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Most practical decision-making problems commonly contain several alternatives evaluated by more than one decision maker (DM) according to the built criteria system. It is known as multi-criteria group decision making problems. The decision-making results should be based on highly similar individual opinions to derive the most suitable choice. Nevertheless, the divergence of individual judgments is usually significant due to various subjective and objective reasons. In this case, some DMs need to modify their judgments to derive final results, the consensus adjustment in group decision making. Like supply chain management, the centralized consensus process can minimize individual adjustments under the set consensus condition. Based on the hypothesis of rational humans that individuals always pursue maximum benefits, this paper proposes an iterative consensus process in a centralized environment. According to non-consensus adjustments, the iterative procedure contains non-transferable and transferable two parts. When the non-transferable consensus adjustment procedure is completed, the transferable consensus adjustment procedure is activated using an asymmetrical Nash bargaining game allocation mechanism. Additionally, a case study is provided to demonstrate the efficaciousness and practicality of the new method. Meanwhile, sensitivity and comparison analyses are done. [ABSTRACT FROM AUTHOR]

Heterogeneous computing has been developing continuously in the field of high-performance computing because of its high performance and energy efficiency. More and more accelerators have emerged, such as GPU, FPGA, DSP, AI accelerator, and so on. Usually, the accelerator is connected to the host CPU as a peripheral device to form a tightly coupled heterogeneous computing node, and then, a parallel system is constructed by multiple nodes. This organization is computationally efficient, but not flexible. When new accelerators appear, it is difficult to join the system that has been built. At the hardware level, we create an array of accelerators and connect them to the existing system through a high-speed network. At the software level, we dynamically organize computing resources from various arrays to build a virtual heterogeneous computing node. This approach also includes a standard programming environment. Therefore, it is a more flexible, elastic, and scalable heterogeneous computing organization. In this paper, a supernode OpenCL implementation is proposed for hybrid parallel computing systems, in which virtual supernodes can be dynamically constructed between different computing arrays, and a standard OpenCL environment is implemented based on RDMA communication of high-speed interconnection, which can be combined with the system-level MPI programming environment, thereby realizing the large-scale parallel computing of the hybrid array. SNCL is compatible with existing MPI/OpenCL programs without the need for additional modifications. Experiments show that the runtime overhead of the supernode OpenCL environment is very low, and it is suitable for deploying applications with high computing density and large data scale between different arrays to utilize their computing power without affecting scalability. [ABSTRACT FROM AUTHOR]

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Summary: Task‐based systems have become popular due to their ability to utilize the computational power of complex heterogeneous systems. A typical programming model used is the Sequential Task Flow (STF) model, which unfortunately only supports static task graphs. This can result in submission overhead and a static task graph that is not well‐suited for execution on heterogeneous systems. A common approach is to find a balance between the granularity needed for accelerator devices and the granularity required by CPU cores to achieve optimal performance. To address these issues, we have extended the STF model in the StarPU runtime system by introducing the concept of hierarchical tasks. This allows for a more dynamic task graph and, when combined with an automatic data manager, it is possible to adjust granularity at runtime to best match the targeted computing resource. That data manager makes it possible to switch between various data layout without programmer input and allows us to enforce the correctness of the DAG as hierarchical tasks alter it during runtime. Additionally, submission overhead is reduced by using large‐grain hierarchical tasks, as the submission process can now be done in parallel. We have shown that the hierarchical task model is correct and have conducted an early evaluation on shared memory heterogeneous systems using the Chameleon dense linear algebra library. [ABSTRACT FROM AUTHOR]

In this communication we aim to provide an overview of Earth observation and remote sensing in archaeology following a non-traditional literature review approach, that is, investigating recent developments in artificial intelligence (AI) and language models. Towards this direction, the generative pre-trained transformer (ChatGPT) language model was used to extract relevant information. The ChatGPT language model—recently released by OpenAI—appears to provide an alternative way for retrieving comprehensive information for various thematic topics. ChatGPT is currently operated on a beta version by millions of users worldwide, free of access for a limited period. In this study, specific queries related to Earth observation and remote sensing in archaeology were made by the authors to the ChatGPT. Innovations and limitations are discussed, while a comparison with traditional bibliographic analysis is performed.

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.

Objective: To analyze how supply control measures allow to face the volatility of cucumber prices in Mexico. Methodology: A programming model applied to the cucumber market in 2020 was formulated and three scenarios on control and planning the planted area were analyzed. Results: Indicate that in 2020 the production and commercialization of 371 thousand tons of cucumber generated an income and a profit for the producer of 1,914 and 721 million pesos. A 30 % contraction in the annual supply would increase the unit profit from 1,945 to 2,102 pesos per ton. Limitations: Since 98.8 % of Mexican cucumber exports are destined for the US market, shipments of the vegetable to other countries were not considered. Conclusions: Due to the positive effects of supply control through surface planning as a measure to deal with price volatility, the creation of a producer organization is recommended to make possible the organization and planning of the surface planted with vegetables. [ABSTRACT FROM AUTHOR]

Certain workloads such as in‐memory databases are inherently hard to scale‐out and rely on cache‐coherent scale‐up non‐uniform memory access (NUMA) systems to keep up with the ever‐increasing demand for compute resources. However, many parallel programming frameworks such as OpenMP do not make efficient use of large scale‐up NUMA systems as they do not consider data locality sufficiently. In this work, we present PGASUS, a C++ framework for NUMA‐aware application development that provides integrated facilities for NUMA‐aware task parallelism and data placement. The framework is based on an extensive review of parallel programming languages and frameworks to incorporate the best practices of the field. In a comprehensive evaluation, we demonstrate that PGASUS provides average performance improvements of 1.56× and peak performance improvements of up to 4.67×across a wide range of workloads. [ABSTRACT FROM AUTHOR]

• Uncertain self-confidence enables DMs with more flexibly. • A new clustering method based on ranking position deviation is offered. • Decision-making weights are defined by ordinal and cardinal indices. • The new method performs ordinal and cardinal consensus analysis simultaneously. Consensus analysis is necessary for large-scale group decision making (LSGDM) for ensuring reasonable decision results. This paper offers a new method for LSGDM with uncertain self-confidence that follows ordinal-cardinal consensus analysis. For this purpose, a new interval ranking method is first proposed to compare alternatives. Then, an improved ordinal clustering method on criteria is introduced using the deviation between individual ranking positions. In view of opinion deviation, uncertain self-confidence deviation, and ranking deviation, the weights of decision makers (DMs) are defined. Similarly, the weights of clusters are determined by further combining cluster cardinality. Further, an ordinal-cardinal consensus procedure is offered, which contains two algorithms: the first algorithm is about the ordinal consensus improvement in view of three aspects: ranking adjustment, opinion adjustment, and uncertain self-confidence; the second algorithm studies the cardinal consensus improvement under the ordinal consensus requirement, which also mainly contains three aspects: opinion adjustment, the number of adjusted judgments, and uncertain self-confidence. Moreover, a new algorithm for LSGDM is presented. Finally, an example is provided to check the feasibility and efficiency of the new method, and a comparison analysis is also made. [ABSTRACT FROM AUTHOR]

In this paper, a stable two-sided matching (TSM) method considering the matching intention of agents under a hesitant fuzzy environment is proposed. The method uses a hesitant fuzzy element (HFE) as its basis. First, the HFE preference matrix is transformed into the normalized HFE preference matrix. On this basis, the distance and the projection of the normalized HFEs on positive and negative ideal solutions are calculated. Then, the normalized HFEs are transformed into agent satisfactions. Considering the stable matching constraints, a multiobjective programming model with the objective of maximizing the satisfactions of two-sided agents is constructed. Based on the agent satisfaction matrix, the matching intention matrix of two-sided agents is built. According to the agent satisfaction matrix and matching intention matrix, the comprehensive satisfaction matrix is set up. Furthermore, the multiobjective programming model based on satisfactions is transformed into a multiobjective programming model based on comprehensive satisfactions. Using the G-S algorithm, the multiobjective programming model based on comprehensive satisfactions is solved, and then the best TSM scheme is obtained. Finally, a terminal distribution example is used to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this communication we aim to provide an overview of Earth observation and remote sensing in archaeology following a non-traditional literature review approach, that is, investigating recent developments in artificial intelligence (AI) and language models. Towards this direction, the generative pre-trained transformer (ChatGPT) language model was used to extract relevant information. The ChatGPT language model—recently released by OpenAI—appears to provide an alternative way for retrieving comprehensive information for various thematic topics. ChatGPT is currently operated on a beta version by millions of users worldwide, free of access for a limited period. In this study, specific queries related to Earth observation and remote sensing in archaeology were made by the authors to the ChatGPT. Innovations and limitations are discussed, while a comparison with traditional bibliographic analysis is performed. [ABSTRACT FROM AUTHOR]

The field of edge computing has grown considerably over the past few years, with applications in artificial intelligence and big data processing, particularly due to its powerful accelerators offering a large amount of hardware parallelism. As the computing power of the latest edge systems increases, applications of edge computing are being expanded to areas that have traditionally required substantially high-performant computing resources such as scientific computing. In this paper, we review the latest literature and present the current status of research for implementing high-performance computing (HPC) on edge devices equipped with parallel accelerators, focusing on software environments including programming models and benchmark methods. We also examine the applicability of existing approaches and discuss possible improvements necessary towards realizing HPC on modern edge systems. [ABSTRACT FROM AUTHOR]

Unified programming models can effectively improve program portability on various heterogeneous high-performance computers. Existing unified programming models put a lot of effort to code portability but are still far from achieving good performance portability. In this paper, we present a preliminary design of a performance-portable unified programming model including four aspects: programming language, programming abstraction, compilation optimization, and scheduling system. Specifically, domain-specific languages introduce domain knowledge to decouple the optimizations for different applications and architectures. The unified programming abstraction unifies the common features of different architectures to support common optimizations. Multi-level compilation optimization enables comprehensive performance optimization based on multi-level intermediate representations. Resource-aware lightweight runtime scheduling system improves the resource utilization of heterogeneous computers. This is a perspective paper to show our viewpoints on programming models for emerging heterogeneous systems. [ABSTRACT FROM AUTHOR]

The R&D (Research & Development) process of mobile application can be regarded as a complex system engineering that needs to take into account a variety of influencing elements, such as customer’s demands, engineering measures and business goals etc., as well as their interplay. A novel decision support platform is proposed to assist project managers in determining the optimal technical measures with implementation degrees and resource allocation scheme. Specially, the HDQ (House of Development Quality) is designed to collect essential data and information as well as reflect correlations among elements, which can effectively connect isolated information islands. Through the transform of HDQ, a multi-objective programming model is constructed to meet customers’ overall demands and specific requirements. A combination of specified technical measures and resource allocation can be obtained under ideal conditions. Additionally, the extended model considering resource substitution is designed to realize further improvement by effectively utilizing the surplus resource in the above optimal condition, demonstrating the advantage of proper managerial arrangement and resource deployment. Finally, the R&D case of family financial management application is conducted to illustrate the effectiveness of the suggested method, which offers a new perspective on mobile application R&D project management.

For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processingsystem designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.

Existing GPU graph analytics frameworks are typically built from specialized, bottom-up implementations of graph operators that are customized to graph computation. In this work we describe Mini-Gunrock, a lightweight graph analytics framework on the GPU. Unlike existing frameworks, Mini-Gunrock is built from graph operators implemented with generic transform-based data-parallel primitives. Using this method to bridge the gap between programmability and high performance for GPU graph analytics, we demonstrate operator performance on scale-free graphs with an average 1.5x speedup compared to Gunrock’s corresponding operator performance. Mini-Gunrock’s graph operators, optimizations, and applications code have 10x smaller code size and comparable overall performance vs. Gunrock.

Optimal cropping pattern of Maragheh-Bonab plain was determined in different decision priorities (from viewpoint of farmers and experts) and annually water availability scenarios [60, 80 and 100 MCM (million cubic meters)/year]. Goal programming model was applied to the optimization of cropping pattern with considering eight goals. In the optimal cropping pattern from viewpoint of farmers, water consumption was reduced compared to existing condition about 42%, 32%, and 3% at 60, 80, and 100 MCM/year scenarios, respectively. While, from viewpoint of experts, this reduction was about 55%, 32%, and 32% for 60, 80, and 100 MCM/year scenarios, respectively. At 60 MCM/year scenario, total net return was calculated lower than the existing cropping pattern about 11% and 45% from the viewpoint of farmers and experts, respectively. However, optimization at 80 and 100 MCM/year scenarios, increased total net return compared to existing condition about 14% and 24% from the viewpoint of farmers and 14% and 14% from the viewpoint of experts, respectively. In the optimal cropping patterns, despite lower water consumption, the amount of net return, economic water productivity, economic energy, and land productivity increased against a decrease in employment, global warming potential, and environmental impacts of pesticides in the whole area. [ABSTRACT FROM AUTHOR]

The agricultural water resource distribution is usually associated with many uncertainties related to incomplete historical records, seasonal and demand fluctuations, and complex relationships between water resources and environmental and economic factors. These uncertainties aggravate the complexity of agricultural water resource allocation and of achieving economic objectives. Therefore, an uncertain fuzzy set programming (UFSP) model for agricultural water-system planning in Shuyang town was developed. The uncertain factors affecting the allocation of agricultural water resources, including agricultural income and developmental and sewage treatment costs, are represented by fuzzy sets. The results show that the total local agricultural economic income of Shuyang town can be increased from (23.77, 25.57) to (33.12, 39.67) billion yuan in three stages corresponding to the increase in land for planting, animal husbandry, and forestry and the development of agricultural ecotourism. Furthermore, the study compared the results from the three methods, as well as from the UFSP, UFSP-ß, and UFSP-U models. The comparison shows that the UFSP model has more advantages in predicting the total agricultural economic income than the other models, which reflects the uncertainty of the local agricultural water resource system and, consequently, leads to greater economic benefits. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• We design a personalized normalization method to manage PLTSs. • We construct programming models to estimate the missing values of incomplete PLPRs. • We construct programming models to improve the consistency of PLPRs.. • We construct programming models to support the CRP with PLPRs. 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. [ABSTRACT FROM AUTHOR]

In mixed critical systems, applications with different security and time criticality share computing resources. Suffered from kinds of uncertainties in the system, designers need a design method with tight timing that can satisfy multiple design constraints such as function behavior certainty, timing behavior predictability and high computing performance at the same time, which challenges the theories and methods of existing computer architectures and programming languages. A real-time machine model, RTM, and a time triggered instruction set, TTI, which support time semantics, are proposed as the important foundation in constructing a Multi-tier Tight Timing design method, MTTT. A helicopter flight control program is used as an example to illustrate the effectiveness of RTM and TTI instruction set. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, the edge computing paradigm has been attracting much attention in the Internet-of-Things domain. It aims to push the frontier of computing applications, data, and services away from the usually centralized cloud servers to the boundary of the network. The benefits of this paradigm shift include better reactivity and reliability, reduced data transfer costs toward the centralized cloud servers, and enhanced confidentiality. The design of energy-efficient edge compute nodes requires, among others, low power cores such as microprocessors. Heterogeneous architectures are key solutions to address the crucial energy-efficiency demand in modern systems. They combine various processors providing attractive power and performance trade-offs. Unfortunately, no standard heterogeneous microcontroller-based architecture exists for edge computing. This paper deals with the aforementioned issue by exploring typical low power architectures for edge computing. Various heterogeneous multicore designs are developed and prototyped on FPGA for unbiased evaluation. These designs rely on cost-effective and inherently ultra-low power cores commercialized by Cortus SA, a world-leading semiconductor IP company in the embedded ultra-low power microcontroller domain. Some microarchitecture-level design considerations, e.g., floating point and out-of-order computing capabilities, are taken into account for exploring candidate solutions. In addition, a tailored and flexible multi-task programming model is defined for the proposed architecture paradigm. We analyze the behavior of various application programs on available core configurations. This provides valuable insights on the best architecture setups that match program characteristics, so as to enable increased energy-efficiency. Our experiments on multi-benchmark programs show that on average 22% energy gain can be achieved (up to 45%) compared to a reference system design, i.e., a system with the same execution architecture, but agnostic of the task management insights gained from the comprehensive evaluation carried out in this work.

The Map-Join-Reduce programming model effectively supports the join operation among different heterogeneous data sets by adding the Join module and processes the multiway joining directly. In this paper, we propose a rigorous description of the Map-Join-Reduce that performs as an executable specification. First, this paper briefly introduces the differences between the Map-Join-Reduce and the MapReduce. Then, we use Haskell to specify each module of the Map-Join-Reduce programming model and analyze the structure and function of each module. Finally, we test the specification by analyzing an example of the mall sales records. The executable specification contributes to helping the developers to unscramble the relationship between the MapReduce and the Map-Join-Reduce, which may serve as a basis for further development of the theory of related programming model design. Furthermore, the most important function of an executable specification is guaranteeing the target informal or semi-formal model with interesting properties. This paper is a forward step to prepare for verifying related properties and, even, providing verified prototypes.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]