Organization design optimization method for improving work efficiency of earth-rock engineering machinery

Abstract

The invention relates to the field of earth-rock engineering, and discloses an organization design optimization method for improving the work efficiency of earth-rock engineering machinery, and the method comprises the following steps: S1, carrying out the modeling of each process in the earth-rock 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 the process relation model, identifying a critical path of the project, and determining a critical process which affects the project construction period; s3, on the basis of the identified key process, performing process scheduling optimization by using mixed integer linear programming to ensure that the project period is minimized under resource limitation, and generating an optimized process scheduling scheme; and S4, based on the optimized process scheduling scheme, establishing a multi-stage queuing theory model, and optimizing the scheduling of the mechanical equipment. By optimizing procedure scheduling and mechanical equipment scheduling, the construction efficiency of earth-rock engineering is remarkably improved, the construction period and resource waste are reduced, and it is ensured that a project is efficiently completed on schedule.

Description

Technical Field

[0001] The invention relates to the technical field of earthwork engineering, and in particular to an organizational design optimization method for improving the operating efficiency of earthwork engineering machinery. Background Art

[0002] In earthwork projects, the rational scheduling of mechanical equipment and the efficient coordination between processes are the key to ensuring smooth construction and timely completion of the project. However, there are still many problems in the current organization design and scheduling methods of earthwork projects, which affect the overall construction efficiency and the rational use of resources.

[0003] In the prior art, process scheduling is usually based on experience or simple static planning methods, lacking scientific optimization methods. The dependencies between processes are relatively complex, making it difficult to accurately and clearly model and analyze them. In this case, process scheduling often relies on subjective judgment and cannot effectively identify the key processes that have the greatest impact on the project schedule. Due to the lack of systematic process scheduling optimization methods, the identification of critical paths is inaccurate, resulting in delays in some processes that directly affect the progress of the entire project. Moreover, in a resource-constrained environment, how to efficiently and reasonably allocate resources to ensure the smooth completion of key processes is still a weak point in the prior art.

[0004] Secondly, the scheduling and configuration of mechanical equipment are inefficient, resulting in a waste of resources. At the construction site, the collaborative work between different mechanical equipment is not effectively optimized, and the equipment often has a long waiting time when switching between processes, or due to unreasonable scheduling, some equipment is idle while others are in an overloaded state. The existing technology lacks effective means to simulate the task load and work efficiency of equipment in different processes, making it impossible to dynamically adjust the scheduling of mechanical equipment. This not only prolongs the construction period, but also leads to a decrease in equipment utilization and increases project costs.

[0005] At the same time, as the scale of construction projects expands, the complexity of process and equipment scheduling is also increasing. Traditional scheduling methods are difficult to handle the complex scheduling problems of multiple processes and multiple equipment in large-scale projects. In particular, when dealing with dependencies and resource conflicts between processes, existing methods cannot provide effective solutions. This limitation makes the construction plan vulnerable to external factors, lacks flexibility and adjustability, and ultimately affects the progress and economic benefits of the project.

[0006] Therefore, the existing earthwork engineering scheduling methods are difficult to meet the requirements of modern engineering projects for efficient construction, precise scheduling and rational use of resources. There is an urgent need for a scientific and systematic organizational design optimization method that can optimize the scheduling of processes and mechanical equipment in a dynamic environment, improve equipment utilization, shorten construction period, and reduce resource waste in construction. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an organizational design optimization method for improving the operating efficiency of earthwork engineering machinery, which solves the problems of unreasonable process scheduling, low utilization rate of machinery and equipment, and difficulty in optimizing construction period in existing earthwork engineering.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an organizational design optimization method for improving the operating efficiency of earthwork engineering machinery, comprising the following steps:

[0009] 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.

[0010] 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;

[0011] 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;

[0012] Based on the optimized process scheduling plan, a multi-stage queuing theory model is established to optimize the scheduling of mechanical equipment.

[0013] Preferably, the step of modeling each process in the earthwork project by means of a directed acyclic graph comprises:

[0014] Get all process information of earthwork engineering and define process set V = {v1, v2, ..., v n}, each process v i Indicates a specific job task;

[0015] According to the dependency relationship between processes, a directed edge set E between processes is constructed. i , v j )}, where each edge (v i , v j ) represents process v i Post-process v j To begin;

[0016] For each process v i , determine its execution time t i , generate the process execution time set T = {t1, t2, ..., t n};

[0017] Define each edge (v i , vj )’s weight e ij , represents the process v i and process v j The time interval between them generates the edge weight set W = {e ij};

[0018] 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.

[0019] Preferably, 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:

[0020] Based on the process relationship model generated by the directed acyclic graph, determine all processes v i The dependencies and execution time t i ;

[0021] Calculate the earliest start time ES(v i ), which is based on the completion time of all previous processes and the time interval between the previous process and the current process. ij , the calculation formula for the earliest start time is:

[0022]

[0023] Among them, ES(v i ) represents process v i The earliest start time, predecessors of v i Indicates that all in v i The process that must be completed before

[0024] Calculate the latest start time LS(v i ), 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:

[0025]

[0026] Among them, LS(v i ) represents process v i The latest start time of v, successors of v i Indicates that all in v i The process that starts next;

[0027] By comparing the earliest start time and the latest start time, the free float time FT (v i), i.e. the time during which the process can be delayed:

[0028] FT(v i )=LS(v i )-ES(v i )

[0029] When FT(v i )=0, the process v is identified i Located on the critical path of the project;

[0030] 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.

[0031] 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.

[0032] Preferably, the step of optimizing the process scheduling by using mixed integer linear programming based on the identified key processes to ensure that the project duration is minimized under resource constraints and generating an optimized process scheduling plan includes:

[0033] Based on the identified key processes, define each process v i The start time variable x i , each variable x i is an integer representing the process v i The actual start time of

[0034] The objective function is constructed to minimize the completion time of the last process, thereby minimizing the duration of the entire project. The objective function expression is:

[0035]

[0036] Among them, t i For process v i Execution time, V is the set of all processes;

[0037] Resource constraints are introduced to ensure that each process is executed with the necessary resources. For each resource k, the expression is:

[0038]

[0039] Among them, V k represents the set of processes that require resource k, y ik Indicates process v i The number of resources used, R k is the total available amount of resource k;

[0040] Add time constraints for dependencies between processes to ensure that any process vi It starts only after all its predecessor processes are completed. The constraint expression is:

[0041]

[0042] Among them, e ji Indicates process v j and process v i The time interval between them, E is the dependency edge set between processes;

[0043] 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.

[0044] Preferably, the step of establishing a multi-stage queuing theory model based on the optimized process scheduling scheme to optimize the scheduling of mechanical equipment includes:

[0045] Based on the optimized process scheduling scheme, identify each process v i The type and quantity of mechanical equipment required, defining the task arrival rate λ of each equipment i and service rate μ i , build a multi-stage queuing model for equipment;

[0046] 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;

[0047] Define the utilization rate ρ of each equipment i at different process nodes i , represents the equipment load condition, and the calculation formula is:

[0048]

[0049] 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 device, ensuring ρ i <1 to ensure system stability;

[0050] Calculate the average waiting time W of each device at each process node i , the calculation formula of waiting time is:

[0051]

[0052] By reducing W i To optimize equipment scheduling and ensure that the waiting time of mechanical equipment between processes is minimized;

[0053] Calculate the average number of tasks L for each device in the system i, the formula is:

[0054] L i =λ i W i

[0055] Ensure that the number of tasks is within a reasonable range and avoid device overload or idleness by adjusting the task arrival rate λ i and service rate μ i To balance the equipment utilization in each process;

[0056] 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;

[0057] Finally, the scheduling plan is optimized based on the equipment queuing model to reduce the transfer and waiting of equipment between different processes.

[0058] The present invention also provides an organizational design optimization device for improving the operating efficiency of earthwork engineering machinery, comprising:

[0059] Process modeling module, used to model each process in earthwork engineering;

[0060] 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;

[0061] Scheduling optimization module, which is used to optimize multi-process scheduling and minimize construction period using mixed integer linear programming;

[0062] The queuing theory optimization module is used to optimize the scheduling of mechanical equipment based on a multi-stage queuing theory model.

[0063] The present invention also provides 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 above method is implemented.

[0064] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0065] The present invention provides an organizational design optimization method for improving the operating efficiency of earthwork engineering machinery. It has the following beneficial effects:

[0066] 1. The present invention models the process through a directed acyclic graph, clearly describes the dependency between the processes, and avoids cyclic dependency and execution conflict between the processes. This ensures the rationality and order of the entire construction process, thereby improving the coordination between the processes.

[0067] 2. The present invention calculates the earliest and latest start times of each process through the critical path method, and identifies the key processes that directly affect the project duration. Critical path analysis provides the core processes for duration control, allowing construction managers to focus resources and energy on these key points to ensure that the project is completed on schedule.

[0068] 3. The present invention uses mixed integer linear programming to optimize process scheduling and generates the optimal process start time and resource allocation scheme under resource constraints. This optimization scheme can effectively reduce resource conflicts and process delays and minimize construction period.

[0069] 4. The present invention optimizes the scheduling of mechanical equipment through a multi-stage queuing theory model, calculates the task arrival rate and service rate of the equipment between each process, and reduces the average waiting time of the mechanical equipment. This improves the utilization efficiency of the equipment, avoids equipment idleness or overload, and thus improves the overall construction efficiency.

[0070] 5. The present invention calculates and optimizes the task volume and utilization rate of the equipment to ensure that the equipment can operate under a reasonable load and avoid task backlog. The switching of the equipment between different processes is more efficient, which maximizes the utilization rate of the equipment.

[0071] 6. The present invention significantly shortens the total construction period of the project by optimizing process scheduling and equipment scheduling. The optimal scheduling scheme reduces the time waste between processes and the non-operating time of equipment, thereby speeding up the overall construction progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0073] Figure 2 It is a schematic diagram of the structure of the device of the present invention;

[0074] Figure 3 It is a schematic diagram of the computer device structure of the present invention.

[0075] Among them, 100, process modeling module; 200, critical path calculation module; 300, scheduling optimization module; 400, queuing theory optimization module; 40, computer equipment; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] Please refer to the attached Figure 1 The embodiment of the present invention provides an organizational design optimization method for improving the operating efficiency of earthwork engineering machinery, comprising the following steps:

[0078] S1. Modeling each process in earthwork engineering through directed acyclic graph

[0079] First, each process in the earthwork project is modeled through a directed acyclic graph (DAG) to clarify the dependencies between processes, providing a basis for subsequent critical path analysis, construction period optimization, and equipment scheduling. In this step, the process node represents a specific task, and the sequence and dependencies between processes are represented by directed edges to form a complete process relationship model. This step ensures the rationality of process execution and provides an accurate scheduling basis for the optimization plan.

[0080] In this embodiment, each process of the earthwork project is modeled by the following steps:

[0081] First, obtain all process information of earthwork engineering and define process set V = {v1, v2, ..., v n}, where each process v i Represents a specific job task. This collection contains all the job tasks that need to be executed in the project.

[0082] Then, according to the logical and physical dependencies between processes, a directed edge set E between processes is established. i , v j )}, where each edge e ij Indicates process v i After completion, process v j This side collection clarifies the sequence of processes and ensures the smooth execution of subsequent processes.

[0083] For each process v i , determine its execution time t i , generate the process execution time set T = {t1, t2, ..., t n This time information is used for subsequent critical path method time calculations to ensure that the execution time of all processes is accurately recorded and managed.

[0084] Next, for each edge (vi , v j ) Determine a time interval e ij , represents the process v i and process v j The interval time between them forms the edge weight set W = {e ij}. These time intervals take into account possible setup times or other dependencies between operations.

[0085] Finally, a process dependency model is established through a directed acyclic graph G = (V, E). The nodes in this graph represent processes, and the edges represent the dependencies and execution order between processes, ensuring that each process is executed in order without circular dependencies.

[0086] In this embodiment, in order to further ensure the rationality of the process, the generated process dependency model is verified to be acyclic. The acyclic graph ensures the feasibility of process execution, that is, all processes can be executed in the order of dependency, avoiding execution blocking problems caused by circular dependencies.

[0087] In this embodiment, after the process model is established, the dependent path length of each process can be calculated to verify the dependency relationship between the processes and the correctness of the model, and provide accurate data support for subsequent process scheduling and optimization.

[0088] Through this directed acyclic graph modeling step, a structured model and data support are provided for subsequent critical path analysis, construction period optimization and mechanical equipment scheduling.

[0089] S2. Based on the process relationship model, use the critical path method to calculate the earliest start time and the latest start time of each process and identify the critical path of the project

[0090] After constructing the directed acyclic graph (DAG) model of the process, the earliest start time and the latest start time of each process are calculated through the critical path method (CPM), the critical path of the project is identified, and the key processes that affect the project duration are determined. The main purpose of this step is to identify which processes have a direct impact on the project duration and provide clear goals for subsequent duration optimization and scheduling optimization.

[0091] In this embodiment, the critical path method (CPM) is used to calculate the time of each process, and the specific steps are as follows:

[0092] First, the dependencies between processes are obtained through the directed acyclic graph model G = (V, E) constructed in the previous step. i , get its execution time t i And the dependency edges e between processes ij , represents the process v j In process v iThe required time interval is completed before.

[0093] In this embodiment, the earliest start time (ES) of each process is calculated using the critical path method. i ) is determined by the earliest time when all the previous processes of this process are completed. The calculation formula is as follows:

[0094]

[0095] Among them, ES(v i ) represents process v i The earliest start time, t j is the previous process v j The execution time, e ij is the previous process v j and the current process v i The time interval between.

[0096] In this embodiment, the latest start time (LS) of each process is calculated, that is, the latest time that the process can start without affecting the project duration. The calculation formula of the latest start time is:

[0097]

[0098] Among them, LS(v i ) represents process v i The latest start time, LS(v j ) is the subsequent process v j The latest start time, e ij is the time interval between the current process and the subsequent process.

[0099] In this embodiment, by calculating the earliest and latest start time of each process, the free floating time (FT) of each process, that is, the time that the process can be delayed, can be further determined. The calculation formula of the free floating time is:

[0100] FT(v i )=LS(v i )-ES(v i )

[0101] When FT(v i )=0, the process v is identified i On the critical path of the project.

[0102] In this embodiment, the critical path consists of all processes with zero free float time. The processes on this path directly determine the overall duration of the project, because any process delay on the critical path will cause a delay in the project duration. Therefore, identifying these critical processes is crucial for project management.

[0103] The earliest and latest start times of the processes calculated by the critical path method can clarify which processes must be strictly executed according to the plan and which processes have a certain degree of scheduling flexibility. This provides a data basis for subsequent construction period optimization and resource scheduling, and helps construction managers to reasonably plan the construction progress.

[0104] S3. Based on the identified key processes, use mixed integer linear programming to optimize process scheduling

[0105] After identifying the key processes, we further use mixed integer linear programming (MILP) to optimize the process scheduling. This step builds an optimization model to ensure the optimal scheduling of the processes under limited resources and time constraints, thereby minimizing the overall duration of the project. The goal of this step is to determine the optimal start time and resource allocation plan for the process to ensure that the key processes are completed on schedule while maximizing resource utilization efficiency.

[0106] In this embodiment, mixed integer linear programming is specifically used to optimize process scheduling, and the steps are as follows:

[0107] Define decision variables: First, define the integer decision variable x for the process start time i , where x i Indicates process v i The actual start time of the process. The value of this variable is what we need to optimize, representing the scheduling time of each process.

[0108] Establishing the objective function: The main goal of optimization is to minimize the duration of the entire project, that is, to minimize the completion time of the last process in the project. Therefore, the objective function can be expressed as:

[0109]

[0110] Among them, x i For process v i The start time, t i is the execution time of the process. By minimizing this objective function, the total duration of the project can be kept as short as possible.

[0111] Resource constraints: To ensure that the process can be executed with limited resources, constraints must be defined for each resource k. For each resource k, suppose there is a process set V k Using this resource, the constraint expression is:

[0112]

[0113] Among them, y ik Indicates process v i The amount of resources used, Rk is the maximum available amount of resource k. This constraint ensures that the available upper limit of each resource will not be exceeded, avoiding resource conflicts.

[0114] Process dependency constraints: In order to ensure that the processes are executed in the predetermined order, the dependencies between the processes must be considered, that is, each process v i The start time of a process cannot be earlier than the time when all its predecessor processes are completed. Therefore, for any process v i and its predecessor process v j , add time constraints:

[0115]

[0116] Among them, e ji For process v j and process v i This constraint ensures the dependencies between processes and guarantees the order and continuity of the project.

[0117] Solve the optimization problem: By constructing the above mixed integer linear programming problem, the process scheduling problem is transformed into a standard mathematical optimization problem. Using a linear programming solver (such as CPLEX, Gurobi, etc.), the optimal start time x of all processes can be found. i , thus obtaining the optimized process scheduling plan.

[0118] In this embodiment, mixed integer linear programming can effectively minimize the construction period under resource constraints, ensure that all processes are executed in the order of dependencies, and allocate resources reasonably. This optimization method provides construction managers with accurate construction period control and resource scheduling solutions, which helps to improve the execution efficiency of the overall project.

[0119] S4. Based on the optimized process scheduling scheme, establish a multi-stage queuing theory model to optimize the scheduling of mechanical equipment

[0120] After completing the process scheduling optimization, the scheduling of mechanical equipment is further optimized to reduce the waiting time and non-operating time of mechanical equipment between processes. To this end, based on the optimized process scheduling scheme, a multi-stage queuing theory model is established to simulate and optimize the scheduling process of mechanical equipment in each process. This step optimizes the scheduling of mechanical equipment between different processes so that the equipment can switch between different processes in an efficient manner to ensure smooth construction.

[0121] In this embodiment, the scheduling of mechanical equipment is optimized through a multi-stage queuing theory model, and the specific steps are as follows:

[0122] Define the task arrival rate and service rate in the model: First, for each process, define the task arrival rate λ of the mechanical equipmenti and service rate μ i , where λ i Indicates that the equipment is in process v i The task arrival frequency, μ i Indicates the rate at which the equipment completes the task of this process.

[0123] Determine equipment utilization: Based on the task arrival rate λ i and service rate μ i , the utilization rate of the equipment can be calculated i , the formula is:

[0124]

[0125] In order to ensure the stability of the equipment, that is, the equipment can complete the task in time, ρ must be satisfied i <1, indicating that the device will not be overloaded.

[0126] Calculate the average waiting time of the equipment: In order to reduce the idle or waiting time of the equipment, it is necessary to calculate the average waiting time W of the equipment in each process i The formula is:

[0127]

[0128] This formula describes the average waiting time of the equipment in the current process. i , which can optimize the switching efficiency of equipment between different processes and reduce delays in operations.

[0129] Average number of tasks in the system: Further, calculate the average number of tasks L in the system i , which indicates the number of tasks waiting to be processed in the current process. The calculation formula is:

[0130] L i =λ i W i

[0131] This value is used to measure the backlog of tasks that the equipment needs to handle in the current process. By optimizing the scheduling process, the number of backlog tasks can be reduced to ensure that the equipment works efficiently.

[0132] Optimization of multi-stage queuing model: By establishing the equipment scheduling of all processes as a multi-stage queuing system, each process is a service node and the equipment is a customer in the waiting queue. In the complex situation of multiple processes and multiple equipment, the scheduling order of the equipment can be further optimized so that each equipment can complete the task in time and reduce the waiting time between each process. By balancing and optimizing the equipment scheduling of each process in the system, the utilization efficiency of the equipment can be maximized.

[0133] In this embodiment, by introducing a multi-stage queuing theory model, the scheduling process of mechanical equipment can be optimized to ensure smooth switching of equipment between different processes and reduce non-operating time. By calculating and analyzing the task arrival rate, service rate, waiting time and backlog of tasks of the equipment, the scheduling of equipment can be effectively optimized, the utilization efficiency of mechanical equipment at the construction site can be improved, and operation interruptions can be reduced, thereby ensuring that the project is completed on schedule.

[0134] The present invention optimizes equipment scheduling through a queuing theory model, which can effectively reduce the waiting time of mechanical equipment, improve equipment utilization, and thus improve overall construction efficiency. This method provides construction managers with a scientific basis for equipment scheduling, making the switching of equipment between different processes smoother, avoiding equipment idleness or overload, and helping to save construction costs and shorten construction periods.

[0135] The organizational design optimization device for improving the operating efficiency of earth-moving engineering machinery described below and the organizational design optimization method for improving the operating efficiency of earth-moving engineering machinery described above can be referenced to each other.

[0136] Please refer to the attached Figure 2 The present invention also provides an organizational design optimization device for improving the operating efficiency of earthwork engineering machinery, comprising:

[0137] The process modeling module 100 is used to model each process in the earthwork engineering;

[0138] A critical path calculation module 200, for calculating the earliest and latest start times of a process based on a critical path method, and identifying a critical path of a project;

[0139] The scheduling optimization module 300 is used to optimize the scheduling of multiple processes and minimize the construction period by using mixed integer linear programming;

[0140] The queuing theory optimization module 400 is used to optimize the scheduling of mechanical equipment based on a multi-stage queuing theory model.

[0141] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, which will not be repeated here.

[0142] Please refer to the attached Figure 3 The present invention further provides a computer device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is executed.

[0143] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41, the above method is executed.

[0144] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0145] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An organizational design optimization method for improving the operating efficiency of earthwork engineering machinery, characterized in that: The following steps are involved: 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 steps of modeling each process in the earthwork project by using a directed acyclic graph include: Get all process information of earthwork engineering and define process set V = {v1, v2, ..., v n }, each process v i Indicates a specific job task; According to the dependency relationship between processes, a directed edge set E between processes is constructed. i , v j )), where each edge (v i , v j ) represents process v i Post-process v j To begin; For each process v i , determine its execution time t i , generate the process execution time set T = {t1, t2, ..., t n }; Define each edge (v i , v j )’s weight e ij , represents the process v i and process v j The time interval between them generates the 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 using the critical path method to calculate the earliest start time and the latest start time of each process based on the process relationship model, 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 all processes v i The dependencies and execution time t i ; Calculate the earliest start time ES(v i ), which is based on the completion time of all previous processes and the time interval between the previous process and the current process. ij , the calculation formula for the earliest start time is: Among them, ES(v i ) represents process v i The earliest start time, predecessors of v i Indicates that all in v i The process that must be completed before Calculate the latest start time LS(v i ), 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: Among them, LS(v i ) represents process v i The latest start time of v, successors of v i Indicates that all in v i The process that starts next; By comparing the earliest start time and the latest start time, the free float time FT (v i ), i.e. the time during which the process can be delayed: FT(v i )=LS(v i )-ES(v i ) When FT(v i )=0, the process v is identified i Located 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: The steps of optimizing the process scheduling by using mixed integer linear programming based on the identified key processes to ensure that the project duration is minimized under resource constraints and generate an optimized process scheduling plan include: Based on the identified key processes, define each process v i The start time variable x i , each variable x i is an integer representing the process v i The actual start time of 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, t i For process v i Execution time, 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: Among them, V k represents the set of processes that require resource k, y ik Indicates process v i The number of resources used, R k is the total available amount of resource k; Add time constraints for dependencies between processes to ensure that any process v i It starts only after all its predecessor processes are completed. The constraint expression is: Among them, e ji Indicates process v j and process v i The time interval between them, 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, characterized in that: The steps of establishing a multi-stage queuing theory model based on the optimized process scheduling scheme and optimizing the scheduling of mechanical equipment include: Based on the optimized process scheduling scheme, identify each process v i The type and quantity of mechanical equipment required, defining the task arrival rate λ of each equipment i and service rate μ i , build a multi-stage queuing model for equipment; 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 of each equipment i at different process nodes i , represents the equipment load condition, and 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 device, ensuring ρ i <1 to ensure system stability; Calculate the average waiting time W of each device at each process node i , the calculation formula of waiting time is: By reducing W i To optimize equipment scheduling and ensure that the waiting time of mechanical equipment between processes is minimized; Calculate the average number of tasks L for each device in the system i , the formula is: L i =λ i W i Ensure that the number of tasks is within a reasonable range and avoid device overload or idleness by adjusting the task arrival rate λ i and service rate μ i To balance the equipment utilization in each process; 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 organizational design optimization device for improving the operating efficiency of earth-moving engineering machinery, used for implementing the organizational design optimization method for improving the operating efficiency of earth-moving engineering machinery as claimed in any one of claims 1 to 5, characterized in that: include: 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, characterized in that: 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, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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