CN113642808B - A dynamic scheduling method for cloud manufacturing resource changes - Google Patents

A dynamic scheduling method for cloud manufacturing resource changes Download PDF

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CN113642808B
CN113642808B CN202111018657.7A CN202111018657A CN113642808B CN 113642808 B CN113642808 B CN 113642808B CN 202111018657 A CN202111018657 A CN 202111018657A CN 113642808 B CN113642808 B CN 113642808B
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胡艳娟
吕文军
王占礼
张邦成
柳虹亮
李静
尹晓静
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Abstract

本发明提供一种面向云制造资源变化的动态调度方法,所属技术领域为先进制造技术领域。其特征在于该方法首先综合云服务平台的需求结合资源调度过程中可能会出现的资源属性变化、新资源接入、资源撤出等干扰情况,对云制造环境下资源的变化进行分析,构建面向云制造资源变化的动态调度模型,最后应用改进粒子群算法对资源的动态调度模型进行求解,以获得能够满足条件的云制造环境下的资源调度方案和实时的资源调度更新策略。本发明广泛应用于云制造平台下的加工制造服务企业,实现了对云制造环境下动态资源的调度优化,改善了制造企业成本高,资源负载不均衡等问题。

The invention provides a dynamic scheduling method for cloud manufacturing resource changes, and belongs to the technical field of advanced manufacturing technology. It is characterized in that the method firstly analyzes the resource changes in the cloud manufacturing environment by combining the needs of the cloud service platform with the interference of resource attribute changes, new resource access, and resource withdrawal that may occur during the resource scheduling process, and constructs a oriented The dynamic scheduling model of cloud manufacturing resource changes. Finally, the improved particle swarm optimization algorithm is used to solve the dynamic scheduling model of resources, so as to obtain the resource scheduling scheme and real-time resource scheduling update strategy in the cloud manufacturing environment that can meet the conditions. The present invention is widely applied to processing and manufacturing service enterprises under the cloud manufacturing platform, realizes the scheduling and optimization of dynamic resources in the cloud manufacturing environment, and improves the problems of high cost and unbalanced resource load of manufacturing enterprises.

Description

一种面向云制造资源变化的动态调度方法A dynamic scheduling method for cloud manufacturing resource changes

技术领域technical field

本发明涉及一种面向云制造资源变化的动态调度方法,该调度方法首先综合资源需要方和资源提供方共同上传至云服务平台的需求结合资源动态调度过程中可能会出现的资源属性变化、新资源接入、资源撤出以及资源维护的干扰情况,对云制造环境下资源调度的变化进行分析,然后根据制造任务和资源的属性特点以及实际的资源调度规则构建面向云制造资源变化的动态调度模型,最后应用改进粒子群算法对资源的动态调度模型进行求解,以获得能够满足条件的云制造环境下的资源调度方案和实时的资源调度更新策略。该发明属于先进制造技术领域。The present invention relates to a dynamic scheduling method oriented to changes in cloud manufacturing resources. The scheduling method first integrates the needs of the resource requester and the resource provider to upload to the cloud service platform together with the resource attribute changes that may occur in the process of resource dynamic scheduling. The interference of resource access, resource withdrawal and resource maintenance, analyze the changes in resource scheduling in the cloud manufacturing environment, and then construct a dynamic scheduling oriented to cloud manufacturing resource changes based on the attributes and characteristics of manufacturing tasks and resources as well as the actual resource scheduling rules Finally, the improved particle swarm optimization algorithm is used to solve the resource dynamic scheduling model to obtain the resource scheduling scheme and real-time resource scheduling update strategy in the cloud manufacturing environment that can meet the conditions. The invention belongs to the field of advanced manufacturing technology.

背景技术Background technique

当前世界范围内制造业的生产模式正在大规模的转向智能化制造,以及伴随着云计算、制造网格、互联网等新兴IT技术的高速发展,企业的生产和发展与网络的联系也日益密切。在此背景下,云制造的概念应运而生。云制造是一种面向服务的,高能低耗的网络化制造新模式,其融合了云计算、物联网、大数据、语义Web等IT技术,将互联网和制造业相结合充分发挥了互联网高效、信息共享、低损耗的特性。云制造作为一种网络化制造模式,能够将分散的各类制造资源和制造能力进行虚拟化,服务化后形成资源池,通过网络进行集中管理和经营,能够在提高资源利用率的同时降低企业的生产成本。在云制造的环境下,系统拥有着大量的高度动态的制造资源。如何高效、高质量的对资源进行动态调度是目前亟待解决的问题,当下传统的调度方法已经很难满足云制造资源动态调度的需要,这时一种新的更高效、更稳定的调度方法就显得极为重要。At present, the production mode of the manufacturing industry in the world is shifting to intelligent manufacturing on a large scale, and with the rapid development of cloud computing, manufacturing grid, Internet and other emerging IT technologies, the production and development of enterprises are increasingly connected with the network. In this context, the concept of cloud manufacturing came into being. Cloud manufacturing is a new service-oriented, high-energy and low-consumption networked manufacturing model. It integrates IT technologies such as cloud computing, Internet of Things, big data, and semantic Web. Features of information sharing and low loss. As a networked manufacturing model, cloud manufacturing can virtualize various scattered manufacturing resources and manufacturing capabilities, form a resource pool after service, and conduct centralized management and operation through the network, which can improve resource utilization and reduce enterprise costs. production cost. In the cloud manufacturing environment, the system has a large number of highly dynamic manufacturing resources. How to dynamically schedule resources with high efficiency and high quality is an urgent problem to be solved at present. The current traditional scheduling method has been difficult to meet the needs of dynamic scheduling of cloud manufacturing resources. At this time, a new more efficient and stable scheduling method is needed. appears extremely important.

发明内容Contents of the invention

本发明的目的就在于提供一种能够解决上述问题,能够在云制造环境下高效稳定的为资源需求方提供足够资源的动态调度方法。该方法能够有效的提高云制造服务平台的柔性,能够在有效完成任务的前提下保证最低的服务成本,最小的服务完成时间以及最高的资源可靠度和最优的资源整体服务效率。The purpose of the present invention is to provide a dynamic scheduling method that can solve the above problems and efficiently and stably provide sufficient resources for resource demanders in a cloud manufacturing environment. This method can effectively improve the flexibility of the cloud manufacturing service platform, and can guarantee the lowest service cost, the smallest service completion time, the highest resource reliability and the best resource overall service efficiency under the premise of effectively completing tasks.

本发明的技术方案为:Technical scheme of the present invention is:

本发明首先综合资源需要方和资源提供方共同上传至云服务平台的需求结合资源动态调度过程中可能会出现的资源属性变化、新资源接入、资源撤出以及资源维护的干扰情况,对云制造环境下资源调度的变化进行分析,然后根据制造任务和资源的属性特点以及实际的资源调度规则构建面向云制造资源变化的动态调度模型,最后应用改进粒子群算法对资源的动态调度模型进行优化求解,以获得能够满足条件的云制造环境下的资源调度方案和实时的资源调度更新策略。该方法的实现步骤包括:The present invention first integrates the needs of resource demanders and resource providers to upload to the cloud service platform together with the resource attribute changes, new resource access, resource withdrawal, and resource maintenance that may occur during the dynamic resource scheduling process. The changes in resource scheduling in the manufacturing environment are analyzed, and then a dynamic scheduling model for cloud manufacturing resource changes is constructed according to the attributes and characteristics of manufacturing tasks and resources as well as actual resource scheduling rules. Finally, the improved particle swarm optimization algorithm is used to optimize the dynamic scheduling model of resources Solve to obtain the resource scheduling scheme and real-time resource scheduling update strategy in the cloud manufacturing environment that can meet the conditions. The implementation steps of the method include:

(1)云制造资源的服务组合和动态性分析(1) Service composition and dynamic analysis of cloud manufacturing resources

在云制造环境下种类繁多,功能各异的制造资源以数据的形式储存于云平台中,其状态属性随着其服务进程实时发生动态变化。云制造系统通过资源分类技术可以将云制造资源按照功能或粒度进行分类,根据云平台中不同子任务对于资源需求不同,通过资源匹配技术实现不同类别资源进行组合共同完成制造任务。In the cloud manufacturing environment, there are many kinds of manufacturing resources with different functions stored in the cloud platform in the form of data, and their status attributes change dynamically in real time with the service process. The cloud manufacturing system can classify cloud manufacturing resources according to function or granularity through resource classification technology. According to the different resource requirements of different subtasks in the cloud platform, different types of resources can be combined to complete manufacturing tasks through resource matching technology.

(2)基于云制造资源变化的动态调度模型(2) Dynamic scheduling model based on changes in cloud manufacturing resources

基于云制造环境下资源具有分布性、多样性、动态性、抽样性的特征,结合云制造环境下资源动态属性分析的结果,给出了在云制造环境下资源动态调度约束的相关概念,构建了以总制造服务时间、总制造服务成本、制造资源平均可靠度、制造资源平均服务效率为优化目标的多目标优化模型。Based on the distribution, diversity, dynamics, and sampling characteristics of resources in the cloud manufacturing environment, combined with the results of the dynamic attribute analysis of resources in the cloud manufacturing environment, the related concepts of resource dynamic scheduling constraints in the cloud manufacturing environment are given and constructed. A multi-objective optimization model with total manufacturing service time, total manufacturing service cost, average reliability of manufacturing resources, and average service efficiency of manufacturing resources as optimization objectives is established.

(3)基于改进粒子群算法的资源动态调度优化求解(3) Resource dynamic scheduling optimization solution based on improved particle swarm optimization algorithm

在建立的面向云制造资源变化的动态调度模型的基础上,对调度中各类资源的变化情况进行编码,完成对资源调度问题的形式化描述。利用层次分析法计算调度方案中的服务需求权重,确定资源调度对不同服务的偏好,最后通过粒子群算法实现对资源调度问题的求解。On the basis of the established dynamic scheduling model for cloud manufacturing resource changes, the changes of various resources in scheduling are coded to complete the formal description of resource scheduling problems. The AHP is used to calculate the service demand weight in the scheduling scheme, and the resource scheduling preference for different services is determined. Finally, the resource scheduling problem is solved by the particle swarm optimization algorithm.

作为优选,上述步骤1云制造环境下的资源在定义角度下可分为广义资源和狭义资源,全程参与制造加工过程的设备称为狭义资源,产品从最初的设计到产品完成全生命周期中所有涉及的各类资源的总和称为广义资源。从资源粒度的角度可以分为单一功能的资源和复合功能的资源,单一功能资源具备单一高效的能力,复合功能的资源同时具备多种功能,综合服务能力强。所有资源以数据形式储存在云制造服务平台中。As a preference, the resources in the above step 1 cloud manufacturing environment can be divided into broad-sense resources and narrow-sense resources from the perspective of definition. The equipment that participates in the entire manufacturing process is called narrow-sense resources. The sum of all kinds of resources involved is called generalized resources. From the perspective of resource granularity, it can be divided into single-function resources and composite-function resources. Single-function resources have a single and efficient capability, and composite-function resources have multiple functions at the same time, with strong comprehensive service capabilities. All resources are stored in the cloud manufacturing service platform in the form of data.

云制造环境下资源的动态调度可以将制造过程看作是动态过程,在资源调度的全周期中会存在多种不同的扰动因素,这种扰动称作是资源的动态性。云制造环境下资源的动态性主要体现在以下角度:The dynamic scheduling of resources in the cloud manufacturing environment can regard the manufacturing process as a dynamic process. There will be many different disturbance factors in the whole cycle of resource scheduling. This disturbance is called the dynamic nature of resources. The dynamics of resources in the cloud manufacturing environment are mainly reflected in the following perspectives:

(1)资源属性变化,云制造环境下的资源会随着使用完成用户的评价发生属性的变化。在云制造服务制造周期的t1时刻,云制造系统中的初始资源集E1={E1,E2…Ei…El}综合服务能力因为初始任务集U1={F1,F2…Fi…Fn}用户的评价发生了变化,E1更新为E2服务于新的制造任务集/> (1) Changes in resource attributes. Resources in the cloud manufacturing environment will change their attributes as they are used and evaluated by users. At time t 1 of the cloud manufacturing service manufacturing cycle, the initial resource set E 1 ={E 1 ,E 2 ...E i ...E l } comprehensive service capabilities in the cloud manufacturing system because the initial task set U 1 ={F 1 ,F 2 ...F i ...F n } The user's evaluation has changed, E 1 is updated to E 2 , Serving a new set of manufacturing tasks />

(2)新资源接入,在资源调度全生命周期的t2时刻,云制造服务平台中接入El+1,El+2…El+c等新资源,与资源集构成新的资源集/> 服务于t2时刻还未完成的任务形成的新制造任务集/> (2) New resource access. At time t 2 of the resource scheduling life cycle, new resources such as E l+1 , E l+2 ... E l+c are connected to the cloud manufacturing service platform, and the resource set Form a new resource set /> A new manufacturing task set formed to serve the unfinished tasks at time t2 />

(3)资源维护,在云制造服务周期的t3时刻,云制造平台中Eb,Eb+1…Eb+q等资源发生故障需要进行维修,撤出此次云制造服务,因为维修所需时间不同,因此维修资源重新接入平台的时间也不同,云制造平台中资源集合由E3更新为E4,云制造服务平台根据更新后的资源集E4对t3时刻未完成的任务构成的新制造任务集合继续进行服务。(3) Resource maintenance. At time t3 of the cloud manufacturing service cycle, resources such as E b , E b+1 ... E b+q in the cloud manufacturing platform fail and need to be repaired, and the cloud manufacturing service is withdrawn because the maintenance The required time is different, so the time for maintenance resources to re-connect to the platform is also different. The resource set in the cloud manufacturing platform is updated from E 3 to E 4 , and the cloud manufacturing service platform compares the unfinished tasks at time t 3 according to the updated resource set E 4 A new set of manufacturing tasks composed of tasks Continue to serve.

(4)资源撤销,制造任务的执行进程推进至t4时刻,此时云制造平台中Ep,Ep+1,Ep+w等资源发生故障撤出此次云制造服务,云制造服务平台中的资源集合由E4更新为E5,撤销的资源后续不再参与调度服务,云制造服务平台根据更新后的资源E5对第四阶段任务集U4在t4时刻未完成的任务构成的新制造任务集合继续进行服务。(4) The resource is withdrawn, and the execution process of the manufacturing task advances to time t4 . At this time, resources such as E p , E p+1 , and E p+w in the cloud manufacturing platform fail and withdraw from the cloud manufacturing service. The cloud manufacturing service The resource set in the platform is updated from E 4 to E 5 , and the revoked resources will no longer participate in the scheduling service, and the cloud manufacturing service platform will perform the unfinished tasks of the fourth stage task set U 4 at time t 4 according to the updated resource E 5 A new set of manufacturing tasks Continue to serve.

在云制造环境下,由于存在多种扰动因素,因此资源的调度会呈现一定的动态性。为使云制造资源动态调度的过程能够顺利进行,特订立相关调度规则如下:In the cloud manufacturing environment, due to the existence of various disturbance factors, the scheduling of resources will present certain dynamics. In order to make the process of dynamic scheduling of cloud manufacturing resources go smoothly, the relevant scheduling rules are formulated as follows:

(1)每个制造资源都能够独立完成一项或几项二级制造任务,同一项二级制造任务在同一时刻只能被同一制造资源加工;(1) Each manufacturing resource can independently complete one or several secondary manufacturing tasks, and the same secondary manufacturing task can only be processed by the same manufacturing resource at the same time;

(2)一级制造任务被分解后形成二级制造任务,二级制造任务称为最小待服务单元;(2) The first-level manufacturing task is decomposed to form a second-level manufacturing task, and the second-level manufacturing task is called the smallest unit to be served;

(3)二级制造任务与下一个二级制造任务在不同的制造资源之间进行加工会产生一定的物流成本和物流时间;(3) The processing of the secondary manufacturing task and the next secondary manufacturing task between different manufacturing resources will generate certain logistics costs and logistics time;

(4)任务的服务方式按照二级制造任务的前后次序有序接受服务,后一项二级制造任务的开始时间需大于前一项二级制造任务结束时间和两个任务所选择资源之间的物流时间之和;(4) The service method of the task is to accept the service in an orderly manner according to the order of the secondary manufacturing tasks. The start time of the latter secondary manufacturing task must be greater than the end time of the previous secondary manufacturing task and the interval between the resources selected for the two tasks. The sum of logistics time;

(5)不同资源之间的物流总成本与物流时间和物流距离成正比;(5) The total cost of logistics between different resources is proportional to the logistics time and logistics distance;

(6)不同类型的一级制造任务有着相同的执行优先级;(6) Different types of primary manufacturing tasks have the same execution priority;

(7)在零时刻所有的云制造资源均可被使用;(7) All cloud manufacturing resources can be used at zero time;

(8)二级任务一旦开始加工直至其制造服务完成,期间不可中断。(8) Once the secondary task starts processing until its manufacturing service is completed, the period cannot be interrupted.

作为优选,上述步骤2中,确定了资源动态调度的基本目标,给出了在云制造环境下资源动态调度约束的概念,建立了多目标优化调度模型,将资源调度过程形式化表示,云制造环境下资源动态调度约束主要包括以下内容:Preferably, in the above step 2, the basic goal of resource dynamic scheduling is determined, the concept of resource dynamic scheduling constraints in the cloud manufacturing environment is given, a multi-objective optimal scheduling model is established, and the resource scheduling process is formally expressed. Cloud manufacturing The resource dynamic scheduling constraints in the environment mainly include the following contents:

(1)总制造服务时间约束,每个任务的执行时间不能超过用户所规定的最大完成时间即:T=Max(Tij_end)≤Tmax(i=1,2…n),T表示制造任务总时间,Tmax表示用户规定的最晚交货期。(1) The total manufacturing service time constraint, the execution time of each task cannot exceed the maximum completion time specified by the user, that is: T=Max(T ij_end )≤T max (i=1,2...n), T represents the manufacturing task The total time, T max represents the latest delivery date specified by the user.

(2)总制造服务成本约束,总制造服务成本不可超过用户总预算即:其中Cmax表示用户的总预算成本。(2) The total manufacturing service cost constraint, the total manufacturing service cost cannot exceed the user's total budget, namely: where C max represents the total budgeted cost of the user.

(3)资源平均可靠度约束,(3) resource average reliability constraint,

(4)制造资源平均服务效率约束,(4) Constraints on the average service efficiency of manufacturing resources,

(5)二级制造任务加工时序约束,任务的服务方式按照二级制造任务顺序接受服务,后置二级制造任务服务开始时间不可小于前置二级制造任务结束时间与物流时间之和即:(5) The processing time sequence of the secondary manufacturing task is constrained. The service method of the task is to accept the service according to the order of the secondary manufacturing task. The service start time of the subsequent secondary manufacturing task cannot be less than the sum of the end time of the preceding secondary manufacturing task and the logistics time:

其中,表示二级制造任务Fij的完成时间,/>表示二级制造任务Fij与Fi(j+1)所在资源之间的物流时间,/>表示二级制造任务Fi(j+1)的任务开始时间。in, Indicates the completion time of the secondary manufacturing task F ij , /> Indicates the logistics time between the secondary manufacturing task F ij and the resource where F i(j+1) is located, /> Indicates the task start time of the secondary manufacturing task F i(j+1) .

(6)子任务的粒度约束,任务被分解后形成的二级制造任务为加工最小单元粒度,每个制造资源都能独立完成一种或几种类型的二级制造任务,同一个二级制造任务在同一时刻不能被不同制造资源加工,即: (6) The granularity constraints of subtasks. The secondary manufacturing task formed after the task is decomposed is the smallest unit granularity of processing. Each manufacturing resource can independently complete one or several types of secondary manufacturing tasks. The same secondary manufacturing task Tasks cannot be processed by different manufacturing resources at the same time, namely:

本资源调度方法选取制造任务总制造服务时间,总制造服务成本,制造资源平均服务效率,制造资源可靠度作为资源动态调度模型的优化目标。主要内容有:This resource scheduling method selects the total manufacturing service time of manufacturing tasks, the total manufacturing service cost, the average service efficiency of manufacturing resources, and the reliability of manufacturing resources as the optimization objectives of the resource dynamic scheduling model. The main contents are:

(1)总制造服务时间是指制造服务开始一直到最后一个二级制造任务完成的总时间;(1) The total manufacturing service time refers to the total time from the start of the manufacturing service to the completion of the last secondary manufacturing task;

(2)总制造服务成本包括制造二级制造任务的制造加工成本以及相邻两个二级制造任务在不同服务资源之间的物流成本。云制造服务平台给用户提供的制造资源应该满足总制造服务成本最少的目标。(2) The total manufacturing service cost includes the manufacturing processing cost of manufacturing the secondary manufacturing task and the logistics cost between two adjacent secondary manufacturing tasks between different service resources. The manufacturing resources provided by the cloud manufacturing service platform to users should meet the goal of the least total manufacturing service cost.

(3)云制造环境下资源提供方的综合实力、设备服务能力、计算能力在内的多种因素都会对资源的服务效率产生影响,同时不同的资源服务于相同的任务时效率也会存在差异。(3) In the cloud manufacturing environment, various factors including the comprehensive strength of resource providers, equipment service capabilities, and computing capabilities will affect the service efficiency of resources. At the same time, different resources will have different efficiencies when serving the same task. .

(4)云制造环境下资源的可靠度作为资源的自身属性,取决于用户对资源的服务质量可靠和服务时间可靠的综合评价,资源的可靠度随着资源不断地参与制造,用户不断地评价动态更新。(4) The reliability of resources in the cloud manufacturing environment, as a resource's own attribute, depends on the user's comprehensive evaluation of the service quality and service time reliability of resources. As resources continue to participate in manufacturing, users continue to evaluate the reliability of resources. dynamic updates.

作为优选,云制造环境下基于资源变化的动态调度的多目标函数为:As a preference, the multi-objective function of dynamic scheduling based on resource changes in the cloud manufacturing environment is:

其中,f1(x)表示总制造服务时间目标函数,f2(x)表示总制造服务成本目标函数,f3(x)表示制造资源平均可靠度目标函数,f4(x)表示制造资源平均服务效率目标函数。Among them, f 1 (x) represents the objective function of total manufacturing service time, f 2 (x) represents the objective function of total manufacturing service cost, f 3 (x) represents the objective function of average reliability of manufacturing resources, and f 4 (x) represents the objective function of manufacturing resources Average service efficiency objective function.

作为优选,上述步骤3中,对调度中各类资源的变化情况进行编码,完成对资源调度问题的形式化描述。利用层次分析法计算调度方案中的服务需求权重,确定资源调度对不同服务的偏好,最后通过改进粒子群算法实现对资源调度问题的求解。Preferably, in the above step 3, the changes of various resources in the scheduling are coded to complete the formalized description of the resource scheduling problem. The AHP is used to calculate the service demand weight in the scheduling scheme, to determine the resource scheduling preference for different services, and finally to solve the resource scheduling problem by improving the particle swarm optimization algorithm.

作为优选,上述云制造环境下的资源调度包括资源层、一级制造任务层和二级制造任务层,其中资源层为二级制造任务层的可选资源集合。另外云制造资源在使用过程中也会发生诸如资源属性变化、新资源接入、资源撤出和资源维护的干扰事件影响资源调度的过程。本方法利用改进粒子群算法解决动态资源的调度问题,主要包括以下步骤:Preferably, the above-mentioned resource scheduling in the cloud manufacturing environment includes a resource layer, a primary manufacturing task layer and a secondary manufacturing task layer, wherein the resource layer is an optional resource set of the secondary manufacturing task layer. In addition, during the use of cloud manufacturing resources, interference events such as resource attribute changes, new resource access, resource withdrawal, and resource maintenance will also affect the process of resource scheduling. This method uses the improved particle swarm optimization algorithm to solve the scheduling problem of dynamic resources, and mainly includes the following steps:

(1)编码,本方法根据资源调度问题中可能会发生的干扰事件,以及相对应干扰事件的具体解决办法,在可行解的范围内初始化一群粒子,每个粒子都代表着资源动态调度问题的一个潜在最优解。(1) Coding. This method initializes a group of particles within the range of feasible solutions according to the interference events that may occur in the resource scheduling problem and the specific solutions to the corresponding interference events. Each particle represents the resource dynamic scheduling problem. a potentially optimal solution.

(2)适应度函数的构造,本方法采用线性加权的方式将多目标优化问题转化为单目标函数进行求解,在构造适应度函数时需满足制造时间和制造服务成本取最小值,平均制造服务效率和平均资源可靠度取最大值。(2) The construction of the fitness function. This method uses linear weighting to transform the multi-objective optimization problem into a single objective function for solution. When constructing the fitness function, it is necessary to satisfy the minimum value of manufacturing time and manufacturing service cost, and the average manufacturing service cost Efficiency and average resource reliability take the maximum value.

(3)迭代操作,迭代操作主要包括计算种群中各粒子的适应度值,对个体最优粒子和全局最优粒子进行更新,根据速度、位置更新公式对粒子的速度和位置进行更新。(3) Iterative operation, the iterative operation mainly includes calculating the fitness value of each particle in the population, updating the individual optimal particle and the global optimal particle, and updating the speed and position of the particle according to the speed and position update formula.

与现有方法相比,本发明方法的优点在于:以云制造环境下资源的动态变化为目标,对云制造资源在调度过程中可能会发生的诸如资源属性变化,资源损坏、维修,资源撤销问题进行了分析并给出了在云制造资源出现扰动事件后服务系统的调度策略,以保证云制造任务顺利完成。结合调度问题中主要的影响因素如制造服务时间,制造服务成本,资源平均可靠可靠度及资源平均服务效率等因素建立了多目标优化模型,采用改进粒子群优化算法对问题进行优化求解,有效的提高了云制造服务平台的柔性。能够在有效完成任务的前提下保证服务成本最低,服务完成时间最小以及最高的资源平均可靠度和最优的资源整体服务效率。Compared with the existing methods, the method of the present invention has the advantages of: targeting at the dynamic changes of resources in the cloud manufacturing environment, it can deal with the possible occurrence of cloud manufacturing resources during the scheduling process, such as resource attribute changes, resource damage, maintenance, and resource revocation The problem is analyzed and the scheduling strategy of the service system after the cloud manufacturing resource disturbance event is given to ensure the smooth completion of the cloud manufacturing task. Combined with the main factors in the scheduling problem, such as manufacturing service time, manufacturing service cost, resource average reliability and resource average service efficiency, a multi-objective optimization model was established, and the improved particle swarm optimization algorithm was used to optimize and solve the problem, effectively Improve the flexibility of the cloud manufacturing service platform. Under the premise of effectively completing tasks, it can guarantee the lowest service cost, the shortest service completion time, the highest average resource reliability and the best resource overall service efficiency.

附图说明Description of drawings

图1是本发明中资源变化情况关系图Fig. 1 is a relationship diagram of resource changes in the present invention

图2是本发明中基于资源变化的改进粒子群算法的资源调度全阶段流程图Fig. 2 is the whole-stage flow chart of the resource scheduling of the improved particle swarm optimization algorithm based on resource changes in the present invention

具体实施方式Detailed ways

下面将结合附图对本发明作进一步说明。The present invention will be further described below in conjunction with accompanying drawing.

实施例1:一种面向云制造资源变化的动态调度方法,如图1所示,资源动态调度流程的基本内容包括:Embodiment 1: a kind of dynamic scheduling method for cloud manufacturing resource change, as shown in Figure 1, the basic content of resource dynamic scheduling process includes:

(1)云制造资源的服务组合和动态性分析,在云制造环境下云制造资源的状态属性随着其服务进程实时发生动态变化。云平台需要根据资源状态属性的变化实时调整调度策略。(1) The service composition and dynamic analysis of cloud manufacturing resources. In the cloud manufacturing environment, the state attributes of cloud manufacturing resources change dynamically in real time with the service process. The cloud platform needs to adjust the scheduling strategy in real time according to the changes of resource status attributes.

(2)基于云制造资源变化的动态调度模型,结合云制造环境下资源动态属性的研究成果,给出在云制造环境下资源动态调度约束的相关概念,构建多目标优化调度模型。(2) Based on the dynamic scheduling model of cloud manufacturing resource changes, combined with the research results of resource dynamic attributes in cloud manufacturing environment, the related concepts of resource dynamic scheduling constraints in cloud manufacturing environment are given, and a multi-objective optimal scheduling model is constructed.

(3)基于改进粒子群算法的资源动态调度优化求解,在上述动态调度模型的基础上,利用层次分析法计算调度方案中的服务需求权重,确定资源调度对不同服务的偏好,最后通过粒子群算法实现对资源调度问题的求解。(3) Resource dynamic scheduling optimization solution based on the improved particle swarm optimization algorithm. On the basis of the above dynamic scheduling model, use the AHP to calculate the service demand weight in the scheduling plan, determine the resource scheduling preference for different services, and finally pass the particle swarm The algorithm implements the solution to the resource scheduling problem.

如图2所示,为基于改进粒子群算法的资源调度全阶段流程,具体的优化调度求解步骤如下:As shown in Figure 2, it is the whole-stage process of resource scheduling based on the improved particle swarm optimization algorithm. The specific optimization scheduling solution steps are as follows:

(1)初始化粒子种群,设置种群粒子的初始位置、初始速度、迭代次数。(1) Initialize the particle population, and set the initial position, initial velocity, and number of iterations of the population particles.

(2)分别计算在单独考虑制造时间时总制造服务时间的最小值Tmin,在单独考虑总制造服务成本时制造服务成本的最小值Cmin,在单独考虑制造资源平均可靠度时平均可靠度的最大值Relmax,在单独考虑资源平均服务效率时资源服务效率的最大值Emax,然后将各单独目标进行归一化处理,得到归一化后的适应度函数,最后计算种群中各粒子的适应度值。(2) Calculate the minimum value T min of the total manufacturing service time when considering the manufacturing time alone, the minimum value C min of the manufacturing service cost when considering the total manufacturing service cost alone, and the average reliability when considering the average reliability of manufacturing resources separately The maximum value Rel max of the resource service efficiency is the maximum value E max of the resource service efficiency when the average service efficiency of the resource is considered separately, and then each individual target is normalized to obtain the normalized fitness function, and finally the particle in the population is calculated fitness value.

(3)对个体最优粒子和全局最优粒子进行更新。(3) Update the individual optimal particle and the global optimal particle.

(4)根据位置公式和速度公式对粒子的位置和速度进行更新。(4) Update the particle's position and velocity according to the position formula and velocity formula.

(5)判断是否达到最大迭代次数,若达到最大迭代次数,结束返回此时刻已经完成的任务和其对应的资源编号。若没有达到最大迭代次数,返回步骤2。(5) Judging whether the maximum number of iterations has been reached, and if the maximum number of iterations has been reached, return the tasks that have been completed at this moment and their corresponding resource numbers. If the maximum number of iterations is not reached, return to step 2.

(6)对最优个体进行解码,转换为调度结果。(6) Decode the optimal individual and convert it into a scheduling result.

(7)保存步骤6中在t=t1时刻还未开始的制造任务集。(7) Save the manufacturing task set that has not started at the time t=t 1 in step 6.

(8)E1,E2···Ei等资源属性变化更新。(8) E 1 , E 2 ···E i and other resource attributes are changed and updated.

(9)对新的制造任务集和更新后的资源进行数据处理,然后按照步骤1,步骤2,步骤3,步骤4,步骤5进行操作。(9) Perform data processing on the new manufacturing task set and the updated resource, and then operate according to step 1, step 2, step 3, step 4, and step 5.

(10)对最优个体进行解码,转换为调度结果;(10) Decode the optimal individual and convert it into a scheduling result;

(11)保存步骤10中在t=t2时刻还未开始的制造任务集。(11) Save the manufacturing task set that has not started at the time t=t 2 in step 10.

(12)E1+1,E1+2···E1+c等新资源接入云平台。(12) New resources such as E 1+1 , E 1+2 ···E 1+c are connected to the cloud platform.

(13)对上步骤保存的新制造任务集和资源进行数据处理,然后按照步骤1,步骤2,步骤3,步骤4,步骤5进行操作。(13) Perform data processing on the new manufacturing task set and resources saved in the previous step, and then operate according to step 1, step 2, step 3, step 4, and step 5.

(14)对最优个体进行解码,转换为调度结果;(14) Decode the optimal individual and convert it into a scheduling result;

(15)保存步骤14中在t=t3时刻还未开始的制造任务集。(15) Save the manufacturing task set that has not started at the time t= t3 in step 14.

(16)Eb,Eb+1···Eb+q等资源进行维护。(16) E b , E b+1 ··· E b+q and other resources are maintained.

(17)对上步骤保存的新制造任务集和资源进行数据处理,然后按照步骤1,步骤2,步骤3,步骤4,步骤5进行操作。(17) Perform data processing on the new manufacturing task set and resources saved in the previous step, and then operate according to step 1, step 2, step 3, step 4, and step 5.

(18)对最优个体进行解码,转换为调度结果;(18) Decode the optimal individual and convert it into a scheduling result;

(19)保存步骤18中在t=4时刻还未开始的制造任务集。(19) Save the manufacturing task set that has not started at time t=4 in step 18.

(20)Ep,Ep+1···Ep+w等资源撤出云平台。(20) Resources such as E p , E p+1 ··· E p+w are withdrawn from the cloud platform.

(21)对上步骤保存的新制造任务集和资源进行数据处理,然后按照步骤1,步骤2,步骤3,步骤4,步骤5进行操作。(21) Perform data processing on the new manufacturing task set and resources saved in the previous step, and then operate according to step 1, step 2, step 3, step 4, and step 5.

(22)对最优个体进行解码,转换为调度结果;(22) Decode the optimal individual and convert it into a scheduling result;

(23)存储已完成的制造任务及其对应的资源编号,输出全局调度方案。(23) Store the completed manufacturing tasks and their corresponding resource numbers, and output the global scheduling scheme.

本文以某云制造资源调度场景为例,基于层次分析法对调度方案中的服务需求权重进行如下步骤的求解:In this paper, taking a cloud manufacturing resource scheduling scenario as an example, based on the analytic hierarchy process, the service demand weight in the scheduling scheme is solved by the following steps:

(1)构造层次模型,基于对层次分析法进行权重求解的原理,结合本方法研究对象构建以最佳资源调度方案为目标层,总制造服务时间、总制造服务成本、平均制造服务效率、资源平均可靠度为指标因素层的阶梯层次模型。(1) Construct a hierarchical model, based on the principle of solving the weight of the AHP, combined with the research object of this method to construct the optimal resource scheduling plan as the target layer, the total manufacturing service time, total manufacturing service cost, average manufacturing service efficiency, resource The average reliability is a step-level model of the index factor layer.

(2)本方法依据层次分析法的基本原理和求解步骤,对调度模型中的影响因素采用层次分析法对权重进行求解。在本实例中假定总制造服务成本与总制造服务时间同等重要,总制造服务时间比平均制造服务效率稍微重要,两者比资源平均可靠度稍微重要,制造服务平均效率与资源平均服务效率同等重要,构造初始判断矩阵A=(aij)n×n如下。(2) This method is based on the basic principles and solving steps of the AHP, and uses the AHP to solve the weights of the influencing factors in the scheduling model. In this example it is assumed that total manufacturing service cost is equally important as total manufacturing service time, total manufacturing service time is slightly more important than average manufacturing service efficiency, both are slightly more important than resource average reliability, and average manufacturing service efficiency is equally important as resource average service efficiency , construct the initial judgment matrix A=(a ij ) n×n as follows.

(3)计算权重(3) Calculate the weight

对初始判断矩阵A进行归一化处理得到标准矩阵如下,Normalize the initial judgment matrix A to get the standard matrix as follows,

中的元素按行相加并进行归一化处理,得到最终权向量/> right The elements in are added by row and normalized to get the final weight vector />

W=[0.333,0.333,0.167,0.167]T W=[0.333,0.333,0.167,0.167] T

计算判断矩阵A的最大特征值λmaxCalculate the maximum eigenvalue λ max of the judgment matrix A,

λmax=4λ max =4

对所求权重进行一致性检验,Perform a consistency check on the weights sought,

根据下表可知在n=4时,平均随机一致性指标RI=0.90,According to the table below, when n=4, the average random consistency index RI=0.90,

CR=0.0237≤0.1CR=0.0237≤0.1

综上所述,所求权重符合一致性检验,本实例所求权重为:To sum up, the required weight conforms to the consistency test, and the required weight in this example is:

W=[0.333,0.333,0.167,0.167]T W=[0.333,0.333,0.167,0.167] T

基于改进粒子群算法的资源动态调度,调度算法的具体操作步骤如下:Based on the dynamic scheduling of resources based on the improved particle swarm optimization algorithm, the specific operation steps of the scheduling algorithm are as follows:

(1)编码(1) Coding

本发明对粒子采用实数编码和整数编码,适应度函数采用线性加权模式,各指标权重值得计算采用层次分析法进行求解,使待服务的任务和资源构成初始方案解空间实现对调度问题的求解。例如,制造任务集U的任务类型及二级制造任务类型列表为:The invention adopts real number encoding and integer encoding for particles, linear weighting mode is adopted for fitness function, and the calculation of each index weight value is solved by analytic hierarchy process, so that tasks and resources to be served constitute the initial solution space to solve the scheduling problem. For example, the list of task types and secondary manufacturing task types of manufacturing task set U is:

即存在n个一级制造任务时,每个一级制造任务Fi都可被分解为ki个二级制造任务Fij,粒子的第一层编码表示二级任务制造层,通过1到n之间的整数进行赋值,可表示为[1 11 1 1 2 2 2 2 2],粒子的第二层编码表示二级任务执行顺序层,初始可通过赋[0,1]之间的随机实数完成,可表示为[0.3 0.4 0.5 0.2 0.6 0.7 0.42 0.53 0.62 0.27],第三层编码表示每个二级制造任务对应分配的加工资源编号层,可表示为[1 3 5 2 4 3 1 2 4 3];最终粒子编码表示为:That is, when there are n first-level manufacturing tasks, each first-level manufacturing task F i can be decomposed into k i second-level manufacturing tasks F ij . The integers between are assigned, which can be expressed as [1 11 1 1 2 2 2 2 2]. The second layer of particle encoding represents the second-level task execution sequence layer. Initially, it can be assigned a random real number between [0,1] Completed, it can be expressed as [0.3 0.4 0.5 0.2 0.6 0.7 0.42 0.53 0.62 0.27], the third layer of coding indicates the processing resource number layer assigned to each secondary manufacturing task, which can be expressed as [1 3 5 2 4 3 1 2 4 3]; the final particle code is expressed as:

(2)适应度函数的构造(2) Construction of fitness function

根据目标函数的定义,即满足制造时间和制造服务成本取最小值时,平均制造服务效率和平均资源可靠度取最大值,对于改进粒子群算法采用单目标函数作为适应度函数对粒子进行评价选择,因此需要把多目标优化问题转化为单目标函数的形式进行求解。According to the definition of the objective function, that is, when the manufacturing time and manufacturing service cost are minimized, the average manufacturing service efficiency and average resource reliability are maximized. For the improved particle swarm optimization algorithm, a single objective function is used as the fitness function to evaluate and select particles , so it is necessary to transform the multi-objective optimization problem into the form of a single objective function for solution.

其中,W1表示总制造服务时间,W2表示总制造服务成本,W3表示制造资源平均服务效率,W4表示制造资源平均可靠度的权重值;Tmin表示当仅考虑制造服务时间时制造服务时间的最小值,Cmin表示当仅考虑制造服务成本时服务成本的最小值,Emax表示当仅考虑平均制造服务效率时制造服务效率的最大值,Relmax表示当仅考虑平均资源可靠度时平均资源可靠度的最大值。Among them, W 1 represents the total manufacturing service time, W 2 represents the total manufacturing service cost, W 3 represents the average service efficiency of manufacturing resources, W 4 represents the weight value of the average reliability of manufacturing resources; T min represents the manufacturing The minimum value of service time, C min represents the minimum value of service cost when only considering manufacturing service cost, E max represents the maximum value of manufacturing service efficiency when only considering average manufacturing service efficiency, Rel max represents when only considering average resource reliability The maximum value of the time-average resource reliability.

(3)粒子位置与速度更新(3) Particle position and velocity update

通过记录粒子个体最优值和种群最优值完成对粒子速度与位置的更新,同时完成对任务执行顺序层和资源层的更新,通过对任务执行顺序层和资源层的更新生成新的任务执行顺序,同时计算新的任务执行顺序所对应的适应度函数值。粒子速度与位置的更新操作按以下公式进行,其中ω表示惯性权重。Update the particle speed and position by recording the particle individual optimal value and population optimal value, and at the same time complete the update of the task execution sequence layer and resource layer, and generate new task execution by updating the task execution sequence layer and resource layer sequence, and calculate the fitness function value corresponding to the new task execution sequence. The update operation of particle velocity and position is carried out according to the following formula, where ω represents the inertia weight.

(4)粒子群算法的改进(4) Improvement of particle swarm algorithm

本发明采用线性权重代替惯性权重,以消除较大的惯性权重值导致粒子速度过大而引起的下一次粒子更新后直接穿过最优解位置的问题和较小的惯性权重值导致粒子速度过小而引起的粒子陷入局部搜索空间的问题导致的计算失败。惯性权重ω按公式线性变化。The present invention uses linear weights instead of inertial weights to eliminate the problem that a larger inertial weight value leads to excessive particle velocity and directly passes through the optimal solution position after the next particle update and a smaller inertial weight value leads to excessive particle velocity. Computational failures are caused by the problem of small particles getting trapped in a local search space. The inertia weight ω varies linearly according to the formula.

其中,Max-ITER表示最大迭代次数,ωmax,ωmin分别表示惯性权重ω的最大值与最小值,f表示当前迭代次数。Among them, Max-ITER represents the maximum number of iterations, ω max and ω min represent the maximum and minimum values of the inertia weight ω respectively, and f represents the current number of iterations.

通过迭代操作,最终可得到制造任务集U的调度方案(节选)。Through iterative operation, the scheduling scheme (excerpt) of the manufacturing task set U can be finally obtained.

Claims (3)

1. A dynamic scheduling method for cloud manufacturing resource change is characterized in that: the method comprises the following steps:
step 1, comprehensively analyzing resource scheduling demand conditions in a cloud manufacturing environment by a cloud manufacturing platform according to manufacturing service demands uploaded by a resource demand party and a resource provider;
step 2, constructing a dynamic scheduling model oriented to cloud manufacturing resource change according to the attribute characteristics of manufacturing resources and by combining with an actual resource scheduling rule;
step 3, optimizing and solving a dynamic scheduling process of the resources by applying an improved particle swarm algorithm to obtain a resource scheduling scheme and a real-time scheduling update strategy under the cloud manufacturing environment meeting the conditions;
the application of the improved particle swarm algorithm in the step 3 to dynamically schedule and optimize resources in a cloud manufacturing environment comprises the following steps:
(1) Determining a coding mode of a resource scheduling problem, and realizing formal description of multi-level tasks and resources in resource scheduling;
(2) Calculating the fitness value of each particle in the population according to the particle swarm algorithm flow, and updating the individual optimal particles and the global optimal particles;
(3) Updating the speed and the position of the particles according to a speed and position updating formula;
(4) Executing a loop condition, judging whether the algorithm reaches preset iteration times, if so, ending the algorithm and outputting the global optimal particles and fitness values thereof at the moment, and if not, continuing to execute the loop;
the particle swarm algorithm comprises the following steps:
(1) Setting various parameters in an algorithm, and randomly initializing the initial position and the speed of the particles;
(2) Calculating the fitness value of each particle;
(3) Comparing the fitness value of the current position of the particle with the fitness value of the optimal position of the particle in the searching process, reserving the optimal fitness value as the historical optimal fitness value of the individual particle, and simultaneously updating the historical optimal position by using the current position of the particle;
(4) Comparing the size of the historical optimal fitness value of the individual particles with the fitness value of the particle population at the optimal position, and reserving the larger fitness value as the current global optimal fitness value, wherein the current position is used as the optimal position;
(5) Updating the speed and the position of the particles according to a speed and position updating formula;
(6) And (3) recalculating the particle fitness value after the position and speed updating, and if the particle fitness value fails to reach the termination condition, continuing the operation of the step (2).
2. The cloud manufacturing resource variation oriented dynamic scheduling method as described in claim 1, wherein: the method comprises the following scheduling rules:
(1) Each manufacturing resource can independently complete one or more secondary manufacturing tasks, and the same secondary manufacturing task can only be processed by the same manufacturing resource at the same time;
(2) The first-level manufacturing task is decomposed to form a second-level manufacturing task, and the second-level manufacturing task is called a minimum unit to be serviced;
(3) Processing between different manufacturing resources for a secondary manufacturing task and a next secondary manufacturing task can create logistic costs and logistic time;
(4) The service mode of the task orderly receives service according to the front-to-back sequence of the secondary manufacturing task, and the starting time of the latter secondary manufacturing task is required to be larger than the sum of the ending time of the former secondary manufacturing task and the logistics time between the resources selected by the two tasks;
(5) The total cost of the logistics among different resources is proportional to the logistics time and the logistics distance;
(6) Different types of first-level manufacturing tasks have the same execution priority;
(7) All cloud manufacturing resources can be used at time zero;
(8) The secondary tasks are uninterrupted once they begin processing until their manufacturing service is complete.
3. The cloud manufacturing resource variation oriented dynamic scheduling method as described in claim 1, wherein: the constraint requirements described mainly include total manufacturing service time constraints, total manufacturing service cost constraints, resource average reliability constraints, manufacturing resource average service efficiency constraints, secondary manufacturing task processing timing constraints, and subtask granularity constraints.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107203412A (en) * 2017-05-04 2017-09-26 电子科技大学 A kind of cloud resource method for optimizing scheduling that particle cluster algorithm is improved based on membranous system
CN107346469A (en) * 2017-06-12 2017-11-14 哈尔滨理工大学 Multiple target integrated dispatch method is transported under cloud manufacturing environment more
CN110599068A (en) * 2019-09-29 2019-12-20 哈尔滨理工大学 Cloud resource scheduling method based on particle swarm optimization algorithm

Family Cites Families (1)

* Cited by examiner, † Cited by third party
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CN110851272B (en) * 2019-10-30 2022-02-11 内蒙古农业大学 Cloud Task Scheduling Method Based on Phagocytosis Particle Swarm Genetic Hybrid Algorithm

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107203412A (en) * 2017-05-04 2017-09-26 电子科技大学 A kind of cloud resource method for optimizing scheduling that particle cluster algorithm is improved based on membranous system
CN107346469A (en) * 2017-06-12 2017-11-14 哈尔滨理工大学 Multiple target integrated dispatch method is transported under cloud manufacturing environment more
CN110599068A (en) * 2019-09-29 2019-12-20 哈尔滨理工大学 Cloud resource scheduling method based on particle swarm optimization algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
云环境下资源调度效率优化研究与仿真;石峰;计算机仿真;第33卷(第5期);全文 *

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