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• 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]

• 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]

Highlights • A new type of preference relations, LIFPRs, is introduced. • A natural additive consistency concept for LIFPRs is defined. • Models to judge the consistency and derive additively consistent LIFPRs are built. • Model to determine missing values is constructed. • A new method is provided that can address inconsistent and incomplete ILFPRs. Abstract Linguistic intuitionistic fuzzy sets can be regarded as a qualitative form of intuitionistic fuzzy sets. This type of fuzzy sets uses a linguistic membership degree and a linguistic non-membership degree to represent the qualitative preferred and non-preferred judgments of decision makers. Preference relation is a useful and efficient tool for decision making that only requires the decision makers to compare two objects at one time. Taking the advantages of linguistic intuitionistic fuzzy sets and preference relations, this paper introduces linguistic intuitionistic fuzzy preference relations (LIFPRs) and studies their application to decision making. To ensure the ranking of objects reasonably, an additive consistency concept is introduced, and several of its desirable properties are discussed. To cope with inconsistent and incomplete LIFPRs, programming model-based methods to derive additively consistent LIFPRs and determine missing values are constructed, respectively. Subsequently, an approach to multi-criteria decision making with LIFPRs is offered, and the application of the new approach is illustrated by using a decision-making problem about evaluating mobile phones. [ABSTRACT FROM AUTHOR]

Abstract This paper focuses on preference relations with interval-valued hesitant fuzzy variables, namely, interval-valued hesitant fuzzy preference relations (IVHFPRs). This type of preference relations permits the decision makers to apply several intervals in [0, 1] to denote their uncertain hesitancy preferences, and the numbers of intervals in different interval-valued hesitant fuzzy variables may be different. This endows the decision makers with more flexibility to express their preferences for compared objects. To calculate the priority weight vector from IVHFPRs logically, this paper defines an additive consistency concept for IVHFPRs. Then, 0–1 mixed programming models are constructed to judge the additive consistency of IVHFPRs, and a method for deriving additively consistent IVHFPRs is introduced. Using the additive consistency based probabilistic distribution, two methods for calculating the priority weight vector from additively consistent IVHFPRs are proposed. In virtue of the second method, we further discuss group decision making with IVHFPRs. To do this, a group consensus index is defined, and an approach to increase the consensus level is offered. After that, a group decision-making method with incomplete and inconsistent IVHFPRs is presented. Finally, two practical examples are selected to show the application of the new method, and comparison analysis is also made. [ABSTRACT FROM AUTHOR]

In order to make use of the advantages of optical computing and promote the application and popularization of Ternary Optical Computer (TOC), this paper proposes a new computing-data model, presents the corresponding computing-data file in detail, and gives the implementation method of programming application with the model files. It tries to set up a bridge between users and TOC. Experimental results show that the model presented in this paper is correct and the implementation method is feasible. The model can better simplify TOC’s application process, and allow users to apply TOC to carry out various computational applications without needing to understand any details of optical implementation. [ABSTRACT FROM AUTHOR]

Abstract Data-Flow Threads (DF-Threads) is a new execution model that permits to seamlessly distribute the workload across several cores (in a multi-core) and several nodes (in a multi-node/multi-board configuration). In this paper, the advance in deploying this execution model is shown while developing it by using a combination of a simulator model (i.e., the COTSon framework) and a reconfigurable hardware platform (i.e., the AXIOM-board). The AXIOM platform consists of a custom board based on the Xilinx Zynq Ultrascale+ ZU9EG, which incorporates the largest FPGA available on that System-on-Chip at the moment, four 64-bit ARM cores and two 32-bit ARM cores, up to 32GiB of main memory and several 16Gbit/s transceivers. While a complete DF-Threads system is still under development, but is already capable of running a full Linux OS and simple applications, so some initial results are presented here. In particular, well-known programming models that are used to exploit the Thread-Level Parallelism such as Cilk, OpenMPI and Jump are compared with DF-thread execution. Cilk is good for multi-cores, but it is not suitable for multi-nodes systems. In the latter cases, the distribution of the workload could be managed partly by the programmer when using programming models such as message-passing (OpenMPI has been chosen for reference) or distributed shared-memory (Jump in our case). The obtained results show that a DF-Thread execution on a cluster of eight 4-core boards can provide a speed-up of more than 14x compared to the same configuration when using OpenMPI and more than 80x when compared with a OpenMPI single core, single node execution. [ABSTRACT FROM AUTHOR]

Abstract The Thick Control Flow (TCF) model simplifies parallel programming by bundling computations with the same control flow into single flows of variable thickness, and has the prospect of alleviating redundant usage of software and hardware resources. While architectures that can support the TCF model have been proposed, current proposals cannot support concurrent memory accesses that can both simplify programming and speed up many parallel algorithms by a logarithmic factor. In this paper, we extend current TCF architectures to efficiently support concurrent read as well as write memory accesses. The solution is based on bounded size step-caches, and exploit the two-part, hybrid, frontend-backend structure of current TCF processors, and synchronization properties of the TCF model itself. According to our simulation-based evaluation, a concurrent memory access TCF processor with B backends can execute algorithms with substantial concurrent memory accesses up to B times faster than a baseline TCF processor not supporting concurrent memory access. The hardware overhead of the solution is estimated to be modest. We include parallel program code to illustrate the gains by supporting concurrent memory accesses. [ABSTRACT FROM AUTHOR]

The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated. [ABSTRACT FROM AUTHOR]

• Considering the dynamic reference in prospect theory to reflect the idea of DMs. • Measuring the consensus degree by the variance of the prospect value. • Considering both subjective adjustment opinion and uncertain evaluation in CRP. • Obtaining the consensus evaluation with the minimum account rules. • Considering the effect of endo-confidence on weight of DM. 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. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPSs) are widely used in many applications that require interactions between humans and their physical environment. These systems usually integrate a set of hardware-software components for optimal application execution in terms of performance and energy consumption. The AXIOM project (Agile, eXtensible, fast I/O Module), presented in this paper, proposes a hardware-software platform for CPS coupled with an easy parallel programming model and sufficient connectivity so that the performance can scale-up by adding multiple boards. AXIOM supports a task-based programming model based on OmpSs and leverages a high-speed, inexpensive communication interface called AXIOM-Link. The board also tightly couples the CPU with reconfigurable resources to accelerate portions of the applications. As case studies, AXIOM uses smart video surveillance, and smart home living applications. [ABSTRACT FROM AUTHOR]

This study incorporates demand emission sensitivity into a programming model to explore the issue of decoupling the lock-in effect between economic growth and CO 2 emissions through structure adjustment from a final demand perspective. As a demonstration, the proposed methodology is applied to data from China for the year 2012, the most relevant productive linkages and the most sensitive components of final demand in terms of CO 2 emissions were identified. The results indicate that to formulate better carbon mitigation policies, we should pay more attention to the main internal linkages in terms of CO 2 emissions within the economy. [ABSTRACT FROM AUTHOR]

Triangular fuzzy reciprocal preference relations (TFRPRs) are good choices to denote the decision makers' fuzzy preferences. Consistency analysis is a very important topic to preference relations that can avoid the illogical ranking order. However, all previous multiplicative consistency concepts for TFRPRs cannot well address this type of preference relations. To address this issue, this paper introduces a new multiplicative consistency concept for TFRPRs, which can be seen as a natural extension of Tanino's multiplicative consistency concept for reciprocal preference relations. Based on the new consistency concept, 0–1 mixed programming models for judging the multiplicative consistency of TFRPRs are constructed, and an approach for deriving multiplicative consistent TFRPRs is provided. Considering the incomplete case, programming models to determine missing values are constructed. Subsequently, a multiplicative consistency based method for decision making with TFRPRs is presented that can address the inconsistent and incomplete cases. Meanwhile, illustrative examples are provided to show the concrete application of the proposed procedure, and comparison analysis is also made. [ABSTRACT FROM AUTHOR]

A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator. [ABSTRACT FROM AUTHOR]

This work focuses on the development of a high-performance fourth-order finite-volume method to solve the nonlinear partial differential equations governing the compressible Navier–Stokes equations on a Cartesian grid with adaptive mesh refinement. The novelty of the present study is to introduce the loop chaining concept to this complex fourth-order fluid dynamics algorithm for significant improvement in code performance on parallel machines. Specific operations involved in the algorithm include the finite-volume formulation of fourth-order spatial discretization stencils and optimal inter-loop parallelization strategies. Numerical fluxes of the Navier–Stokes equations comprise the hyperbolic (inviscid) and elliptic (viscous) ​components. The hyperbolic flux is evaluated using high-resolution Godunov’s method and the elliptic flux is based on fourth-order centered-difference methods everywhere in the computational domain. The use of centered-difference methods everywhere supports the idea of fusing modular codes to achieve high efficiency on modern computers. Temporal discretization is performed using the standard fourth-order ​Runge–Kutta method. The fourth-order accuracy of solution in space and time is verified with a transient Couette flow problem. The algorithm is applied to solve the Sod’s shock tube and the transient flat-plate boundary layer flow. The numerical predictions are validated by comparing to the analytical solutions. The performance of the baseline code is compared to that of the fused scheme which fuses modular codes via loop chaining concept and a significant improvement in execution time is observed. [ABSTRACT FROM AUTHOR]

The current trend in development of parallel programming models is to combine different well established models into a single programming model in order to support efficient implementation of a wide range of real world applications. The dataflow model has particularly managed to recapture the interest of the research community due to its ability to express parallelism efficiently. Thus, a number of recently proposed hybrid parallel programming models combine dataflow and traditional shared memory models. Their findings have influenced the introduction of task dependency in the OpenMP 4.0 standard. This article presents DaSH – the first comprehensive benchmark suite for hybrid dataflow and shared memory programming models. DaSH features 11 benchmarks, each representing one of the Berkeley dwarfs that capture patterns of communication and computation common to a wide range of emerging applications. DaSH also includes sequential and shared-memory implementations based on OpenMP and Intel TBB to facilitate easy comparison between hybrid dataflow implementations and traditional shared memory implementations based on work-sharing and/or tasks. Finally, we use DaSH to evaluate three different hybrid dataflow models, identify their advantages and shortcomings, and motivate further research on their characteristics. [ABSTRACT FROM AUTHOR]

Over the last years, the number of IoT devices has grown exponentially, highlighting the current Cloud infrastructure limitations. In this regard, Fog and Edge computing began to move part of the computation closer to data sources by exploiting interconnected devices as part of a single heterogeneous and distributed system in a computing continuum viewpoint. Since these devices are typically heterogeneous in terms of performance, features, and capabilities, this perspective should encompass programming models and run-time management layers. This work presents and evaluates the BarMan open-source framework by implementing a Fog video surveillance use-case. BarMan leverages a task-based programming model combined with a run-time resource manager and the novel BeeR framework to deploy the application's tasks transparently. This enables the possibility of considering aspects related to the energy and power dissipated by the devices and the single application. Moreover, we developed a task allocation policy to maximize application performance, considering run-time aspects, such as load and connectivity, of the time-varying available devices. Through an experimental evaluation performed on a real cluster equipped with heterogeneous embedded boards, we evaluated different execution scenarios to show the framework's functionality and the benefit of a distributed approach, leading up to an improvement of 66% on the frame processing latency w.r.t. a monolithic solution. • A run-time managed framework for Fog computing and resource continuum, from Cloud to IoT. • Dynamic and performance-aware task allocation policy. • Experimental evaluation on a real test-bed with an integrated self-developed video surveillance use-case. • Enabling energy-efficient management of distributed computing devices in Fog computing. • 66% of improvement in the execution times w.r.t. to a monolithic execution. [ABSTRACT FROM AUTHOR]

The improvements in semiconductor technologies are gradually enabling extreme-scale systems such as teradevices (i.e., chips composed by 1000 billion of transistors), most likely by 2020. Three major challenges have been identified: programmability, manageable architecture design, and reliability. TERAFLUX is a Future and Emerging Technology (FET) large-scale project funded by the European Union, which addresses such challenges at once by leveraging the dataflow principles. This paper presents an overview of the research carried out by the TERAFLUX partners and some preliminary results. Our platform comprises 1000+ general purpose cores per chip in order to properly explore the above challenges. An architectural template has been proposed and applications have been ported to the platform. Programming models, compilation tools, and reliability techniques have been developed. The evaluation is carried out by leveraging on modifications of the HP-Labs COTSon simulator. [ABSTRACT FROM AUTHOR]

Abstract: Current and future embedded manycore systems bring complex and heterogeneous architectures with a large number of processing cores, making both parallel programming at this scale and understanding the architecture itself a daunting task. Process Networks and other dataflow based Models of Computation (MoC) are a good base to present a universal model of the underlying manycore architectures to the programmer. If a language exposes a simple to grasp MoC in a consistent way across architectures, the programmer can concentrate the efforts on optimizing the expression of parallelism in the application instead of porting code to a given system. In this paper, we present a process network extension to C called τ C and its mapping to both a POSIX target and the P2012/STHORM platform, and show how the language offers an architecture independent solution of this problem. [Copyright &y& Elsevier]

Abstract: In mobile computing scenarios, users move in different environments and applications can automatically explore their surroundings. This type of context-aware application is an emerging trend, so far not widely disseminated. We believe that the proposal of a programming model specifically designed for the specification of context-aware applications, using general abstractions, will foster a rapidly widespread of such kind of software. In this article, we propose the use of the Holoparadigm (in short Holo) to fill this gap. We created a programming language based on Holo (called HoloL) to demonstrate the strengths of Holoparadigm to develop context-aware applications. The article uses a sample scenario to show the Holo''s practical aspects related to implementation of context-aware applications. We also present a platform to HoloL (called HoloEnv), composed of a compiler, a virtual machine, and a distributed execution environment. Finally, different performance aspects of the HoloEnv''s components were evaluated through experimental results. [Copyright &y& Elsevier]

Abstract: Advances in communications and embedded systems have led to the proliferation of wireless sensor and actuator networks (WSANs) in a wide variety of application domains. One important key of many such WSAN applications is the needed to meet non-functional requirements (e.g., lifetime, reliability, time guarantees) as well as functional ones (e.g. monitoring, actuation). Some application domains even require that sensor nodes be deployed in harsh environments (e.g., refineries), where they can fail due to communication interference, power problems or other issues. Unfortunately, the node failures can be catastrophic for critical or safety related systems. State machines can offer a promising approach to separate the two concerns – functional and non-functional – bringing forth reliability exception conditions handling, by means of fault handling states. We develop an approach that allows users to define and program typical applications using their platform language, but also adds state machine logic to design, view and handle explicitly other concerns such as reliability. The experimental section shows a working deployment of this concept in an industrial refinery setting [Copyright &y& Elsevier]

Abstract: An asynchronous one-sided message-passing programming (AOMP) model is provided in a in a P2P-based high performance computing system, P2HP-2. Here the instructions to parallelize applications with AOMP in P2HP-2 are presented in this paper. Therefore, the characteristics of applications are first formulized, where those with static datadependency can currently be applied in P2HP-2. Then the application are programmed with AOMP as a project with simple program multiple data (SPMD), where the procedure is processed as pretreatment, distributed computing, and post-treatment. Utilizing the application programming interfaces (APIs) of AOMP, the project is programmed and can be submitted to P2HP-2. Finally, a parallel serial threading algorithm with static data-dependency is created with AOMP. Through the experiments and analysis, great parallel efficiency has been achieved with the AOMP in P2HP-2, especially for those applications with static data-dependency [Copyright &y& Elsevier]

Abstract: High performance computing with low cost machines becomes a reality with GPU. Unfortunately, high performances are achieved when the programmer exploits the architectural specificities of the GPU processors: he has to focus on inter-GPU communications, task allocations among the GPUs, task scheduling, external memory prefetching, and synchronization. In this paper, we propose and evaluate a compile flow. It automates the transformation of a program expressed with the high level system design language SystemC, to its implementation on a cluster of multi-GPU. SystemC constructs and scheduler are directly mapped to the GPU API, preserving their semantic. Inter-GPU communications are abstracted by means of SystemC channels. [ABSTRACT FROM AUTHOR]

Abstract: Decreasing dimensions is the key of dynamic comprehensive evaluation problems, which turns multidimensional time series to two-dimensional data. This article extends the ordered weighted averaging (OWA) operator to aggregating data in dynamic comprehensive evaluation problems and presents two new operator (time ordered weighted averaging (TOWA) operator and time ordered weighted geometric averaging (TOWGA) operator). Then, two new methods are presented, which are twice-weighted evaluation and prefix comprehensive evaluation. And the article introduces the mathematical programming model to solve TOWA and TOWGA weighting vector (time weighting vector). Finally, an applied example was given to illustrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Reactors on buildings (RB) can achieve carbon sequestration through photoelectrocatalytic reaction to effectively control the carbon emission of buildings, while flexible solar cells (FSCs) can realize self-sufficiency in energy consumption of buildings without affecting their exteriors. It is supposed that they can be assembled together on building surface to achieve the ultra-low energy consumption and ultra-low carbon emission of buildings. Based on principles in solid geometry, calculation method for solar radiation on random spatial surface is proposed, and the programming model for the assembly of RB and FSCs on building surface targeted by environmental effect and economic benefit is established based on their working characteristics. Then a comprehensive programming model considering both environmental effect and economic benefit is established. Subsequently, main parameters in comprehensive programming model are analyzed through virtual experiment conducted on computers with input data available for Guangzhou, China, and the approximate equation of the curve of assembly ratio with weight as well as the concept of function characterization of buildings are proposed. Meanwhile, the equation that can be used to predict the assembly ratio of FSCs based on photoelectrocatalytic efficiency is established. All these studies provide a systematic method for the combined application of RB and FSCs in buildings. • Reactors on buildings (RB) developed in previous study and flexible solar cells (FSCs) are reviewed. • A systematic design method for green buildings based on the combined system of RB and FSCs is established. [ABSTRACT FROM AUTHOR]

This paper proposes a novel framework providing a declarative interface to access real-time process scheduling services available in an operating system kernel. The main idea is to let applications declare their temporal requirements or characteristics without knowing exactly which underlying scheduling algorithms are offered by the system. The proposed framework can adequately handle such a set of heterogeneous requirements configuring the platform and partitioning the requests among the available multitude of cores, so to exploit the various scheduling disciplines that are available in the kernel, matching application requirements in the best possible way. The framework is realized with a modular architecture in which different plugins handle independently certain real-time scheduling features. The architecture is designed to make its behavior customization easier and enhance the support for other operating systems by introducing and configuring additional plugins. [ABSTRACT FROM AUTHOR]

• A transformation function to keep the symmetry of preferences. • Consider individual problems and portray the mutual measures among information. • Log-normal distribution-based increase model and TOPSIS-entropy-based programming model. • Comparison analyses and simulation experiment. This paper provides a multi-stage medical scheme selection process to obtain the suitable scheme in the referral system. Firstly, considering the uncertainty of the experts and physicians, the preference relations (PRs) under the probabilistic linguistic term environment are introduced to the system. Then, this paper checks the consistency of the PRs and selects out the inconsistent PRs. For the inconsistent one, this paper provides alternative evaluations by repairing the most inconsistent element. Then, let experts choose from the alternative evaluation set. Next, by introducing the Bonferroni mean (BM) operator and Choquet integral, this paper integrates the historical data and the evaluations with stage weights to obtain the comprehensive assessment. The weights are calculated by the provided discrete and continuous stage weight functions or the programming model. Finally, the medical scheme selection process for the lung cancer and its simulation experiment are investigated to demonstrate the effectiveness and application of provided methods. The simulation experiments on sensitivity analysis and comparative analysis with existing methods are also conducted to validate the provided method. [ABSTRACT FROM AUTHOR]

• This paper focuses on the parking unpunctuality when optimizing the allocation of shared parking slots; • The parking probability functions of consumers and owners in different time slots are developed; • Several propositions are conducted to analyze the properties of the optimal allocation; • When considering the parking unpunctuality, the allocation is less compact. An effective way to solve the parking difficulty is to utilize idle private parking slots as temporary parking supplies under the shared parking mode. A platform-based management approach can make reasonable use of parking slots by gathering the parking time windows of owners and consumers in advance. However, the actual parking time is difficult to be consistent with that registered on the platform. Owners and consumers may be unpunctual when arriving and departing. The parking unpunctuality is a stochastic factor and affects the allocation of parking slots. This paper focuses on how to model and optimize the parking unpunctuality in the shared parking slot allocation problem. We formulate the parking probability function of consumers and owners in different time slots and transform the stochastic programming model into an expectation model. It is found that in a certain time slot, the optimal number of idle parking slot reserved is related to the probability of unpunctuality, as well as the number of cars registered to arrive and depart in the neighboring time slots. The numerical experiments indicate that when considering the parking unpunctuality, the allocation is less compact; the satisfaction of consumers is decreased, especially when the supply is limited or the consumer demands are excessive. In the middle and later periods throughout the daytime, parking slots are more likely to be overloaded. [ABSTRACT FROM AUTHOR]