a personalized normalization method with probabilistic linguistic preference relations

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INFORMATION FUSION 2021年 69卷 156-176页

作者： Tian, Zhang-peng Nie, Ru-xin Wang, Jian-qiang China Univ Min & Technol Sch Econ & Management Xuzhou 221116 Peoples R China Cent South Univ Sch Business Changsha 410083 Peoples R China

Probabilistic linguistic preference relation (PLPR) provides an effective and flexible tool with which preference degrees of decision-makers can be captured when they vacillatingly express linguistic preference values among several linguistic terms. Individual consistency and group consensus are two important research topics of PLPRs in group decision making (GDM). Considering the problems associated with these two topics, this study proposes a novel GDM framework with consistency-driven and consensus-driven optimization models based on a personalized normalization method for managing complete and incomplete PLPRs. First, existing limitations of the traditional normalization method for probabilistic linguistic term sets (PLTSs) managing ignorance information are specifically discussed. Given the potential valuable information hidden in PLTSs, a personalized normalization method is newly proposed through a two-stage decision-making process with a comprehensive fusion mechanism. Then, based on the proposed normalization method for PLTSs, consistency-driven optimization models that aim to minimize the overall adjustment amount of a PLPR are constructed to improve consistency. Moreover, the developed models are extended to improve consistency and estimate the missing values of an incomplete PLPR. Subsequently, a consensus-driven optimization model that aims to maximize group consensus by adjusting experts? weights is constructed to support the consensus-reaching process. Finally, an illustrative example, followed by some comparative analyses is presented to demonstrate the application and advantages of the proposed approach.

关键词： Group decision making Probabilistic linguistic term set programming model Incomplete probabilistic linguistic preference relation

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Two-stage multi-sided matching dispatching models based on improved BPR function with probabilistic linguistic term sets

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第1期12卷 151-169页

作者： Li, Bo Xu, Zeshui Zhang, Yixin Sichuan Univ Inst Disaster Management & Reconstruct Chengdu 610207 Peoples R China Sichuan Univ Sch Business State Key Lab Hydraul & Mt River Engn Chengdu 610064 Sichuan Peoples R China Univ Manchester Dept Comp Sci Manchester M13 9PL Lancs England

Disaster has great destructiveness and a wide influence on urban and rural construction, and more than one place is affected by disaster. Thus, it needs to take multiple disaster points into account. In view of different rescue missions, both medical rescue teams and search rescue teams also need to be dispatched. That is the multi-sided matching among medical rescue teams, search rescue teams and disaster points. Firstly, we describe the matching process, including the related symbols used in the process, and define a concept called multi-sided matching. Then, we aim to solve two problems: determining the competency degree of rescuers and calculating the time reliability of rescue teams arriving at disaster points. For the first one, we invite experts to evaluate pending rescue teams in terms of professional ability and collaboration ability using probabilistic linguistic term sets (PLTSs) because PLTSs not only keep original linguistic information but also give distributed expressions. For the second one, we determine the arriving time and calculating the time reliability by using the improved Bureau of Public Road (BPR) function. After that, we construct the two-stage multi-sided matching programming models based on the improved BPR function and PLTSs. Finally, a case study is used to demonstrate the proposed matching process, some comparative analyses and discussions are also conducted to validate the proposed models.

关键词： Multi-sided matching Bureau of public road (BPR) programming model Probabilistic linguistic term set

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Self-tuning serverless task farming using proactive elasticity control (vol 24, pg 799, 2021)

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第4期24卷 3855-3855页

作者： Kehrer, Stefan Zietlow, Dominik Scheffold, Jochen Blochinger, Wolfgang Reutlingen Univ Parallel & Distributed Comp Grp Alteburgstr 150 D-72762 Reutlingen Germany Max Planck Inst Intelligent Syst Autonomous Learning Grp Max Planck Ring 4 D-72076 Tubingen Germany

The cloud evolved into an attractive execution environment for parallel applications, which make use of compute resources to speed up the computation of large problems in science and industry. Whereas Infrastructure as a Service (IaaS) offerings have been commonly employed, more recently, serverless computing emerged as a novel cloud computing paradigm with the goal of freeing developers from resource management issues. However, as of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other and benefit from on-demand and elastic compute resources as well as per-function billing. In this work, we discuss how to employ serverless computing platforms to operate parallel applications. We specifically focus on the class of parallel task farming applications and introduce a novel approach to free developers from both parallelism and resource management issues. Our approach includes a proactive elasticity controller that adapts the physical parallelism per application run according to user-defined goals. Specifically, we show how to consider a user-defined execution time limit after which the result of the computation needs to be present while minimizing the associated monetary costs. To evaluate our concepts, we present a prototypical elastic parallel system architecture for self-tuning serverless task farming and implement two applications based on our framework. Moreover, we report on performance measurements for both applications as well as the prediction accuracy of the proposed proactive elasticity control mechanism and discuss our key findings.

关键词： Cloud computing Parallel computing Function-as-a-service Parallel cloud programming Elasticity programming model

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A new method for deriving priority from dual hesitant fuzzy preference relations

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第11期36卷 6613-6644页

作者： Tang, Jie Meng, Fanyong Pedrycz, Witold Fujita, Hamido Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn 219 Ningliu Rd Nanjing 210044 Jiangsu Peoples R China Cent South Univ Sch Business Changsha Hunan Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Ho Chi Minh City Univ Technol HUTECH Fac Informat Technol Ho Chi Minh City Vietnam Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada Spain Iwate Prefectural Univ Fac Software & Informat Sci Takizawa Iwate Japan

Dual hesitant fuzzy elements (DHFEs) are suitable to express hesitant possible preferred and nonpreferred judgments of decision makers. Preference relation is an important tool in decision making that only needs the decision makers to compare a pair of objects at one time. This study focuses on decision making with dual hesitant fuzzy preference relations (DHFPRs). Considering the consistency, an additive consistency concept is defined. Meanwhile, the property of the new concept is studied. Using this consistency concept, a method for assessing the additive consistency of DHFPRs is offered. To extend the application of DHFPRs, a programming model to determine the missing DHFEs in incomplete DHFPRs is built, which have the highest additive consistency level for the known ones. Two equivalent methods to calculate the priority vector are offered. One method obtains the probabilistic dual hesitant fuzzy priority vector, and the other derives the intuitionistic fuzzy priority vector. Furthermore, a consensus index is defined to measure the consensus of individual opinions in group decision making (GDM), and an interactive method for increasing the consensus level is offered. On the basis of the additive consistency and consensus, an algorithm to GDM with DHFPRs is offered that can address inconsistent and incomplete cases. Finally, a practical example about evaluating color TV is provided to demonstrate the usefulness of the new procedure.

关键词： additive consistency consensus analysis DHFPR group decision making programming model

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A Group Decision Making Method with Interval-Valued Intuitionistic Fuzzy Preference Relations and Its Application in the Selection of Cloud Computing Vendors for SMEs

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INFORMATICA 2021年第1期32卷 163-193页

作者： Zhang, Shaolin Tang, Jie Meng, Fanyong Yuan, Ruiping Cent South Univ Sch Business Changsha 410083 Peoples R China Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China

To solve the problem of choosing the appropriate cloud computing vendors in small and medium-sized enterprises (SMEs), this paper boils it down to a group decision making (GDM) problem. To facilitate the judgment, this paper uses preference relation as the decision making technology. Considering the situation where uncertain positive and negative judgments exist simultaneously, interval-valued intuitionistic fuzzy preference relations (IVIFPRs) are employed to express the decision makers' judgments. In view of the multiplicative consistency and consensus analysis, a new GDM algorithm with IVIFPRs is offered. To accomplish this goal, a new multiplicative consistency is first defined, which can avoid the limitations of the previous ones. Then, a programming model is built to check the consistency of IVIFPRs. To deal with incomplete IVIFPRs, two programming models are constructed to determine the missing values with the goal of maximizing the level of multiplicative consistency and minimizing the total uncertainty. To achieve the minimum adjustment of original preference information, a programming model is established to repair inconsistent IVIFPRs. In addition, programming models for getting the decision makers (DMs)' weights and improving the consensus degree are offered. Finally, a practical decision making example is given to illustrate the effectiveness of the proposed method and to compare it with previous methods.

关键词： group decision making IVIFPR multiplicative consistency programming model consensus

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A group decision making method with intuitionistic triangular fuzzy preference relations and its application

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APPLIED INTELLIGENCE 2021年第4期51卷 2556-2573页

作者： Zhang, Shaolin Meng, Fanyong Cent South Univ Sch Business Changsha 410083 Peoples R China

This paper aims to offer an intuitionistic triangular fuzzy group decision making method by preference relations. For this purpose, the concept of intuitionistic triangular fuzzy preference relations (ITFPRs) is first offered. Then, an additive consistency concept for ITFPRs is introduced. Meanwhile, a programming model is built to check the consistency of ITFPRs. Considering the case where incomplete ITFPRs are obtained, two programming models are constructed, which aim at maximizing the consistency and minimizing the uncertainty of missing information. To achieve the goals of the minimum total adjustment and the smallest number of adjusted elements, two programming models are established to repair inconsistent ITFPRs. In addition, the weights of decision makers are considered, and the consensus levels of individual ITFPRs are studied to ensure the representativeness of decision results. When individual ITFPRs do not meet the consensus requirement, a programming model to reach the consensus threshold is constructed, which permits different intuitionistic triangular fuzzy variables (ITFVs) to have different adjustments and minimizes the total adjustment. Finally, a group decision making algorithm with ITFPRs is proposed, and its feasibility and efficiency are demonstrated through an example of evaluating the intelligent traditional Chinese medicine decocting centers.

关键词： Group decision making ITFPR Additive consistency programming model Consensus

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Performance Portability Study of Epistasis Detection using SYCL on NVIDIA GPU 22

Performance Portability Study of Epistasis Detection using S...

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13th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Jin, Zheming Vetter, Jeffrey S. Oak Ridge Natl Lab POB 2008 Oak Ridge TN 37830 USA

ISBN: (纸本)9781450393867

We describe the experience of converting a CUDA implementation of a high-order epistasis detection algorithm to SYCL. The goals are for our work to be useful to application and compiler developers with a detailed description of migration paths between CUDA and SYCL. Evaluating the CUDA and SYCL applications on an NVIDIA V100 GPU, we find that the optimization of loop unrolling needs to be applied manually to the SYCL kernel for obtaining comparable performance. The performance of the SYCL group reduce function, an alternative to the CUDA warp-based reduction, depends on the problem and work group sizes. The 64-bit popcount operation implemented with tree of adders is slightly faster than the built-in popcount operation. When the number of OpenMP threads is four, the highest performance of the SYCL and CUDA applications are comparable.

关键词： portability programming model GPU epistasis

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An Iterated Greedy Algorithm for a Parallel Machine Scheduling Problem with Re-entrant and Group Processing Features

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第4期30卷 1241-1252页

作者： Yuan, Shuaipeng Wang, Bailin Li, Tike Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China

This research paper addresses a novel parallel machine scheduling problem with re-entrant and group processing features, specifically motivated by the hot milling process in the modern steel manufacturing industry. The objective is to minimize the makespan. As no existing literature exists on this problem, the paper begins by analyzing the key characteristics of the problem. Subsequently, a mixed integer linear programming model is formulated. To tackle the problem, an improved iterated greedy algorithm (IGA) is proposed. The IGA incorporates a problem-specific heuristic to construct the initial solution. Additionally, it incorporates an effective destruction and reconstruction procedure. Furthermore, an acceptance rule is developed to prevent the IGA from getting stuck in local optima. The proposed approach is evaluated through computational experiments. The results demonstrate that the proposed IGA outperforms three state-of-the-art meta-heuristics, highlighting its high effectiveness. Overall, this research contributes to the understanding and solution of the parallel machine scheduling problem with re-entrant and group processing features in the context of the hot milling process. The proposed algorithm provides insights for practical applications in the steel manufacturing industry.

关键词： group processing iterated greedy algorithm parallel machine scheduling programming model re-entrant

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A consensus model under framework of prospect theory with acceptable adjustment and endo-confidence

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INFORMATION FUSION 2023年第1期97卷

作者： Tian, Xiaoli Li, Wanqing Li, Lanhao Kou, Gang Ye, Chen Southwestern Univ Finance & Econ Fac Business Adm Sch Business Adm Chengdu 611130 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Southwest Minzu Univ Sch Econ Chengdu 611130 Peoples R China

Consensus is an important issue in group decision making to make a reliable and scientific decision, and it has become a hot topic recently. Due to the complexity and uncertainty of decision-making problems, several aspects of alternatives should be considered in decision-making process. Also, the bounded rational characteristics of decision makers (DMs) may significantly affect the decision results. This paper proposes a multi-criteria consensus model based on prospect theory (PT) to consider the unsymmetrical risk attitudes of DM for gain or loss and on endo-confidence (confidence which is derived by DMs' evaluation information) levels of DMs to determine their weights. First, the reference point in PT is selected and how to measure the prospect consensus degree is presented. Then, the way to identify the expert who need to modify his/her evaluation is given and two programming models are proposed to obtain the updated evaluation. Subsequently, both the overall difference and the endo-confidence level of the DM are used to determine the weights of DMs. Finally, an illustrative example and the comparative analysis are used to demonstrate the feasibility of the proposed model.

关键词： Consensus programming model Prospect theory Reference point Endo -confidence level

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BSPCloud: A Hybrid Distributed-memory and Shared-memory programming model

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2013年第1期6卷 87-97页

作者： Liu, Xiaodong Tong, Weiqin ZhiRen, Fu WenZhao, Liao Shanghai Univ Sch Comp Engn & Sci Shanghai 200072 Peoples R China China Telecom Corp Ltd Shanghai 200085 Peoples R China

ISBN: (纸本)3642128130

programming models for cloud computing has been a research focus recently. Current programming models for cloud computing mainly focus on improving the efficiency of the cloud computing platforms but little has been done on the performance predictability of models. This paper presents a cloud computing programming model, called BSPCloud. The BSPCloud has the advantages of predictable performance. BSPCloud uses a hybrid of distributed-memory and shared-memory bulk synchronous parallel (BSP) programming model. Computing tasks are first divided into a set of coarse granularity bulks which are computed by the distributed-memory BSP model, and each coarse granularity bulk is further divided into a set of bulk threads which are computed by the shared-memory BSP model. BSPCloud makes full use of the multi-core architecture, and more importantly, the performance of the BSPCloud is predictable. We have implemented a proof-of-concept BSPCloud parallel programming library in java. We give the implementation of BSPCloud parallel programming library and show how the library implements hybrid programming. We detail applying the BSPCloud library on matrix multiplication. The performance predictability and speedup are evaluated in the cloud platform. We give the results of experiments.

关键词： Cloud programming programming model BSPCloud Bulk synchronous parallel

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