CN101261694A

CN101261694A - A Simulation Verification Method for Virtual Optimal Scheduling in Process Industry

Info

Publication number: CN101261694A
Application number: CNA2007100675295A
Authority: CN
Inventors: 薛安克; 杨成忠; 金朝阳; 徐生林; 周晓惠
Original assignee: Hangzhou Electronic Science and Technology University
Current assignee: Hangzhou Electronic Science and Technology University
Priority date: 2007-03-07
Filing date: 2007-03-07
Publication date: 2008-09-10

Abstract

The invention relates to a simulative validation method used for the simulative scheduling-optimization of process industry, belonging to the advanced manufacturing and automatization technical field and aiming at solving the problem that the actual scheduling problem is not solved despite certain development of present domestic production scheduling model, arithmetic and calculation capability. The method of the invention includes the following steps: the generation of a scheduling plan, the prediction of potential production abnormity by calculation, simulation by virtue of visible ESCPetri-Nets technique of a graph and the generation and the storage of process data. An actual result indicates that the adoption of the method of the invention can dramatically enhance the execution quality of the scheduling plan and enhance the utilization rate of raw material and manufacturing equipment with dramatic economical benefit.

Description

A Simulation Verification Method for Virtual Optimal Scheduling in Process Industry

技术领域 technical field

本发明属于先进制造与自动化技术领域，涉及一种流程工业拟实优化调度的仿真验证方法。The invention belongs to the technical field of advanced manufacturing and automation, and relates to a simulation verification method for virtual optimal scheduling in the process industry.

背景技术 Background technique

目前，国内流程工业生产过程的生产计划和调度水平还很落后，绝大多数企业仍停留在手工阶段，其粗放型的计划制定与生产调度效率低下、准确性差、实时性差，复杂运行环境下的动态调度极其困难，生产系统的整体效益低下、资源浪费严重。国内虽然在生产调度模型、算法和计算性能方面取得了一定进展，但实际调度问题还远未解决。出现的调度软件缺少调度方案合理性的模拟验证功能。生产过程模拟仿真验证方法有助于判断调度计划、调度方案是否符合生产过程实际。At present, the production planning and scheduling level of the domestic process industry production process is still very backward, and the vast majority of enterprises are still in the manual stage. Their extensive planning and production scheduling are inefficient, poor in accuracy, and poor in real-time performance. Dynamic scheduling is extremely difficult, the overall efficiency of the production system is low, and resources are wasted seriously. Although some progress has been made in the production scheduling model, algorithm and computing performance in China, the actual scheduling problem is far from being solved. The emerging scheduling software lacks the simulation verification function of the rationality of the scheduling scheme. The production process simulation simulation verification method is helpful to judge whether the scheduling plan and scheduling plan are in line with the actual production process.

本发明的仿真验证就是在调度系统中用仿真模型模拟生产过程，以期在真实生产前，检验生产计划的可行性，目前已经公开的发明专利如，专利申请号为200510061522.3(一种流程工业拟实优化调度系统的方法)的专利申请提出一种普遍适用于流程工业拟实优化调度系统的方法，整体解决了流程工业的调度问题中的生产工艺流程的描述与建模、模型优化求解、生产过程模拟、生产过程动态监控与再调度。上述发明提出了流程工业的拟实优化调度系统的整体化方法，缺少对生产过程动态监控与再调度的具体方法描述。专利申请号为200510061523.8(一种流程工业可视化生产工艺流程描述的建模方法)的专利申请提出了一种适用于一般流程工业企业的间歇性或半间歇性生产系统的可视化建模方法，可以用此种简单、直观的方法构建优化调度的数学模型，为滚动优化调度求解提供基础。目前已申请的有关生产调度仿真验证方法如，申请号为200410021489.7(流程行业优化排产动态调度的组态平台方法)的专利申请提出一种图形化的建模组态模型和虚拟生产仿真模型，该仿真模型可虚拟合同生产的全流程，验证生产过程中的物料、时间及产能平衡，预测合同完成日期，该发明并没有解决混杂系统的混流生产模式调度方案的仿真验证问题，且该发明只简单给出了虚拟生产仿真模型的基本概念，并未给出仿真验证的实现过程。The simulation verification of the present invention is to use the simulation model to simulate the production process in the dispatching system, in order to test the feasibility of the production plan before the real production. The invention patent that has been disclosed at present is, for example, the patent application number is 200510061522.3 (a process industry virtual reality Method for Optimizing Scheduling System) The patent application proposes a method generally applicable to process industry virtual optimization scheduling system, which solves the description and modeling of production process, model optimization solution, production process in the scheduling problem of process industry as a whole Simulation, dynamic monitoring and rescheduling of production process. The above-mentioned invention proposes an integrated method for the pseudo-reality optimization scheduling system of the process industry, but lacks a specific method description for the dynamic monitoring and re-scheduling of the production process. The patent application No. 200510061523.8 (a modeling method for visual production process description in process industry) proposes a visual modeling method suitable for intermittent or semi-intermittent production systems of general process industry enterprises, which can be used This simple and intuitive method constructs a mathematical model of optimal scheduling, which provides a basis for solving rolling optimal scheduling. Currently applied for production scheduling simulation verification methods, such as the patent application No. 200410021489.7 (configuration platform method for optimizing production scheduling and dynamic scheduling in the process industry) proposes a graphical modeling configuration model and virtual production simulation model, The simulation model can virtualize the whole process of contract production, verify the balance of materials, time and production capacity in the production process, and predict the contract completion date. The basic concept of virtual production simulation model is simply given, and the realization process of simulation verification is not given.

发明内容 Contents of the invention

本发明针对现有技术的不足，提出一种普遍适用于流程工业生产过程调度方案模拟验证的方法，解决了流程工业的实际调度问题。本发明的仿真验证就是在调度系统中用混杂系统仿真模型模拟生产过程，以期在真实生产前，检验生产计划的可行性。The invention aims at the deficiencies of the prior art, and proposes a method generally applicable to the simulation verification of the production process scheduling scheme of the process industry, and solves the actual scheduling problem of the process industry. The simulation verification of the present invention is to use the hybrid system simulation model to simulate the production process in the scheduling system, in order to test the feasibility of the production plan before the real production.

本发明的具体方法包括以下步骤：Concrete method of the present invention comprises the following steps:

a.调度方案生成，具体方法是根据生产约束信息，建立优化调度的数学模型，并根据用户设定的优化目标以及模型求解器，解算出优化调度方案；a. Scheduling plan generation, the specific method is to establish a mathematical model for optimal scheduling based on production constraint information, and calculate the optimal scheduling plan according to the optimization goal set by the user and the model solver;

b.通过推算预知可能出现的生产异常，具体方法是采集企业资源计划系统中当前的产品销售计划、原料采购计划、库存信息、设备检修计划、能源供应计划数据，通过设备约束、容量约束、物流平衡约束、能源约束按时间进行跟踪推算；b. Predict possible production abnormalities through calculation. The specific method is to collect the current product sales plan, raw material procurement plan, inventory information, equipment maintenance plan, and energy supply plan data in the enterprise resource planning system. Through equipment constraints, capacity constraints, and logistics Balance constraints and energy constraints are tracked and calculated according to time;

设备约束具体为The device constraints are specifically

对于间歇生产设备： $W_{ijt} \cdot (\underset{i^{'} &Element; I_{j}}{Σ} Σ_{t^{'} = t + 1}^{t + P_{ij} - 1} W_{i^{'} j t^{'}}) = 0,$ $&ForAll; j, t, i &Element; I_{j} - - - (1)$ For batch production equipment: $W_{ijt} \cdot (\underset{i^{'} &Element; I_{j}}{Σ} Σ_{t^{'} = t + 1}^{t + P_{ij} - 1} W_{i^{'} j t^{'}}) = 0,$ $&ForAll; j, t, i &Element; I_{j} - - - (1)$

对于连续生产设备： $\underset{i &Element; I_{j}}{Σ} Y_{ijt} \leq 1,$ $&ForAll; j, t, i &Element; I_{j} - - - (2);$ For continuous production equipment: $\underset{i &Element; I_{j}}{Σ} Y_{ijt} \leq 1,$ $&ForAll; j, t, i &Element; I_{j} - - - (2);$

容量约束具体为The capacity constraints are specifically

${W W}_{ijt ijt} {V V}_{ij ij}^{min min} \leq \leq {B B}_{ijt ijt} \leq \leq {W W}_{ijt ijt} {V V}_{ij ij}^{max max},,$ $&ForAll; &ForAll; i i,, t t,, j j &Element; &Element; Ji Ji - - - - - - - - ((33))$

物流平衡约束具体为The logistics balance constraint is specifically

${S S}_{s the s,, t t} = = {S S}_{s the s,, t t - - 11} + + \underset{i i &Element; &Element; {\overset{&OverBar; &OverBar;}{T T}}_{s the s}}{Σ Σ} \overset{&OverBar; &OverBar;}{{ρ ρ}_{is is}} \underset{j j &Element; &Element; {J J}_{i i}}{Σ Σ} (({B B}_{i i,, j j,, t t - - {P P}_{ij ij}} + + {Q Q}_{ijt ijt} Δt Δt)) - - \underset{i i &Element; &Element; {T T}_{s the s}}{Σ Σ} {ρ ρ}_{is is} \underset{j j &Element; &Element; {J J}_{i i}}{Σ Σ} (({B B}_{ijt ijt} + + {Q Q}_{ijt ijt} Δt Δt)) + + {R R}_{st st} - - {D D.}_{st st},,$ $&ForAll; &ForAll; s the s,, t t - - - - - - ((44))$

能源约束具体为The energy constraints are specifically

${U u}_{ut out} = = \underset{i i}{Σ Σ} \underset{j j &Element; &Element; {J J}_{i i}}{Σ Σ} {Σ Σ}_{θ θ = = 00}^{{p p}_{ij ij} - - 11} (({α α}_{uiθ uiθ} {W W}_{ij ij,, t t - - θ θ} + + {β β}_{uiθ uiθ} {B B}_{ij ij,, t t - - θ θ} + + {β β}_{uiθ uiθ} {Q Q}_{ij ij,, t t - - θ θ} Δt Δt)),,$ $&ForAll; &ForAll; u u,, t t - - - - - - ((55))$

其中，u为能源种类，设U_ut ^max为能源上限；Among them, u is the type of energy, and U _ut ^max is set as the upper limit of energy;

各约束定义式中变量定义为：The variables in each constraint definition are defined as:

W_ijt∈{0，1}，W_ijt＝1表示t时刻在间歇生产设备j上开始操作i，否则W_ijt＝0；W _ijt ∈ {0, 1}, W _ijt = 1 means that the operation i starts on the batch production equipment j at time t, otherwise W _ijt = 0;

Y_ijt∈{0，1}，Y_ijt＝1表示t时段在连续生产设备j上正在操作i，否则Y_ijt＝0；Y _ijt ∈ {0, 1}, Y _ijt = 1 means that i is operating on continuous production equipment j in period t, otherwise Y _ijt = 0;

Z_sjt∈{0，1}，Z_sjt＝1表示t时段状态s物品正在占用存贮设备j，否则Z_sjt＝0；Z _sjt ∈ {0, 1}, Z _sjt = 1 means that item s in state s is occupying storage device j during period t, otherwise Z _sjt = 0;

B_ijt∈R⁺，表示t时刻操作i在间歇生产设备j上开始处理的批量；B _ijt ∈ R ⁺ , represents the batch that operation i starts to process on intermittent production equipment j at time t;

Q_ijt∈R⁺，表示t时段操作i在连续生产设备j上的生产速率；Q _ijt ∈ R ⁺ , represents the production rate of operation i on continuous production equipment j in period t;

S_st∈R⁺，表示t时刻状态s物品的存贮量；S _st ∈ R ⁺ , represents the storage capacity of items in state s at time t;

R_st∈R⁺，表示t时刻状态s原料(中成品)的到货量；R _st ∈ R ⁺ , represents the arrival quantity of raw materials (finished products) in state s at time t;

D_st∈R⁺，表示t时刻状态s产品的提货量；D _st ∈ R ⁺ , represents the delivery quantity of the product in state s at time t;

Δt∈R⁺，表示均匀时段的时间长度；Δt∈R ⁺ , represents the time length of the uniform period;

c.利用图形的可视化ESCPetri-Nets网技术进行仿真，具体方法是将步骤a优化调度方案与步骤b可能出现的生产异常结合在一起，利用图形的可视化ESCPetri-Nets网技术进行仿真；c. Use the graphical visualization ESCPetri-Nets network technology to simulate, the specific method is to combine the optimal scheduling plan of step a with the possible production abnormality in step b, and use the graphical visualization ESCPetri-Nets network technology to simulate;

d.形成并保存过程数据，具体方法是形成并保存过程数据，以突出颜色标识生产异常点，并提示产生异常的原因。d. Form and save the process data, the specific method is to form and save the process data, to highlight the color to mark the abnormal points of production, and to prompt the cause of the abnormality.

本发明中步骤a中的目标模型数据文件的生成、优化调度的数学模型的建立采用已有的技术，如已公开的专利申请200510061523.8提供的方法。模型求解器采用已有的技术，如已公开的专利200510061522.3提供的ILOG OPL Studio模型求解器。The generation of the target model data file and the establishment of the mathematical model for optimal scheduling in step a of the present invention adopt existing technologies, such as the method provided by the published patent application 200510061523.8. The model solver adopts the existing technology, such as the ILOG OPL Studio model solver provided by the published patent 200510061522.3.

本发明的模拟验证的方法实现了调度方案的模拟验证技术，利用连续Petri网简化动态模型，模拟生产过程，得到大量过程数据，用来验证调度方案合理性及监控实际运行；利用混杂系统模型仿真对调度方案进行验证，一方面对调度方案进行更为细致的检验，如一些无法用STN表达的约束，在发生冲突的环节允许人工根据经验进行小的调整；另一方面，在仿真过程中可以得到一些中间数据，可用来评价调度实施情况。The simulation verification method of the present invention realizes the simulation verification technology of the scheduling scheme, utilizes the continuous Petri net to simplify the dynamic model, simulates the production process, and obtains a large amount of process data, which is used to verify the rationality of the scheduling scheme and monitor the actual operation; use the hybrid system model simulation To verify the scheduling scheme, on the one hand, conduct a more detailed inspection on the scheduling scheme, such as some constraints that cannot be expressed in STN, and allow manual small adjustments based on experience in the link where conflicts occur; on the other hand, during the simulation process, you can Get some intermediate data, which can be used to evaluate the scheduling implementation.

具体实施方式 Detailed ways

一种流程工业拟实优化调度的仿真验证方法，具体步骤是：A method for simulation verification of process industry simulation optimization scheduling, the specific steps are:

(1)调度方案生成：根据产品销售计划、原料采购计划、设备维修计划、产品(中间体、原料)库存信息和设备生产能力，生产资源占用、消耗、生产成本等生产约束信息，生成目标模型的数据文件建立优化调度的数学模型，并根据用户设定的优化目标(最大生产能力、最大利润或满足销售订单)，以及模型求解器，解算出优化调度方案。(1) Scheduling plan generation: According to product sales plan, raw material procurement plan, equipment maintenance plan, product (intermediate, raw material) inventory information and equipment production capacity, production resource occupation, consumption, production cost and other production constraint information, generate a target model The mathematical model of optimal scheduling is established based on the data files, and the optimal scheduling scheme is calculated according to the optimization goals set by the user (maximum production capacity, maximum profit, or satisfying sales orders) and the model solver.

(2)通过推算预知可能出现的生产异常：采集企业资源计划系统中当前的产品销售计划、原料采购计划(包括到货情况)、库存信息、设备检修计划、能源供应计划等，通过混杂设备约束(包括间歇生产设备和连续生产设备)、容量约束、物流平衡约束和能源约束按时间进行跟踪推算，预知可能出现的生产异常。(2) Predict possible production abnormalities through calculation: collect the current product sales plan, raw material procurement plan (including arrival status), inventory information, equipment maintenance plan, energy supply plan, etc. in the enterprise resource planning system, and use mixed equipment constraints (including intermittent production equipment and continuous production equipment), capacity constraints, logistics balance constraints and energy constraints are tracked and calculated according to time to predict possible production abnormalities.

(3)将优化调度方案(步骤1获取的数据)与可能出现的生产异常(步骤2获取的数据)结合在一起，利用图形的可视化ESCPetri-Nets网技术仿真生产过程。以调度时间为轴线，动态曲线显示生产车间设备工况、物料平衡(采购-库存-销售)，模拟验证生产调度情况。以物料为中心，随调度时间变化，以图形的方式动态显示某个物料的变化趋势和某时刻与某物料生产消耗相关的设备运行状况。(3) Combining the optimized scheduling scheme (data obtained in step 1) with possible production abnormalities (data obtained in step 2), the production process is simulated by using graphic visualization ESCPetri-Nets technology. Taking the scheduling time as the axis, the dynamic curve shows the equipment working conditions and material balance (purchase-inventory-sales) of the production workshop, and simulates and verifies the production scheduling situation. Centering on the material, it dynamically displays the change trend of a certain material and the operating status of equipment related to the production and consumption of a certain material in a graphical way as the scheduling time changes.

1)物料变化包括：采购、销售、生产(产出及消耗)。1) Material changes include: procurement, sales, production (output and consumption).

2)某时刻与物料消耗相关的生产设备的分布状况分车间、工段显示。2) The distribution of production equipment related to material consumption at a certain moment is displayed in workshops and sections.

3)移动观察点时间游标，即可浏览某时刻的物料动态变化信息、该时刻的物料生产消耗或产出情况及设备运行状况。3) By moving the time cursor of the observation point, you can browse the dynamic change information of materials at a certain moment, the production consumption or output of materials at that moment, and the operation status of equipment.

(4)形成并保存过程数据，以突出颜色标识生产异常点，并提示产生异常的原因。(4) Form and save the process data, mark the abnormal points of production with highlighted colors, and prompt the cause of the abnormality.

以某药品生产车间为例进行实例说明。Take a pharmaceutical production workshop as an example to illustrate.

首先收集药品车间的初始数据，包括产品销售计划、原料采购计划、设备维修计划、产品(中间体、原料)库存信息和设备生产能力，生产资源占用、消耗、生产成本等生产约束信息，按步骤(1)解算出优化调度方案。然后按照步骤(2)将企业资源计划系统中当前的产品销售计划、原料采购计划(包括到货情况)、库存信息、设备检修计划、能源供应计划等，通过设备约束、容量约束、物流平衡约束和能源约束按时间进行跟踪推算预知可能出现的生产异常。在按照步骤(3)将优化调度方案(步骤1获取的数据)与可能出现的生产异常(步骤2获取的数据)结合在一起，利用图形的可视化ESCPetri-Nets网技术仿真生产过程。以调度时间为轴线，动态曲线显示生产车间设备工况、物料平衡(采购-库存-销售)，模拟验证生产调度情况。最后按步骤(4)形成并保存过程数据，以突出颜色标识生产异常点，并提示产生异常的原因。First collect the initial data of the pharmaceutical workshop, including product sales plan, raw material procurement plan, equipment maintenance plan, product (intermediate, raw material) inventory information and equipment production capacity, production resource occupation, consumption, production cost and other production constraint information, step by step (1) Solve and calculate the optimal scheduling scheme. Then according to step (2), the current product sales plan, raw material procurement plan (including arrival status), inventory information, equipment maintenance plan, energy supply plan, etc. in the enterprise resource planning system are combined through equipment constraints, capacity constraints, and logistics balance constraints. And energy constraints are tracked and calculated according to time to predict possible production abnormalities. According to step (3), the optimal scheduling plan (data obtained in step 1) is combined with possible production abnormalities (data obtained in step 2), and the production process is simulated by using the visualized ESCPetri-Nets network technology of graphics. Taking the scheduling time as the axis, the dynamic curve shows the equipment working conditions and material balance (purchase-inventory-sales) of the production workshop, and simulates and verifies the production scheduling situation. Finally, according to the step (4), the process data is formed and saved, and the production abnormal points are marked with prominent colors, and the reasons for the abnormalities are prompted.

实际结果表明，采用本发明方法后调度方案执行质量显著提高，提高了原材料和生产设备利用率，经济效益显著。Actual results show that after adopting the method of the invention, the execution quality of the dispatching plan is significantly improved, the utilization rate of raw materials and production equipment is improved, and the economic benefit is remarkable.

Claims

1. A method for simulation verification of process industry simulation optimization scheduling, characterized in that the method comprises the following steps:

a. Scheduling plan generation, the specific method is to establish a mathematical model for optimal scheduling based on production constraint information, and calculate the optimal scheduling plan according to the optimization goal set by the user and the model solver;

b. Predict possible production abnormalities through calculation. The specific method is to collect the current product sales plan, raw material procurement plan, inventory information, equipment maintenance plan, and energy supply plan data in the enterprise resource planning system. Through equipment constraints, capacity constraints, and logistics Balance constraints and energy constraints are tracked and calculated according to time;

The device constraints are specifically

For batch production equipment:

W_{ijt} &Center Dot; (\underset{i^{'} &Element; I_{j}}{Σ} Σ_{t^{'} = t + 1}^{t + P_{ij} - 1} W_{i^{'} j t^{'}}) = 0, &ForAll; j, t, i &Element; I_{j} - - - (1)

For continuous production equipment:

\underset{i &Element; I_{j}}{Σ} Y_{ijt} \leq 1, &ForAll; j, t, i &Element; I_{j} - - - (2);

The capacity constraints are specifically

{W W}_{ijt ijt} {V V}_{ij ij}^{min min} \leq \leq {B B}_{ijt ijt} \leq \leq {W W}_{ijt ijt} {V V}_{ij ij}^{max max},, &ForAll; &ForAll; i i,, t t,, j j &Element; &Element; Ji Ji - - - - - - ((33))

The logistics balance constraint is specifically

{S S}_{s the s,, t t} = = {S S}_{s the s,, t t - - 11} + + \underset{i i &Element; &Element; \overset{&OverBar; &OverBar;}{{T T}_{s the s}}}{Σ Σ} \overset{&OverBar; &OverBar;}{{ρ ρ}_{is is}} \underset{j j &Element; &Element; {J J}_{i i}}{Σ Σ} (({B B}_{i i,, j j,, t t - - {P P}_{ij ij}} + + {Q Q}_{ijt ijt} Δt Δt)) - - \underset{i i &Element; &Element; {T T}_{s the s}}{Σ Σ} {ρ ρ}_{is is} \underset{j j &Element; &Element; {J J}_{i i}}{Σ Σ} (({B B}_{ijt ijt} + + {Q Q}_{ijt ijt} Δt Δt)) + + {R R}_{st st} - - {D D.}_{st st},, &ForAll; &ForAll; s the s,, t t - - - - - - ((44))

The energy constraints are specifically

{U u}_{ut out} = = \underset{i i}{Σ Σ} \underset{j j &Element; &Element; {J J}_{i i}}{Σ Σ} {Σ Σ}_{θ θ = = 00}^{{p p}_{ij ij} - - 11} (({α α}_{uiθ uiθ} {W W}_{ij ij,, t t - - θ θ} + + {β β}_{uiθ uiθ} {B B}_{ij ij,, t t - - θ θ} + + {β β}_{uiθ uiθ} {Q Q}_{ij ij,, t t - - θ θ} Δt Δt)),, &ForAll; &ForAll; u u,, t t - - - - - - ((55))

Among them, u is the type of energy, and U _ut ^max is set as the upper limit of energy;

The variables in each constraint definition are defined as:

W _ijt ∈ {0, 1}, W _ijt = 1 means that the operation i starts on the batch production equipment j at time t, otherwise W _ijt = 0;

Y _ijt ∈ {0, 1}, Y _ijt = 1 means that i is operating on continuous production equipment j in period t, otherwise Y _ijt = 0;

Z _sjt ∈ {0, 1}, Z _sjt = 1 means that item s in state s is occupying storage device j during period t, otherwise Z _sjt = 0;

B _ijt ∈ R ⁺ , represents the batch that operation i starts to process on intermittent production equipment j at time t;

Q _ijt ∈ R ⁺ , represents the production rate of operation i on continuous production equipment j in period t;

S _st ∈ R ⁺ , represents the storage capacity of items in state s at time t;

R _st ∈ R ⁺ , represents the arrival quantity of raw materials (finished products) in state s at time t;

D _st ∈ R ⁺ , represents the delivery quantity of the product in state s at time t;

Δt∈R ⁺ , represents the time length of the uniform period;

c. Use the graphical visualization ESCPetri-Nets network technology to simulate, the specific method is to combine the optimal scheduling plan of step a with the possible production abnormality in step b, and use the graphical visualization ESCPetri-Nets network technology to simulate;

d. Form and save the process data, the specific method is to form and save the process data, to highlight the color to identify the abnormal points of production, and to prompt the cause of the abnormality.