CN119443757A

CN119443757A - An organizational design optimization method for improving the operating efficiency of earthwork engineering machinery

Info

Publication number: CN119443757A
Application number: CN202411472243.5A
Authority: CN
Inventors: 常海彬; 江涛; 于雷; 房鸿鹏; 邓巧巧; 郑晓瑞; 霍明志; 富英豪
Original assignee: Nuclear Industry Brigade 243; China Nuclear Inner Mongolia Mining Investment Co ltd
Current assignee: Nuclear Industry Brigade 243; China Nuclear Inner Mongolia Mining Investment Co ltd
Priority date: 2024-10-22
Filing date: 2024-10-22
Publication date: 2025-02-14

Abstract

The application relates to the field of earth and stone engineering and discloses a tissue design optimization method for improving the working efficiency of earth and stone engineering machinery, which comprises the following steps that S1, modeling is carried out on each working procedure in the earth and stone engineering through a directed acyclic graph; S2, calculating the earliest starting time and the latest starting time of each process by using a critical path method based on a process relation model, identifying a critical path of a project, and determining a critical process affecting the project construction period, S3, performing process scheduling optimization by using a mixed integer linear programming based on the identified critical process, ensuring that the project construction period is minimized under the resource limitation, and generating an optimized process scheduling scheme, and S4, establishing a multi-stage queuing theory model based on the optimized process scheduling scheme, and optimizing the scheduling of mechanical equipment. According to the application, by optimizing the process scheduling and the mechanical equipment scheduling, the construction efficiency of the earth-rock engineering is obviously improved, the construction period and the resource waste are reduced, and the project is ensured to be completed in time and high efficiency.

Description

Tissue design optimization method for improving working efficiency of earth-rock engineering machinery

Technical Field

The invention relates to the technical field of earth and stone engineering, in particular to a tissue design optimization method for improving the working efficiency of earth and stone engineering machinery.

Background

In the engineering project of the earthwork, reasonable scheduling of mechanical equipment and efficient coordination among working procedures are key to ensuring that construction is smoothly carried out and the construction period is completed on time. However, in the existing earth and stone engineering organization design and scheduling method, a plurality of problems still exist, and the overall construction efficiency and the reasonable utilization of resources are affected.

In the prior art, the process scheduling is usually based on experience or a simple static planning mode, and a scientific optimization means is lacked. The dependency relationship among the working procedures is complex, and modeling and analysis are difficult to accurately and clearly perform. In this case, the process scheduling often depends on subjective judgment, and it is impossible to effectively identify the key process that has the greatest influence on the project period. Due to the lack of systematic process scheduling optimization means, the identification of critical paths is inaccurate, resulting in delays in part of the process directly affecting the progress of the whole project. Moreover, how to efficiently and reasonably allocate resources to ensure that critical processes are successfully completed in an environment with limited resources remains a weak point in the prior art.

Secondly, the scheduling and configuration efficiency of the mechanical equipment is low, resulting in resource waste. In a construction site, the cooperative work between different mechanical devices is not effectively optimized, and the devices often have long waiting time when switching among working procedures, or certain devices are idle due to unreasonable scheduling, while other devices are in an overload running state. The prior art lacks an effective means to simulate the task load and the work efficiency of the equipment in different procedures, so that the scheduling of the mechanical equipment cannot be dynamically adjusted. This not only lengthens the construction period, but also results in a decrease in the utilization rate of the equipment, increasing the project cost.

Meanwhile, as the construction project scale expands, the complexity of process and equipment scheduling increases. The traditional scheduling method is difficult to handle complex scheduling problems of multiple procedures and multiple devices in large-scale engineering, and particularly when the dependency relationship among procedures and resource conflict are handled, the traditional method cannot provide an effective solution. This limitation makes the construction plan susceptible to external factors, lacks flexibility and adjustability, and ultimately affects the progress and economic benefits of the project.

Therefore, the existing earth and stone engineering scheduling method is difficult to meet the requirements of modern engineering projects on efficient construction, accurate scheduling and reasonable resource utilization, and a scientific and systematic organization design optimization method is urgently needed, so that scheduling of working procedures and mechanical equipment can be optimized in a dynamic environment, the equipment utilization rate is improved, the construction period is shortened, and resource waste in construction is reduced.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides an organization design optimization method for improving the working efficiency of earth-rock engineering machinery, and solves the problems of unreasonable process scheduling, low utilization rate of mechanical equipment and difficult optimization of construction period in the existing earth-rock engineering.

In order to achieve the purpose, the invention is realized by the following technical scheme that the tissue design optimization method for improving the working efficiency of the earth-rock engineering machinery comprises the following steps:

Modeling each process in the earth-rock engineering through a directed acyclic graph, wherein process nodes represent specific operation tasks, and the sides represent the dependency relationship among the processes to generate a process relationship model;

calculating the earliest starting time and the latest starting time of each process by using a critical path method based on a process relation model, identifying a critical path of the project, and determining a critical process affecting the project construction period;

Based on the identified key working procedure, utilizing mixed integer linear programming to perform working procedure scheduling optimization, ensuring that project construction period is minimized under resource limitation, and generating an optimized working procedure scheduling scheme;

Based on the optimized procedure scheduling scheme, a multi-stage queuing theory model is established, and scheduling of mechanical equipment is optimized.

Preferably, the modeling of each process in the earth-rock engineering through the directed acyclic graph comprises:

Acquiring all process information of earth and stone engineering, defining a process set V= { V ₁,v₂,...,v_n }, wherein each process V _i represents a specific operation task;

Constructing a directed edge set E= { (v _i,v_j) } among the procedures according to the dependency relationship among the procedures, wherein each edge (v _i,v_j) represents that the procedure v _j can be started after the procedure v _i is finished;

For each process v _i, determining the execution time T _i, and generating a process execution time set t= { T ₁,t₂,...,t_n };

Defining a weight e _ij of each edge (v _i,v_j), representing a time interval between the process v _i and the process v _j, and generating an edge weight set w= { e _ij };

And establishing a process dependency relation model through the directed acyclic graph G= (V, E), wherein the sequence and the dependency relation of the process nodes are determined by directed edges.

Preferably, the step of calculating the earliest start time and the latest start time of each process using a critical path method based on the process relation model, identifying a critical path of the project, and determining the critical process affecting the project period includes:

Determining the dependency relationship of all the procedures v _i and the execution time t _i thereof based on a procedure relation model generated by a directed acyclic graph;

The earliest start time ES (v _i) of each process is calculated based on the completion time of all the preceding processes and the time interval e _ij between the preceding process and the current process, and the calculation formula of the earliest start time is:

Wherein ES (v _i) represents the earliest start time of procedure v _i and predecessors of v _i represents all procedures that must be completed before v _i;

Calculating a latest start time LS (v _i) of each process, wherein the latest start time is based on the earliest start time of all subsequent processes, so that the processes can be completed without influencing the construction period, and a calculation formula is as follows:

Wherein LS (v _i) represents the latest start time of procedure v _i and successors of v _i represents all procedures starting after v _i;

The free floating time FT (v _i) of each process, i.e. the time that the process can be delayed, is determined by a comparison of the earliest start time and the latest start time:

FT(v_i)＝LS(v_i)-ES(v_i)

when FT (v _i) =0, the identification process v _i is located on the critical path of the item;

Combining all the working procedures with zero free floating time to form a critical path of the project, wherein the working procedures on the critical path directly affect the total construction period of the project;

And identifying and determining key procedures affecting project construction period according to the key paths, and taking the key procedures as key points of scheduling optimization of subsequent procedures.

Preferably, the step of performing process scheduling optimization by using mixed integer linear programming based on the identified key process to ensure that project construction period is minimized under resource limitation, and generating an optimized process scheduling scheme includes:

Defining a start time variable x _i of each process v _i based on the identified critical process, each variable x _i being an integer representing the actual start time of process v _i;

constructing an objective function to minimize the completion time of the last process, thereby minimizing the construction period of the whole project, wherein the expression of the objective function is as follows:

Wherein t _i is the execution time of the process V _i, and V is the set of all the processes;

introducing resource constraint, ensuring that each procedure is executed under necessary resources, and for each resource k, the expression is:

Wherein V _k represents a process set requiring the resource k, y _ik represents the number of the resources k used by the process V _i, and R _k is the total available amount of the resources k;

Adding time constraints of inter-process dependencies ensures that any process v _i begins only after all its predecessor processes are completed, the constraint condition expressions are:

Wherein E _ji represents the time interval between the process v _j and the process v _i, and E is the dependency edge set between the processes;

And generating the optimal starting time of each process by solving the mixed integer linear programming problem, and forming an optimized process scheduling scheme of the whole project.

Preferably, the step of establishing a multi-stage queuing theory model based on the optimized procedure scheduling scheme and optimizing the scheduling of the mechanical equipment comprises the following steps:

based on an optimized procedure scheduling scheme, identifying the types and the quantity of mechanical equipment required by each procedure v _i, defining the task arrival rate lambda _i and the service rate mu _i of each equipment, and constructing a multi-stage queuing model of the equipment;

Modeling the scheduling of mechanical equipment into a multi-stage queuing system, wherein each process in the system is a service node, and the equipment is a client in a waiting queue, so that the scheduling of the equipment among different processes is smooth;

the utilization rate ρ _i of each device i at different process nodes is defined, the device load condition is represented, and the calculation formula is as follows:

Wherein lambda _i is the task arrival rate of the equipment i at a certain process node, mu _i is the service rate of the equipment, and rho _i <1 is ensured to ensure the stability of the system;

The average waiting time W _i of each equipment at each process node is calculated, and the calculation formula of the waiting time is as follows:

Optimizing equipment scheduling by reducing W _i, and ensuring that waiting time of mechanical equipment among all working procedures is minimized;

the average task number L _i of each device in the system is calculated, and the formula is as follows:

L_i＝λ_iW_i

Ensuring the number of tasks within a reasonable range, avoiding overload or idling of equipment, and balancing the equipment utilization conditions in each process by adjusting the task arrival rate lambda _i and the service rate mu _i;

on the basis of a multi-stage queuing theory model, dynamically adjusting the scheduling sequence of equipment among the working procedures according to the working procedure priority and the equipment availability, and ensuring the maximization of the equipment utilization rate and the minimization of the waiting time;

And finally, optimizing a scheduling scheme based on a queuing model of the equipment, and reducing the transfer and waiting of the equipment among different procedures.

The invention also provides a tissue design optimizing device for improving the working efficiency of the earth-rock engineering machinery, which comprises:

The working procedure modeling module is used for modeling each working procedure in the earth-rock engineering;

the critical path calculation module is used for identifying critical paths of the project based on the earliest and latest starting time of the critical path method calculation procedure;

the scheduling optimization module is used for optimizing multi-station scheduling by utilizing mixed integer linear programming and minimizing the construction period;

And the queuing theory optimization module is used for optimizing the dispatching of the mechanical equipment based on the multi-stage queuing theory model.

The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described above when executing the computer program.

The invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.

The invention provides a tissue design optimization method for improving the working efficiency of earth and stone engineering machinery. The beneficial effects are as follows:

1. The invention models the working procedures through the directed acyclic graph, clearly describes the dependency relationship among the working procedures, and avoids the cyclic dependency and execution conflict among the working procedures. This ensures the rationality and the sequency of the whole construction process, thereby improving the coordination among the working procedures.

2. The invention calculates the earliest starting time and the latest starting time of each procedure by a critical path method and identifies the critical procedure which directly affects project construction period. Critical path analysis provides the core process of construction period control so that construction managers can concentrate on resources and efforts at these critical points to ensure project completion on schedule.

3. The invention utilizes mixed integer linear programming to perform process scheduling optimization, and generates optimal process starting time and resource allocation scheme under the condition of limited resources. The optimization scheme can effectively reduce resource conflict and process delay, and minimize construction period.

4. According to the invention, the scheduling of the mechanical equipment is optimized through the multi-stage queuing theory model, the task arrival rate and the service rate of the equipment among the working procedures are calculated, and the average waiting time of the mechanical equipment is reduced. The use efficiency of the equipment is improved, the equipment is prevented from being idle or overloaded, and the overall construction efficiency is improved.

5. According to the method, the task quantity and the utilization rate of the equipment are calculated and optimized, so that the equipment can be ensured to run under reasonable load, and task backlog is avoided. The switching of the equipment between different procedures is more efficient, and the utilization rate of the equipment is improved to the maximum extent.

6. The invention obviously shortens the total construction period of the project by optimizing the procedure scheduling and the equipment scheduling. The optimal scheduling scheme reduces time waste among working procedures and non-working time of equipment, thereby accelerating overall construction progress.

Drawings

FIG. 1 is a schematic flow chart of the method of the present invention;

FIG. 2 is a schematic view of the structure of the device of the present invention;

FIG. 3 is a schematic diagram of a computer device according to the present invention.

The system comprises 100 parts of procedure modeling modules, 200 parts of critical path calculation modules, 300 parts of scheduling optimization modules, 400 parts of queuing theory optimization modules, 40 parts of computer equipment, 41 parts of processors, 42 parts of memories, 43 parts of storage media.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1, an embodiment of the present invention provides a tissue design optimization method for improving the working efficiency of an earth-rock engineering machine, including the following steps:

S1, modeling each process in earth-rock engineering through a directed acyclic graph

Firstly, modeling each process in the earthwork engineering through a Directed Acyclic Graph (DAG), and defining the dependency relationship among the processes, thereby providing a foundation for subsequent critical path analysis, construction period optimization and equipment scheduling. In this step, the process nodes represent specific job tasks, and the sequence and dependence among the processes are represented by directed edges, forming a complete process relationship model. This step ensures the rationality of the process execution and provides an accurate scheduling basis for the optimization scheme.

In this embodiment, each process of the earth-rock engineering is modeled by:

First, all process information of the earth-rock engineering is acquired, and a process set v= { V ₁,v₂,...,v_n }, wherein each process V _i represents a specific job task, is defined. This set contains all the job tasks that need to be performed in the project.

Then, a set of directed edges e= { (v _i,v_j) } between the processes is created according to the logical and physical dependencies between the processes, where each edge E _ij indicates that the process v _j can only start after the process v _i is completed. The edge set defines the sequence of the working procedures and ensures the successful execution of the subsequent working procedures.

For each process v _i, the execution time T _i is determined, and a process execution time set t= { T ₁,t₂,...,t_n } is generated. The time information is used for time calculation of a subsequent critical path method, and the execution time of all working procedures is ensured to be accurately recorded and managed.

Next, a time interval e _ij is determined for each edge (v _i,v_j), representing the time interval between process v _i and process v _j, forming an edge weight set w= { e _ij }. These time intervals take into account preparation time or other dependencies that may exist between processes.

Finally, a process dependency model is established through a directed acyclic graph g= (V, E). The nodes in the graph represent the processes, the edges represent the dependency relationship and the execution sequence among the processes, and the orderly execution of the processes is ensured, so that the cyclic dependency does not exist.

In this embodiment, loop-free verification is performed on the generated process dependency model in order to further secure the process rationality. The loop-free graph ensures the feasibility of process execution, namely, all the processes can be sequentially executed according to the dependency relationship, and the problem of execution blocking caused by cyclic dependency is avoided.

In this embodiment, after the process model is built, the dependency path length of each process can be calculated, so as to verify the dependency relationship between the processes and the correctness of the model, and provide accurate data support for the scheduling and optimization of the subsequent processes.

Through the step of modeling the directed acyclic graph, a structured model and data support are provided for subsequent critical path analysis, construction period optimization and mechanical equipment scheduling.

S2, calculating the earliest starting time and the latest starting time of each process by using a critical path method based on a process relation model, and identifying critical paths of the items

After a Directed Acyclic Graph (DAG) model of the process is built, the earliest and latest start times of each process are calculated by a Critical Path Method (CPM), critical paths of the project are identified, and critical processes affecting the project period are determined. The main purpose of this step is to identify which procedures have a direct impact on project duration and to provide a clear target for subsequent duration optimization and scheduling optimization.

In this embodiment, the time of each process is calculated using a Critical Path Method (CPM), and the specific steps are as follows:

First, the dependency relationship between the processes is obtained by the directed acyclic graph model g= (V, E) constructed in the previous step. For each process v _i, its execution time t _i is obtained, along with the dependency edge e _ij between the processes, indicating the time interval required for process v _j to complete before process v _i.

In this embodiment, the earliest start time (ES) of each process is first calculated using a critical path method. The earliest start time ES (v _i) is determined by the earliest time that all the preceding processes of the process are completed. The calculation formula is as follows:

Here, ES (v _i) represents the earliest start time of the process v _i, t _j is the execution time of the preceding process v _j, and e _ij is the time interval between the preceding process v _j and the current process v _i.

In this embodiment, the latest start time (LS) of each process, that is, the time at which the process can start at the latest without affecting the project period, is calculated next. The calculation formula of the latest start time is:

Where LS (v _i) represents the latest start time of the process v _i, LS (v _j) is the latest start time of the subsequent process v _j, and e _ij is the time interval between the current process and the subsequent process.

In this embodiment, the free Floating Time (FT) of each process, i.e., the time that the process can be delayed, can be further determined by calculating the earliest and latest start times of each process. The free floating time is calculated by the following formula:

FT(v_i)＝LS(v_i)-ES(v_i)

When FT (v _i) =0, the identification process v _i is located on the critical path of the item.

In this embodiment, the critical path consists of all the steps with zero free floating time. The process on this path directly determines the overall project duration, as any process delay on the critical path can result in delays in project duration. Therefore, identifying these critical processes is critical to project management.

The earliest and latest starting times of the procedures calculated by the critical path method can be used for determining which procedures are required to be strictly planned to be executed and which procedures have certain scheduling flexibility. This provides a data base for subsequent construction period optimization and resource scheduling, and helps construction managers to rationally plan construction progress.

S3, based on the identified key working procedure, utilizing mixed integer linear programming to carry out working procedure dispatching optimization

Based on the identification of the critical process, the process schedule is further optimized using Mixed Integer Linear Programming (MILP). The step ensures that the optimal scheduling of the working procedure is realized under the limited resource and time limit by constructing an optimization model, thereby minimizing the whole construction period of the project. The goal of this step is to determine the optimal start time of the process and resource allocation scheme to ensure that critical processes are completed on schedule while maximizing resource utilization efficiency.

In this embodiment, the procedure scheduling optimization is specifically performed by adopting mixed integer linear programming, and the steps are as follows:

Defining decision variables first, an integer decision variable x _i of the process start time is defined, where x _i represents the actual start time of process v _i. The value of this variable is what we need to optimize, representing the schedule time for each process.

The objective function is established-the main objective of the optimization is to minimize the construction period of the whole project, i.e. to minimize the completion time of the last process in the project. Thus, the objective function can be expressed as:

Where x _i is the start time of the process v _i, and t _i is the execution time of the process. By minimizing the objective function, it is ensured that the total construction period of the project is as short as possible.

Resource constraints-to ensure that a process can be performed with limited resources, constraints must be defined for each resource k. For each resource k, assuming that there is a set of procedures V _k using the resource, the constraint expression is:

Where y _ik represents the number of resources used in the process v _i, and R _k is the maximum available amount of resource k. This constraint ensures that the upper limit of availability of each resource is not exceeded, avoiding resource conflicts.

Process dependency constraints-in order to ensure that the processes are performed in a predetermined order, the dependency between the processes must be considered, i.e. the start time of each process v _i cannot be earlier than the time after all of its preceding processes are completed. Thus, for any process v _i and its predecessor v _j, a time constraint is added:

Where e _ji is the time interval between process v _j and process v _i. This constraint ensures dependencies between processes and engineering sequence and consistency.

The optimization problem is solved by constructing the mixed integer linear programming problem, and the process scheduling problem is converted into a standard mathematical optimization problem. By using a linear programming solver (e.g., CPLEX, gurobi, etc.), the optimal start time x _i of all the processes can be found, thereby obtaining an optimized process scheduling scheme.

In this embodiment, by mixed integer linear programming, the construction period can be effectively minimized under the constraint of resources, and all the procedures can be ensured to be executed according to the dependency relationship sequence, and resources can be reasonably allocated. The optimization method provides a construction manager with an accurate construction period control and resource scheduling scheme, and is beneficial to improving the execution efficiency of the whole project.

S4, based on an optimized procedure scheduling scheme, a multi-stage queuing theory model is established, and scheduling of mechanical equipment is optimized

After the process scheduling optimization is completed, the scheduling of the mechanical equipment is further optimized, so that the waiting time and the non-operation time of the mechanical equipment among the processes are reduced. For this purpose, a multi-stage queuing theory model is built based on an optimized process scheduling scheme, simulating and optimizing the scheduling flow of the mechanical equipment in each process. According to the method, the dispatching of the mechanical equipment among different working procedures is optimized, so that the equipment can be switched among the different working procedures in an efficient mode, and smooth construction is ensured.

In this embodiment, the scheduling of the mechanical device is optimized by a multi-stage queuing theory model, and the specific steps are as follows:

defining the task arrival rate and the service rate in the model first, for each process, a task arrival rate λ _i and a service rate μ _i of the mechanical device are defined, where λ _i represents the task arrival frequency of the device at process v _i and μ _i represents the rate at which the device completes the process task.

Determining the utilization rate of the device, namely calculating the utilization rate rho _i of the device according to the task arrival rate lambda _i and the service rate mu _i, wherein the formula is as follows:

In order to ensure the stability of the device, i.e. that the device can perform tasks in time, ρ _i <1 must be met, indicating that the device is not overloaded.

Calculating the average wait time of the device to reduce the idle or wait time of the device, the average wait time W _i of the device in each process is calculated. The formula is:

This formula describes the average waiting time of the device in the current process. By reducing W _i, the switching efficiency of the equipment between different procedures can be optimized, and delays in operation are reduced.

The average task number in the system is further calculated as an average task number L _i in the system, which represents the number of tasks waiting to be processed at the same time in the current process. The calculation formula is as follows:

L_i＝λ_iW_i

This value is used to measure the amount of backlog of the task that the device needs to process in the current process. The number of backlog tasks can be reduced by optimizing the scheduling flow, and the high-efficiency work of the equipment is ensured.

Optimization of the multi-stage queuing model by establishing a schedule of devices for all processes as a multi-stage queuing system, each process as a service node, and devices as clients in a waiting queue. Under the complex conditions of multiple procedures and multiple devices, the scheduling sequence of the devices can be further optimized, so that each device can complete tasks in time, and the waiting time among the procedures is reduced. By balancing and optimizing the equipment scheduling for each process in the system, the efficiency of equipment use is maximized.

In this embodiment, by introducing the multi-stage queuing theory model, the scheduling process of the mechanical device can be optimized, smooth switching of the device between different processes is ensured, and non-operation time is reduced. The equipment can be effectively scheduled and optimized by calculating and analyzing the task arrival rate, the service rate, the waiting time and the backlog task quantity of the equipment, the utilization efficiency of mechanical equipment on a construction site is improved, the operation interruption is reduced, and further the engineering is ensured to be completed on schedule.

According to the invention, the equipment scheduling is optimized through the queuing theory model, so that the waiting time of mechanical equipment can be effectively reduced, the equipment utilization rate is improved, and the overall construction efficiency is improved. The method provides scientific basis for equipment scheduling for construction managers, so that equipment can be switched between different working procedures more smoothly, the condition of equipment idling or overload is avoided, and construction cost is saved and construction period is shortened.

The tissue design optimizing device for improving the working efficiency of the earth-rock engineering machinery described below and the tissue design optimizing method for improving the working efficiency of the earth-rock engineering machinery described above can be correspondingly referred to each other.

Referring to fig. 2, the present invention further provides an organization design optimization apparatus for improving the working efficiency of an earth-rock engineering machine, including:

A process modeling module 100 for modeling each process in the earth-rock engineering;

A critical path calculation module 200 for identifying a critical path of the project based on the earliest and latest start times of the critical path calculation process;

The scheduling optimization module 300 is configured to optimize multi-task scheduling by using mixed integer linear programming, and minimize a construction period;

The queuing theory optimization module 400 is configured to optimize scheduling of the mechanical device based on a multi-stage queuing theory model.

The apparatus of this embodiment may be used to execute the above method embodiments, and the principle and technical effects are similar, and are not repeated herein.

Referring to fig. 3, the present invention further provides a computer device 40, comprising a processor 41 and a memory 42, the memory 42 storing a computer program executable by the processor, the computer program executing the method as described above when executed by the processor.

The present invention also provides a storage medium 43, on which storage medium 43 a computer program is stored which, when run by a processor 41, performs a method as above.

The storage medium 43 may be implemented by any type of volatile or nonvolatile Memory device or combination thereof, such as a static random access Memory (Static Random Access Memory, SRAM for short), an electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), an erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), a Programmable Read-Only Memory (PROM for short), a Read-Only Memory (ROM for short), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims

1. A method for optimizing the organizational design of improving the operating efficiency of earthwork engineering machinery, characterized in that it comprises the following steps:

Through the directed acyclic graph, each process in the earthwork project is modeled. The process nodes represent specific tasks, and the edges represent the dependencies between processes, thus generating a process relationship model.

Based on the process relationship model, use the critical path method to calculate the earliest and latest start time of each process, identify the critical path of the project, and determine the key processes that affect the project duration;

Based on the identified key processes, mixed integer linear programming is used to optimize process scheduling to ensure that the project duration is minimized under resource constraints and to generate an optimized process scheduling plan;

Based on the optimized process scheduling plan, a multi-stage queuing theory model is established to optimize the scheduling of mechanical equipment.

2. The organizational design optimization method for improving the operating efficiency of earthwork engineering machinery according to claim 1 is characterized in that the step of modeling each process in the earthwork engineering by means of a directed acyclic graph comprises:

Obtain all process information of earthwork engineering, define process set V = {v ₁ , v ₂ , ..., v _n }, each process _vi represents a specific work task;

According to the dependency relationship between the processes, a directed edge set E = {( _vi , _vj )) between the processes is constructed, where each edge ( _vi , _vj ) indicates that process _vj can only start after process _vi is completed;

For each process v _i , determine its execution time t _i , and generate a process execution time set T = {t ₁ , t ₂ , ..., t _n };

Define the weight e _ij of each edge ( _vi , _vj ) to represent the time interval between process _vi and process _vj , and generate an edge weight set W = {e _ij };

The process dependency model is established through a directed acyclic graph G = (V, E), and the order and dependency of process nodes are determined by directed edges.

3. The organizational design optimization method for improving the operating efficiency of earthwork engineering machinery according to claim 1 is characterized in that the steps of calculating the earliest start time and the latest start time of each process based on the process relationship model using the critical path method, identifying the critical path of the project, and determining the key processes that affect the project duration include:

Based on the process relationship model generated by the directed acyclic graph, determine the dependencies of all processes _vi and their execution time _ti ;

Calculate the earliest start time ES(v _i ) of each process, which is based on the completion time of all previous processes and the time interval e _ij between the previous process and the current process. The calculation formula for the earliest start time is:

Where ES( _vi ) represents the earliest start time of process _vi , and predecessors of _vi represent all processes that must be completed before _vi ;

Calculate the latest start time LS(v _i ) of each process. The latest start time is based on the earliest start time of all subsequent processes to ensure that the process can be completed without affecting the construction period. The calculation formula is:

Where LS( _vi ) represents the latest start time of process _vi , and successors of _vi represents all processes started after _vi ;

By comparing the earliest start time and the latest start time, the free float time FT(v _i ) of each process is determined, that is, the time that the process can be delayed:

FT( _vi )＝LS( _vi )-ES( _vi )

When FT(v _i )＝0, it indicates that process v _i is on the critical path of the project;

All processes with zero free float time are combined to form the critical path of the project. The processes on the critical path directly affect the total duration of the project.

Based on the critical path, identify and determine the key processes that affect the project duration, and use them as the focus of subsequent process scheduling optimization.

4. The organizational design optimization method for improving the operating efficiency of earthwork engineering machinery according to claim 1 is characterized in that, based on the identified key processes, mixed integer linear programming is used to optimize the process scheduling to ensure that the project duration is minimized under resource constraints, and the step of generating the optimized process scheduling plan includes:

Based on the identified key processes, define the start time variable x _i of each process v _i . Each variable x _i is an integer, representing the actual start time of process v _i .

Construct an objective function to minimize the completion time of the last process, thereby minimizing the duration of the entire project. The objective function expression is:

Among them, _ti is the execution time of process _vi , and V is the set of all processes;

Resource constraints are introduced to ensure that each process is executed with the necessary resources. For each resource k, the expression is:

Where _Vk represents the set of processes that require resource k, _yik represents the amount of resource k used by process _vi , and _Rk is the total available amount of resource k;

Add time constraints for dependencies between processes to ensure that any process v _i can only start after all its predecessor processes are completed. The constraint expression is:

Among them, e _ji represents the time interval between process v _j and process _vi , and E is the dependency edge set between processes;

By solving the above mixed integer linear programming problem, the optimal start time of each process is generated, forming an optimized process scheduling plan for the entire project.

5. The organizational design optimization method for improving the operating efficiency of earthwork engineering machinery according to claim 1 is characterized in that the step of establishing a multi-stage queuing theory model based on the optimized process scheduling scheme and optimizing the scheduling of mechanical equipment includes:

Based on the optimized process scheduling scheme, the type and quantity of mechanical equipment required for each process v _i are identified, the task arrival rate λ _i and service rate μ _i of each equipment are defined, and a multi-stage queuing model of the equipment is constructed;

The scheduling of mechanical equipment is modeled as a multi-stage queuing system, where each process in the system is a service node and the equipment is a customer in the waiting queue, ensuring smooth scheduling of equipment between different processes;

Define the utilization rate ρ _i of each equipment i at different process nodes to represent the equipment load. The calculation formula is:

Among them, λ _i is the task arrival rate of equipment i at a certain process node, μ _i is the service rate of the equipment, and ρ _i <1 is ensured to ensure system stability;

Calculate the average waiting time _Wi of each device at each process node. The calculation formula for the waiting time is:

Optimize equipment scheduling by reducing _Wi to ensure that the waiting time of mechanical equipment between processes is minimized;

Calculate the average number of tasks _Li for each device in the system using the formula:

L _i = λ _i W _i

Ensure that the number of tasks is within a reasonable range, avoid equipment overload or idleness, and balance the equipment utilization in each process by adjusting the task arrival rate λ _i and the service rate μ _i ;

Based on the multi-stage queuing theory model, the scheduling order of equipment between processes is dynamically adjusted according to process priority and equipment availability to ensure maximum equipment utilization and minimum waiting time;

Finally, the scheduling plan is optimized based on the equipment queuing model to reduce the transfer and waiting of equipment between different processes.

6. An organization design optimization device for improving the operating efficiency of earthwork engineering machinery, used for implementing the organization design optimization method for improving the operating efficiency of earthwork engineering machinery as claimed in any one of claims 1 to 5, characterized in that it comprises:

Process modeling module, used to model each process in earthwork engineering;

The critical path calculation module is used to calculate the earliest and latest start times of the process based on the critical path method and identify the critical path of the project;

Scheduling optimization module, which is used to optimize multi-process scheduling and minimize construction period using mixed integer linear programming;

The queuing theory optimization module is used to optimize the scheduling of mechanical equipment based on a multi-stage queuing theory model.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.