Engineering budget optimization method based on budget mechanical requirements

Abstract

The invention discloses an engineering budget optimization method based on budget machinery requirements, and the method comprises the steps: carrying out the global optimization of the scheduling and resource configuration of construction machinery through building a mathematical optimization model which takes the minimization of the total cost of a project as a target, and combining a heuristic algorithm and distributed calculation, the problems that in the background technology, manual resource allocation measurement and calculation are low in efficiency and prone to errors, the mechanical vacancy rate is high, multi-dimensional cost control is insufficient, and a scheduling scheme lacks global optimization are effectively solved. According to the method, by obtaining parameters such as budget machine demand machine-team quantity, resource curves and operation priorities and applying multi-dimensional constraint conditions such as machine daily workload, scheduling sequence and field entering and exiting frequency, it is ensured that a generated scheduling scheme meets actual construction requirements. A plurality of cost factors such as mechanical use cost, idle cost, in-out cost and the like are introduced into the optimization model, and comprehensive minimization of various costs is realized in the form of an objective function, so that the total cost of a project is effectively reduced.

Description

Technical Field

[0001] The present application relates to the technical field of engineering budget optimization, and in particular to an engineering budget optimization method based on budget machinery requirements. Background Art

[0002] As the scale of large-scale construction projects gradually expands, the requirements for resource management and cost control in the construction process are also increasing. The reasonable configuration of construction machinery is an important link to ensure the smooth progress of engineering projects. Especially in the field of construction cost management system (CCMS, Construction Cost Management System), how to effectively configure construction machinery resources to meet project needs has become one of the key issues that enterprises are concerned about. Reasonable configuration of construction machinery is not only related to the guarantee of construction progress, but also directly affects the level of project cost and resource utilization efficiency.

[0003] At present, the configuration and scheduling of construction machinery mainly rely on personal experience for preliminary calculation and resource allocation. Construction units usually manually input and calculate resources according to the demand for on-site operations and the actual situation of construction machinery, and then formulate machinery scheduling plans. However, this method has significant problems:

[0004] Low efficiency and prone to errors: Manual resource allocation and calculation requires a lot of time and effort, making it difficult to quickly respond to dynamically changing needs in engineering projects. Omissions or errors are prone to occur during the calculation process, affecting the accuracy of cost calculation.

[0005] Waste of resources: The idle rate of mechanical equipment is high, resulting in insufficient resource utilization and increasing the total cost of the project.

[0006] Insufficient scheduling optimization: Existing technologies make it difficult to comprehensively consider factors such as machinery use costs, scheduling costs, entry and exit costs, and job priorities, resulting in a lack of global optimality in scheduling plans and an inability to achieve effective cost control.

[0007] Therefore, in the allocation and scheduling of construction machinery resources, the low efficiency and error-proneness of manual resource allocation, high machinery idle rate, insufficient multi-dimensional cost control, and lack of global optimization of scheduling plans have become problems that need to be solved urgently. Summary of the invention

[0008] The present application provides an engineering budget optimization method based on budget machinery demand, aiming to solve the problems in the existing technology of construction machinery resource allocation and scheduling, such as low efficiency and easy errors in manual resource allocation, high machinery idle rate, insufficient multi-dimensional cost control and lack of global optimization of scheduling schemes.

[0009] A method for optimizing engineering budget based on budgeted machinery requirements, the method comprising:

[0010] According to the operation requirements of the project, obtain the required shifts of various budgeted machines Resource curves, resource calendars, and activity priority known parameters;

[0011] Establish a mathematical optimization model with the goal of minimizing the total project cost, where the total cost C includes the machinery use cost C 0 、Machinery idle cost C 1 、Machinery entry and exit costs C 2 , Machinery depreciation cost C 3 , construction machinery preference cost C 4 , Mechanical dispatching cost C 5 and mechanical dispatch stay penalty cost C 6 ,in:

[0012] C=C 0 +C 1 +C 2 +C 3 +C 4 +C 5 +C 6 ;

[0013] The presence status of construction machinery Work surface allocation status Mechanical dispatch status And the number of hours of machine replacement budget Set as decision variable;

[0014] The mathematical optimization model is solved by using a heuristic algorithm combined with distributed computing to obtain a construction machinery scheduling solution that minimizes the total cost;

[0015] Output the scheduling plan of the construction machinery, including the number of machines on site every day, the distribution of work surfaces and the number of machines to be replaced with the budgeted machines.

[0016] In the above solution, optionally, the calculation formulas for each cost item of the total cost C are as follows:

[0017] Machinery use cost:

[0018] Machinery Idle Cost:

[0019] Machinery entry and exit costs:

[0020] Machinery depreciation cost:

[0021] Construction Machinery Preference Cost:

[0022] The dispatching cost of construction machinery between working surfaces:

[0023]

[0024] Penalty cost:

[0025] In the above solution, optionally, constraints are imposed on the mathematical optimization model, including:

[0026] Budgeted machinery demand constraints:

[0027] Mechanical daily workload constraints:

[0028] Mechanical entry and exit frequency constraints:

[0029] In the above solution, optionally, the constraint condition also includes:

[0030] Fixed entry and exit of construction machinery:

[0031] Construction machinery cannot enter and exit the site frequently:

[0032] Construction machinery can only be on the work surface if it is present, and can only be on one work surface on the same day:

[0033]

[0034] The premise that construction machine j can work on the kth operation on the tth day is that the construction machine happens to be on the working surface to which the kth operation belongs:

[0035]

[0036] The premise that construction machine j can replace budget machine i on day t is that construction machine j is performing the operation corresponding to budget machine i on day t:

[0037]

[0038] For the working surface K, the construction machine j can be transferred out from the working surface K only if no construction machine of the same type as the construction machine j is transferred in within the specified time:

[0039]

[0040] For operation k, if there is no construction machine of the same type as construction machine j transferred in the next day, construction machine j can be transferred out of operation k;

[0041]

[0042] in, is whether the jth construction machine is present on day t, Is the jth construction machine in the A working surface, Is the jth construction machine working on the kth operation on the tth day? The number of shifts required to replace the i-th budgeted machine demand by the j-th construction machine on the t-th day, is whether the jth construction machine enters the site on the tth day, is the work surface index, indicating the A working surface, Index of construction machinery types, indicating class of construction machinery, k is the job index, indicating the kth job, i is the budget machinery index, indicating the ith class of budget machinery, j is the construction machinery index, indicating the jth construction machinery, t is the construction period index, indicating the tth day of construction period, Is the kth job a member of Working surface, 1 means it belongs to, 0 means it does not belong to, Is the i-th budget machine belonging to the k-th job, 1 means it belongs, 0 means it does not belong, Is the jth construction machine a member of the Class: construction machinery, 1 means it belongs to, 0 means it does not belong to, is the unit-shift usage cost of the j-th construction machine, is the idle cost per unit of the j-th construction machine, is the single entry and exit cost of the j-th construction machine, is the depreciation cost of the j-th construction machine per day, The unit incentive cost of replacing the i-th budgeted machine with the j-th construction machine, For the jth construction machine from The work surface is dispatched to the The scheduling cost of each work surface, is the penalty cost of the jth construction machine not being on any working surface, P ij is the preference of the j-th construction machine to the i-th budget machine, V j is the remaining depreciation period of the jth construction machine. For construction machines with fixed depreciation costs, the remaining depreciation period is fixed at 1 day. ij is the efficiency conversion coefficient when the i-th budget machine is replaced by the j-th construction machine, is the corrected maximum work shift of the j-th construction machine on the t-th day, Budget the required number of shifts for the i-th type of machinery on the tth day.

[0043] In the above solution, optionally, the heuristic algorithm includes the following steps:

[0044] Generate an initial solution based on resource requirements and constraints;

[0045] The initial solution is optimized by genetic algorithm, which includes population initialization, selection, crossover and mutation operations;

[0046] The solution is evaluated based on the fitness function, which is a total cost formula;

[0047] After multiple iterations, the optimal solution is output.

[0048] In the above solution, optionally, the distributed computing includes the following steps:

[0049] Decompose the optimization problem into multiple sub-problems and distribute them to different computing nodes;

[0050] Each computing node calculates the local optimal solution of the subproblem in parallel;

[0051] Aggregate and merge all local optimal solutions to generate an approximate global optimal solution;

[0052] The approximate global optimal solution is further optimized and the final result is output.

[0053] In the above scheme, optionally, the resource curve is used to describe the daily usage demand of various budgeted machines in each operation, and the resource calendar is used to define the maximum daily working time of the construction machinery.

[0054] In the above scheme, optionally, the job priority is used to determine the scheduling order of construction machinery between different work surfaces, and the job with a higher priority is given priority in resource allocation.

[0055] In the above solution, optionally, the scheduling time constraints between the working surfaces include:

[0056] Construction machinery from The work surface is dispatched to the The time constraints required for each operation surface:

[0057]

[0058] In the above scheme, optionally, the output scheduling scheme includes the presence status, job allocation and scheduling path of each type of construction machinery on each day.

[0059] Compared with the prior art, this application has at least the following beneficial effects:

[0060] Based on further analysis and research on the problems of the prior art, this application recognizes that in the prior art construction machinery resource allocation and scheduling, the resource calculation efficiency is low and error-prone through manual configuration, the idle rate of machinery is high, the multi-dimensional cost control is insufficient, and the scheduling scheme lacks global optimization. By establishing a mathematical optimization model with the goal of minimizing the total cost of the project, combined with heuristic algorithms and distributed computing, the scheduling and resource allocation of construction machinery are globally optimized, which effectively solves the problems of low efficiency and error-prone through manual configuration of resources, high idle rate of machinery, insufficient multi-dimensional cost control, and lack of global optimization of the scheduling scheme in the background technology. The method obtains parameters such as the budgeted machinery demand, resource curve, and job priority, and imposes multi-dimensional constraints such as the daily workload of the machinery, scheduling sequence, and entry and exit frequency to ensure that the generated scheduling scheme meets the actual construction needs. Multiple cost factors such as machinery use cost, idle cost, and entry and exit cost are introduced into the optimization model to achieve the comprehensive minimization of various costs in the form of an objective function, thereby significantly reducing the total project expenditure. At the same time, the initial solution is quickly generated by a heuristic algorithm, and the solution efficiency is improved by means of distributed computing, so that large-scale scheduling problems can be completed within a limited time. The final scheduling plan can not only dynamically adapt to changes in project needs, but also avoid excessive idleness and frequent entry and exit of machinery, improve machinery utilization, and significantly improve scheduling efficiency and economy, thereby fully meeting the efficient and low-cost resource management requirements of modern projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flowchart of an engineering budget optimization method based on budgeted machinery requirements provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0063] In one embodiment, Figure 1 As shown, a method for optimizing engineering budget based on budgeted mechanical requirements is provided, comprising the following steps:

[0064] According to the operation requirements of the project, obtain the required shifts of various budgeted machines D i t , resource curves, resource calendars and activity priority known parameters;

[0065] Establish a mathematical optimization model with the goal of minimizing the total project cost, where the total cost C includes the machinery use cost C 0 、Machinery idle cost C 1 、Machinery entry and exit costs C2 , Machinery depreciation cost C 3 , construction machinery preference cost C 4 , Mechanical dispatching cost C 5 and mechanical dispatch stay penalty cost C 6 ,in:

[0066] C=C 0 +C 1 +C 2 +C 3 +C 4 +C 5 +C 6 ;

[0067] The presence status of construction machinery Work surface allocation status Mechanical dispatch status And the number of hours of machine replacement budget Set as decision variable;

[0068] The mathematical optimization model is solved by using a heuristic algorithm combined with distributed computing to obtain a construction machinery scheduling solution that minimizes the total cost;

[0069] Output the scheduling plan of the construction machinery, including the number of machines on site every day, the distribution of work surfaces and the number of machines to be replaced with the budgeted machines.

[0070] In this embodiment, the calculation formulas for each cost item of the total cost C are as follows:

[0071] Machinery use cost:

[0072] Machinery Idle Cost:

[0073] Machinery entry and exit costs:

[0074] Machinery depreciation cost:

[0075] Construction Machinery Preference Cost:

[0076] The dispatching cost of construction machinery between working surfaces:

[0077]

[0078] Penalty cost:

[0079] In this embodiment, constraints are imposed on the mathematical optimization model, including:

[0080] Budgeted machinery demand constraints:

[0081] Mechanical daily workload constraints:

[0082] Mechanical entry and exit frequency constraints:

[0083] In this embodiment, the constraint conditions also include:

[0084] Fixed entry and exit of construction machinery:

[0085] Construction machinery cannot enter and exit the site frequently:

[0086] Construction machinery can only be on the work surface if it is present, and can only be on one work surface on the same day:

[0087]

[0088] The premise that construction machine j can work on the kth operation on the tth day is that the construction machine happens to be on the working surface to which the kth operation belongs:

[0089]

[0090] The premise that construction machine j can replace budget machine i on day t is that construction machine j is performing the operation corresponding to budget machine i on day t:

[0091]

[0092] For the working surface K, the construction machine j can be transferred out from the working surface K only if no construction machine of the same type as the construction machine j is transferred in within the specified time:

[0093]

[0094] For operation k, construction machine j can be transferred out of operation k only if no construction machine of the same type as construction machine j is transferred in the next day.

[0095]

[0096] in, is whether the jth construction machine is present on day t, Is the jth construction machine in the A working surface, Is the jth construction machine working on the kth operation on the tth day? The number of shifts required to replace the i-th budgeted machine demand by the j-th construction machine on the t-th day, is whether the jth construction machine enters the site on the tth day, is the work surface index, indicating the A working surface, Index of construction machinery types, indicating class of construction machinery, k is the job index, indicating the kth job, i is the budget machinery index, indicating the ith class of budget machinery, j is the construction machinery index, indicating the jth construction machinery, t is the construction period index, indicating the tth day of construction period, Is the kth job a member of Working surface, 1 means it belongs to, 0 means it does not belong to, Is the i-th budget machine belonging to the k-th job, 1 means it belongs, 0 means it does not belong, Is the jth construction machine a member of the Class: construction machinery, 1 means it belongs to, 0 means it does not belong to, is the unit-shift usage cost of the j-th construction machine, is the idle cost per unit of the j-th construction machine, is the single entry and exit cost of the j-th construction machine, is the depreciation cost of the j-th construction machine per day, The unit incentive cost of replacing the i-th budgeted machine with the j-th construction machine, For the jth construction machine from The work surface is dispatched to the The scheduling cost of each work surface, is the penalty cost of the jth construction machine not being on any working surface, P ij is the preference of the j-th construction machine to the i-th budget machine, V j is the remaining depreciation period of the jth construction machine. For construction machines with fixed depreciation costs, the remaining depreciation period is fixed at 1 day. ij is the efficiency conversion coefficient when the i-th budget machine is replaced by the j-th construction machine, is the corrected maximum work shift of the j-th construction machine on the t-th day, Budget the required number of shifts for the i-th type of machinery on the tth day.

[0097] In this embodiment, the heuristic algorithm includes the following steps:

[0098] Generate an initial solution based on resource requirements and constraints;

[0099] The initial solution is optimized by genetic algorithm, which includes population initialization, selection, crossover and mutation operations;

[0100] The solution is evaluated based on the fitness function, which is a total cost formula;

[0101] After multiple iterations, the optimal solution is output.

[0102] In this embodiment, the distributed computing includes the following steps:

[0103] Decompose the optimization problem into multiple sub-problems and distribute them to different computing nodes;

[0104] Each computing node calculates the local optimal solution of the subproblem in parallel;

[0105] Aggregate and merge all local optimal solutions to generate an approximate global optimal solution;

[0106] The approximate global optimal solution is further optimized and the final result is output.

[0107] In this embodiment, the resource curve is used to describe the daily usage requirements of various budgeted machines for each operation, and the resource calendar is used to define the maximum daily working hours of the construction machinery.

[0108] In this embodiment, the job priority is used to determine the scheduling order of construction machinery among different work surfaces, and the job with a higher priority is given priority in resource allocation.

[0109] In this embodiment, the scheduling time constraints between the working surfaces include:

[0110] Construction machinery from The work surface is dispatched to the The time constraints required for each operation surface:

[0111]

[0112] In this embodiment, the output scheduling plan includes the presence status, operation allocation and scheduling path of each type of construction machinery on each day.

[0113] Through the above specific implementation steps, the problems existing in the resource allocation and scheduling of construction machinery mentioned in the background technology can be significantly solved:

[0114] Through heuristic algorithms and distributed computing, it replaces the traditional scheduling method that relies on manual resource configuration and measurement, greatly improves computing efficiency, and can quickly respond to dynamically changing needs in engineering projects.

[0115] The optimization model controls the daily workload and replacement ratio of machinery through constraints to avoid excessive idleness of machinery, thereby improving resource utilization efficiency.

[0116] Multidimensional cost items (usage cost, idle cost, entry and exit cost, depreciation cost, etc.) are introduced into the model, and the total project cost is minimized through the optimization of the objective function, effectively reducing project expenses.

[0117] By comprehensively considering budget machinery requirements, operation priorities, time windows and other conditions, we ensure that the optimization plan meets actual construction requirements and improve the feasibility and applicability of the plan.

[0118] Genetic algorithms combined with distributed computing can generate scheduling solutions that are close to the global optimal within a limited time, avoiding the problem of local optimality in traditional methods, thereby significantly improving scheduling quality.

[0119] This embodiment can effectively solve the problems mentioned in the background technology, such as low efficiency and prone to errors in manual resource allocation, high machinery idle rate, insufficient multi-dimensional cost control, and lack of global optimization of scheduling schemes, and comprehensively improve the economy and efficiency of construction machinery resource allocation and scheduling.

[0120] In one embodiment, the algorithm is used to coordinate the dispatch of construction machinery, reduce the idle rate of machinery and equipment and the frequency of invalid dispatch, maximize resource utilization efficiency, and save construction costs for enterprises. When the constraints of the algorithm are used to meet the budgeted machinery requirements in the project, the optimal resource allocation is performed while minimizing the project cost, providing the machinery dispatcher with a cost-optimized configuration reference.

[0121] Through dynamic cost accounting models and automated scheduling optimization solutions, the comprehensive minimization of multi-dimensional costs such as construction machinery usage costs, scheduling costs, and entry and exit costs can be achieved, effectively reducing total project expenditures and improving project profit margins.

[0122] Combining heuristic algorithms with distributed computing frameworks can quickly find feasible solutions and optimal solutions, so that large-scale scheduling problems can be calculated and optimized results can be output within a limited time, providing immediate support for the formulation and adjustment of construction plans.

[0123] This embodiment introduces a multi-objective optimization mechanism in the engineering cost management system to balance the minimization of total cost and the optimal solution of scheduling time to meet the different priority requirements in actual business scenarios. The solution of replacing the budgeted machinery is optimized to minimize the total cost of the project while meeting the budgeted machinery requirements.

[0124] This embodiment is based on the model requirements and the data provided by the customer, with the goal of minimizing costs, and with the known parameters and various requirements provided by the customer as constraints. The mathematical algorithm model is designed and constructed, and the optimal feasible solution of the algorithm model is obtained to obtain: the number of a certain type of construction machinery on site on a certain day, the total number of shifts of a certain type of construction machinery replacing a certain type of budgeted machinery on a certain day, and the total cost value of the project under the current solution. The overall solution is based on the resource curve requirements under the operation, outputs the scheduling plan of mechanical resources between different operation surfaces, and reduces costs as much as possible.

[0125] The core mathematical model of this embodiment is divided into the following parts:

[0126] Objective function: defines the goal to be optimized by the model, that is, outputting the resource name, configuration quantity and status (i.e. scheduling requirements) of each job in each construction period to make the total cost more economical, and outputting the total cost.

[0127] Decision variables: represent the variable quantities in the model, namely the number of construction machines on site and the total number of shifts to replace the budgeted machines.

[0128] Constraints: Ensure that the solution meets various customer needs and known parameters, such as the unit cost of machinery, resource curves and construction period requirements, construction machinery maintaining continuous work, construction machinery cannot be dispatched during the continuous working period, construction machinery needs to meet job priorities during scheduling, the scheduling cost of construction machinery between work surfaces, and the idle cost of machinery resources.

[0129] Known parameters: Provide specific values ​​required by the model, such as resource curves, resource calendars, all resources under the job, daily scheduling costs, job scheduling costs, job scheduling time, job priority, and fixed entry and exit times.

[0130] Solution and result: The overall lowest cost. How to schedule these machines to meet the scheduling between operations, meet the resource curve requirements under the operation, and meet the cost between the two operation surfaces, and complete the project task as much as possible.

[0131] Overall implementation logic and steps:

[0132] Mathematical model construction and analysis:

[0133] The mathematical model construction of this project is essentially a multi-constrained resource optimization problem based on a distributed computing environment, which belongs to the category of large-scale optimization problems in operations research and management science. An approximate solution is given by combining heuristic algorithms with distributed solving, and then a global optimization solution is performed based on the approximate solution through heuristic algorithms. Specifically, the algorithm dynamically adjusts the weight of the objective function on the basis of ensuring that constraints such as resource requirements and scheduling time are met, and finds the optimal solution between reducing costs and improving resource utilization. This allows the most economical configuration method to be selected from a cost perspective, minimizing the total cost of the project.

[0134] Known conditions:

[0135] The budgeted machinery required for each operation, the resource curve requirements for each operation, and the working time for each operation.

[0136] Resource curve: The daily usage of each budgeted machine for each operation.

[0137] Resource Calendar: The maximum working hours of a resource on a particular day.

[0138] All resources under the job: All construction machinery under each job.

[0139] Daily dispatch cost: daily dispatch cost of each construction machinery, including daily dispatch working hours and daily dispatch cost.

[0140] Job scheduling cost: the scheduling cost of each type of construction machinery between different work surfaces.

[0141] Work scheduling time: the scheduling time of each type of construction machinery between different work surfaces.

[0142] Job priority: The scheduling priority of each construction machine among different jobs.

[0143] Fixed entry and exit times: entry and exit times for some construction machinery.

[0144] Development Solution:

[0145]

Objective function

[0146]

Decision variables

[0147] Whether the jth construction machine is present on day t (0-1);

[0148] Whether the jth construction machine is in Working surface (0-1);

[0149] Whether the jth construction machine is working on the kth operation on the tth day (0-1);

[0150] The number of shifts that the j-th construction machine replaces the i-th budgeted machine demand on the t-th day (continuous variable);

[0151] Whether the jth construction machine entered the site on the tth day (0-1 variable);

[0152]

Known parameters

[0153] Index and corresponding relationship:

[0154] Working surface index, indicating the A working surface;

[0155] Index of construction machinery types, indicating Construction machinery;

[0156] k: job index, indicating the kth job;

[0157] i: budget machine index, indicating the i-th type of budget machine;

[0158] j: construction machine index, indicating the jth construction machine;

[0159] t: duration index, indicating the duration of the tth day;

[0160] Does the kth job belong to Working surface, 1 means it belongs to, 0 means it does not belong to;

[0161] Whether the i-th budget machine belongs to the k-th job, 1 means it does, 0 means it does not; Does the jth construction machine belong to Class: construction machinery, 1 means it belongs to, 0 means it does not belong to;

[0162] Unit cost parameters:

[0163] The unit-shift usage cost of the j-th construction machine;

[0164] Idle cost per unit shift of the j-th construction machine;

[0165] The single entry and exit cost of the j-th construction machine (including entry and exit costs);

[0166] The depreciation cost of the j-th construction machine per day;

[0167] Unit incentive cost of replacing the i-th budget machine with the j-th construction machine (algorithm built-in parameter, the value is a small negative number) The jth construction machine is from The work surface is dispatched to the The scheduling cost of each work surface;

[0168] The penalty cost of the j-th construction machine not being on any working surface (a built-in parameter of the algorithm, with a large positive value);

[0169] P ij : The preference of the j-th construction machine to the i-th budget machine (the more preferred, the higher the value);

[0170] V j : The remaining depreciation period of the j-th construction machine. For construction machines with fixed depreciation costs, the remaining depreciation period is fixed at 1 day;

[0171] Requirements related parameters:

[0172] B ij : The efficiency conversion coefficient when the i-th budgeted machine is replaced by the j-th construction machine;

[0173] The corrected maximum work shift of the j-th construction machine on the t-th day;

[0174] The required number of shifts for the i-th budgeted machine on day t;

[0175] Other parameters:

[0176] B ij : The efficiency conversion coefficient when the i-th budgeted machine is replaced by the j-th construction machine;

[0177] The corrected maximum work shift of the j-th construction machine on the t-th day;

[0178] The required number of shifts for the i-th budgeted machine on day t;

[0179]

Model construction

[0180] Objective function: Total cost C = construction machinery use cost C 0 + Construction machinery idle cost C 1 + Construction machinery entry and exit costs C 2 + Construction machinery depreciation cost C 3 + Construction Machinery Preference Cost C 4 + Construction machinery operation surface scheduling cost C 5 + Penalty cost of construction machinery "staying" between work surfaces C 6 ;

[0181] C=C 0 +C 1 +C 2 +C 3 +C 4 +C 5 +C 6 ;

[0182] Cost of using construction machinery:

[0183]

[0184] Idle cost of construction machinery. When the number of replacement shifts exceeds one shift, there is no idle cost (max will be linearized during solution);

[0185]

[0186] The cost of construction machinery entering and leaving the site;

[0187]

[0188] Depreciation cost of construction machinery (min will be linearized during actual solution);

[0189]

[0190] construction machinery preference cost (virtual cost of model control soft constraints);

[0191]

[0192] The dispatching cost of construction machinery between work surfaces (the variables will be multiplied and linearized when solving);

[0193]

[0194] Penalty cost (to ensure that construction machinery does not "linger" when dispatching between work surfaces, and will be dispatched as quickly as possible to ensure the correct expression of dispatch costs);

[0195]

[0196] Constraints:

[0197] Construction machinery needs to meet the needs of budget machinery;

[0198]

[0199] When construction machinery replaces budgeted machinery, the daily work shifts cannot be exceeded;

[0200]

[0201] Construction machinery with a conversion factor of 0 cannot replace budget machinery;

[0202]

[0203] Fixed entry and exit for construction machinery (only restrictions are imposed on construction machinery with fixed entry and exit);

[0204]

[0205] Construction machinery cannot enter and exit the site frequently (it must wait for 6 months before entering the site after exiting the site);

[0206]

[0207] Construction machinery from The work surface is dispatched to the The time constraints required for each operation surface;

[0208]

[0209] Construction machinery can only be on the work surface if it is present, and can only be on one work surface on the same day;

[0210]

[0211] The premise for construction machine j to work on the kth operation on the tth day is that the construction machine happens to be on the kth operation

[0212] The work surface to which the work belongs;

[0213]

[0214] The premise that construction machine j can replace budget machine i on day t is that construction machine j on day t

[0215] The corresponding operation work of machine i is being budgeted;

[0216]

[0217] To avoid frequent scheduling, the idle time of construction machinery must be greater than (the time of scheduling on the work surface + 1 day) before scheduling can be carried out, and there must be no "adjustment" between work surfaces. Therefore, the constraint is equivalent to: for the work surface If no construction machinery of the same type as construction machinery j is transferred in within the specified time, construction machinery j can be removed from the working area. Call out.

[0218]

[0219] The idle time of a construction machine is greater than one day, so it can be scheduled under different operations on the same working surface. Therefore, the constraint equivalent is converted to: for operation k, if there is no construction machine of the same type as construction machine j transferred in the next day, construction machine j can be transferred out of operation k.

[0220]

[0221] The innovation of this embodiment is that it proposes a construction machinery resource scheduling optimization method based on a distributed computing environment. Combined with a heuristic algorithm, it generates an initial solution in a distributed manner and performs global optimization iterations to solve the complexity and real-time problems of large-scale construction machinery scheduling. The core is to organically combine multi-dimensional factors such as the job demand resource curve, machinery scheduling costs, and time constraints and priority constraints between work surfaces to construct a multi-constraint, multi-objective optimization model, and through innovative algorithm design, it realizes the rapid output of scheduling solutions and minimization of total costs in massive data scenarios. This method breaks through the limitation of traditional linear programming that it is difficult to efficiently solve complex nonlinear constraints, and has the remarkable characteristics of high computational efficiency, strong economic scheduling solutions, and high practical applicability.

[0222] The technical effects brought about by adopting the technical solution of this embodiment are as follows:

[0223] Improve computing efficiency: Through the combination of distributed computing and heuristic algorithms, the rapid generation and optimization of mechanical scheduling plans can be achieved in large-scale operation scenarios, and high-quality scheduling results can be output to meet real-time requirements.

[0224] Effectively reduce the total cost of the project: By considering the use cost, idle cost, depreciation cost, entry and exit cost and the newly added scheduling cost, a cost model that is closer to reality is constructed to achieve the goal of minimizing the total cost and effectively reduce the total cost of the project.

[0225] Optimize resource utilization: Based on constraints such as job demand resource curves and scheduling priorities, scientifically allocate machinery and equipment to reduce resource waste and improve machinery utilization, while avoiding inefficient behaviors such as frequent entry and exit.

[0226] Improve the applicability of scheduling plans: Taking into account actual construction conditions such as construction needs, work surface scheduling sequence, time windows and priorities, the generated scheduling plan has higher operability and flexibility to meet site management needs.

[0227] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for optimizing engineering budget based on budgeted machinery requirements, characterized in that: The following steps are involved: According to the operation requirements of the project, obtain the required shifts of various budgeted machines Resource curves, resource calendars, and activity priority known parameters; Establish a mathematical optimization model with the goal of minimizing the total project cost, where the total cost C includes the machinery use cost C 0 、Machinery idle cost C 1 、Machinery entry and exit costs C 2 , Machinery depreciation cost C 3 , construction machinery preference cost C 4 , Mechanical dispatching cost C 5 and mechanical dispatch stay penalty cost C 6 ,in: C=C 0 +C 1 +C 2 +C 3 +C 4 +C 5 +C 6 ; The presence status of construction machinery Work surface allocation status Mechanical dispatch status And the number of hours of machine replacement budget Set as decision variable; The mathematical optimization model is solved by using a heuristic algorithm combined with distributed computing to obtain a construction machinery scheduling solution that minimizes the total cost; Output the scheduling plan of the construction machinery, including the number of machines on site every day, the distribution of work surfaces and the number of machines to be replaced with the budgeted machines.

2. The engineering budget optimization method according to claim 1, characterized in that: The calculation formulas for each cost item of the total cost C are as follows: Machinery use cost: Machinery Idle Cost: Machinery entry and exit costs: Machinery depreciation cost: Construction Machinery Preference Cost: The dispatching cost of construction machinery between working surfaces: Penalty cost:

3. The engineering budget optimization method according to claim 2, characterized in that: Constraints are imposed on the mathematical optimization model, including: Budgeted machinery demand constraints: Mechanical daily workload constraints: Mechanical entry and exit frequency constraints:

4. The engineering budget optimization method according to claim 3, characterized in that: The constraints also include: Fixed entry and exit of construction machinery: Construction machinery cannot enter and exit the site frequently: Construction machinery can only be on the work surface if it is present, and can only be on one work surface on the same day: The premise that construction machine j can work on the kth operation on the tth day is that the construction machine happens to be on the working surface to which the kth operation belongs: The premise that construction machine j can replace budget machine i on day t is that construction machine j is performing the operation corresponding to budget machine i on day t: For the working surface K, the construction machine j can be transferred out from the working surface K only if no construction machine of the same type as the construction machine j is transferred in within the specified time: For operation k, if there is no construction machine of the same type as construction machine j transferred in the next day, construction machine j can be transferred out of operation k; in, is whether the jth construction machine is present on day t, Is the jth construction machine in the A working surface, Is the jth construction machine working on the kth operation on the tth day? The number of shifts required to replace the i-th budgeted machine demand by the j-th construction machine on the t-th day, is whether the jth construction machine enters the site on the tth day, is the work surface index, indicating the A working surface, Index of construction machinery types, indicating class of construction machinery, k is the job index, indicating the kth job, i is the budget machinery index, indicating the ith class of budget machinery, j is the construction machinery index, indicating the jth construction machinery, t is the construction period index, indicating the tth day of construction period, Is the kth job a member of Working surface, 1 means it belongs to, 0 means it does not belong to, Is the i-th budget machine belonging to the k-th job, 1 means it belongs, 0 means it does not belong, Is the jth construction machine a member of the Class: construction machinery, 1 means it belongs to, 0 means it does not belong to, is the unit-shift usage cost of the j-th construction machine, is the idle cost per unit of the j-th construction machine, is the single entry and exit cost of the j-th construction machine, is the depreciation cost of the j-th construction machine per day, The unit incentive cost of replacing the i-th budgeted machine with the j-th construction machine, For the jth construction machine from The work surface is dispatched to the The scheduling cost of each work surface, is the penalty cost of the jth construction machine not being on any working surface, P ij is the preference of the j-th construction machine to the i-th budget machine, V j is the remaining depreciation period of the jth construction machine. For construction machines with fixed depreciation costs, the remaining depreciation period is fixed at 1 day. ij is the efficiency conversion coefficient when the i-th budget machine is replaced by the j-th construction machine, is the corrected maximum work shift of the j-th construction machine on the t-th day, Budget the required number of shifts for the i-th type of machinery on the tth day.

5. The engineering budget optimization method according to claim 1, characterized in that: The heuristic algorithm includes the following steps: Generate an initial solution based on resource requirements and constraints; The initial solution is optimized by genetic algorithm, which includes population initialization, selection, crossover and mutation operations; The solution is evaluated based on the fitness function, which is a total cost formula; After multiple iterations, the optimal solution is output.

6. The engineering budget optimization method according to claim 1, characterized in that: The distributed computing includes the following steps: Decompose the optimization problem into multiple sub-problems and distribute them to different computing nodes; Each computing node calculates the local optimal solution of the subproblem in parallel; Aggregate and merge all local optimal solutions to generate an approximate global optimal solution; The approximate global optimal solution is further optimized and the final result is output.

7. The engineering budget optimization method according to claim 1, characterized in that: The resource curve is used to describe the daily usage requirements of various budgeted machines for each operation, and the resource calendar is used to define the maximum daily working hours of the construction machinery.

8. The engineering budget optimization method according to claim 1, characterized in that: The job priority is used to determine the scheduling order of construction machinery among different work surfaces, and the job with a higher priority is given priority in resource allocation.

9. The engineering budget optimization method according to claim 1, characterized in that: The scheduling time constraints between the work surfaces include: Construction machinery from The work surface is dispatched to the The time constraints required for each operation surface:

10. The engineering budget optimization method according to claim 1, characterized in that: The output scheduling plan includes the presence status, job allocation and scheduling path of each type of construction machinery on each day.

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