Model selection optimization method for equipment on fire fighting truck

Abstract

The invention discloses a model selection optimization method for equipment on a fire fighting truck, which comprises the following steps of: firstly, collecting equipment supply manufacturers on the fire fighting truck, equipment performance indexes and cost of each manufacturer, and respectively scoring the equipment supply by each manufacturer; then establishing a multi-objective optimization mathematical model for equipment type selection on the fire fighting truck, wherein the model comprises optimization variables, constraint conditions and an optimization objective function; and finally, solving the construction cost and the performance score of the whole fire fighting truck under different weights by using a 0-1 planning method through a random weight and combination fusion mode, further calculating the standard deviation of the construction cost and the performance score fluctuation of the whole fire fighting truck, and forming a single optimization objective function based on the fluctuation of a multi-objective function. And selecting manufacturers providing the equipment on the fire fighting truck, and completing type selection optimization of the equipment on the fire fighting truck. The requirements for improving the overall equipment performance of the fire fighting truck and reducing the cost can be better met, the rationality of planning and design of the fire fighting truck is improved, and sustainable development of the fire fighting truck is assisted.

Description

TECHNICAL FIELD

[0001] The application relates to a selection optimization method for equipment on a fire truck, in particular to a selection and planning process of the equipment on the fire truck. BACKGROUND

[0002] The performance of the equipment on the fire truck is the core of supporting the function of the fire truck. Therefore, scientific and reasonable selection and integration of the equipment on the fire truck become key problems in the application and development of the fire truck.

[0003] At present, the selection and integration of the equipment on the fire truck is generally based on the requirements of the equipment performance according to relevant standards. Relevant researches show that when the equipment on the fire truck is selected, more attention is paid to the improvement and update of the performance of the core equipment on the fire truck, and the optimization and analysis of the selection of the equipment on the whole vehicle from the system level are insufficient. Each manufacturer subjectively selects and integrates the equipment according to the standard requirements and the comparison of the performance and cost of the equipment in the industry, and the lack of objectivity leads to the lack of a systematic optimization process in the equipment integration process, and the lack of systematization, and the selection of the equipment on the whole vehicle is not optimized and analyzed from the system level. SUMMARY

[0004] In order to overcome the problems of the lack of objective analysis and the lack of systematization in the selection and integration of the equipment on the whole vehicle of the existing fire truck, and the lack of optimization and analysis of the selection of the equipment on the whole vehicle from the system level, the application provides a selection optimization method for the equipment on the fire truck. By establishing a scoring system for the performance of the same type of equipment of different manufacturers and a multi-objective optimization mathematical model for the selection of the equipment on the fire truck, a selection and integration method based on the volatility analysis of the multi-objective function is further provided, the selection and integration of the equipment on the vehicle are completed, and the effective evaluation of the performance of the selection of the equipment on the whole vehicle is realized.

[0005] The selection optimization method for the equipment on the fire truck comprises the following specific steps:

[0006] Step 1: An information collection unit for the equipment on the fire truck comprises obtaining the requirements of the equipment on the vehicle, the equipment supply manufacturers, the technical indexes and the cost of each manufacturer according to the function requirements of the fire truck.

[0007] Step 2: According to the technical indexes of each equipment supply manufacturer on the fire truck in step 1, each manufacturer's supplied equipment is scored to form the scores of different supply manufacturers of the same type of equipment.

[0008] Step 3: According to the technical indexes of each equipment supply manufacturer on the fire truck in step 1 and the scores of each equipment supply manufacturer on the fire truck in step 2, a multi-objective optimization mathematical model for the selection of the equipment on the fire truck is established, and the mathematical model comprises optimization variables, constraint conditions and an optimization objective function. The optimization variables are defined as α i,j, characterizing whether the jth manufacturer of the ith equipment is selected; α i,jA binary optimization variable that is either 0 or 1.

[0009] Step 4: Based on the multi-objective optimization mathematical model for fire truck equipment selection described in Step 3, the two optimization objectives (vehicle construction cost and performance score) are combined into a single optimization objective using random weighting. A 0-1 programming method is then used to solve for the fire truck construction cost and performance score under different weights. The standard deviation of the fluctuations in the fire truck construction cost and performance score is further calculated to form a single optimization objective function based on the volatility of the multi-objective functions. Finally, the 0-1 programming method is again used to solve for the optimization variables, completing the optimization of fire truck equipment selection.

[0010] The advantages of the present invention are:

[0011] 1. The method of the present invention analyzes and optimizes the problem of on-vehicle equipment selection from the perspective of the overall integration of the fire truck system. On the one hand, the equipment of each equipment supplier on the vehicle is scored to form an evaluation function for equipment selection, which is used for mathematical modeling of the integrated optimization of the whole vehicle equipment; on the other hand, a mathematical model of the fire truck equipment selection problem is established to reasonably describe the optimization problem of fire truck equipment selection. At the same time, optimization analysis is carried out from the two aspects of performance and cost, and an optimization method based on the volatility analysis of multi-objective functions is proposed to solve the problem of the diversity of optimal solutions faced by multi-objective optimization problems, and reasonably form the best solution for the selection of equipment on fire trucks. The present invention can better take into account the needs of improving the overall equipment performance of fire trucks and reducing costs, which is conducive to improving the rationality of fire truck planning and design, and promoting the sustainable development of fire trucks.

[0012] 2. The present method establishes a mathematical model for fire truck equipment selection based on the scoring of onboard equipment, scientifically depicting the planning problem of fire truck equipment selection. Furthermore, in solving this mathematical model, an optimization method based on multi-objective function volatility analysis is proposed to address the challenge of optimal solution diversity faced by multi-objective optimization problems, rationally achieving the optimal solution for fire truck equipment selection. As a result, the present invention can better balance the conflict between fire truck equipment performance and cost, which is conducive to improving the rationality of fire truck planning and design, and is of great significance to the selection and integration of fire truck equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is the overall flow chart of the method of the present invention;

[0014] Figure 2 This is a flow chart of the method for scoring equipment suppliers on fire trucks in the method of the present invention;

[0015] Figure 3 Flowchart of the optimization method based on multi-objective function volatility analysis in the method of the present invention; DETAILED DESCRIPTION

[0016] In order to enable people in this technical field to better understand the patent solution of the present invention, the technical solution in the embodiment of the present invention is clearly and completely described in combination with the drawings in the patent embodiment of the present invention.

[0017] The method for optimizing the selection of equipment on a fire truck of the present invention is as follows: Figure 1 As shown, the specific implementation steps are:

[0018] Step 1: The information collection unit for fire truck equipment includes obtaining the equipment requirements, equipment suppliers, and equipment technical indicators and costs of each manufacturer based on the functional requirements of the fire truck;

[0019] 1.1: Determine the equipment that needs to be installed on the fire truck based on the functional requirements of the fire truck;

[0020] 1.2: Based on the equipment on the fire truck determined in step 1.1, establish the equipment model and technical indicators provided by the supplier of each equipment; let the kth technical indicator of the jth supplier of the i-th equipment be x i,j,k ; i = 1, 2, ..., N; j = 1, 2, ..., M i ,k=1,2,…,K i ;M i represents the number of equipment suppliers of the i-th equipment, K i Indicates the number of technical indicators of the i-th equipment.

[0021] Step 2: Based on the technical indicators of each equipment supplier on the fire truck mentioned in step 1, score the equipment supplied by each manufacturer respectively to form the score of different suppliers of the same equipment; Figure 2 As shown, the specific method is:

[0022] 2.1: Number each piece of equipment on the fire truck and initialize the number i = 1;

[0023] 2.2: Carry out positive processing according to the technical indicators of the current equipment supplier;

[0024] Based on the meaning of the technical indicators of equipment No. i, the technical indicators of each manufacturer supplying equipment No. i are positively correlated. Specifically, if the kth technical indicator is a negative indicator (i.e., smaller k values ​​indicate better technical indicators), the value of the kth technical indicator remains unchanged. If the kth technical indicator is a positive indicator (i.e., larger k values ​​indicate better technical indicators), the kth technical indicator is calculated by subtracting the original value k from the maximum value of the kth technical indicators of all suppliers. For example, if an indicator x has three suppliers, and the technical indicators k of the three suppliers are 5, 10, and 19, respectively, then the maximum value, 19, is selected and subtracted from the technical indicators k of the three suppliers. The resulting positively correlated technical indicators of the three suppliers are 14, 9, and 0, respectively.

[0025] The formula of the above-mentioned forward processing method is expressed as:

[0026] Among the j-th supplier of equipment No. i, if the k-th technical indicator is a negative indicator, then , i=1,2,…,N, j=1,2,…M i ,k=1,2,…,K i ; If the kth technical indicator is expressed as the larger the better, then , i=1,2,…,N, j=1,2,…M i ,k=1,2,…,K i Among them, y i,j,k It represents the kth technical indicator of the positive transformation of the jth supplier of the i-th equipment.

[0027] 2.3: Standardize the technical indicators after the positive transformation described in step 2.2;

[0028] Among them, the result y of the positive transformation of the kth technical indicator of the jth manufacturer supplying the i-th equipment is i,j,k After standardization, it becomes:

[0029] , i=1,2,…,N, j=1,2,…M i ,k=1,2,…,K i .

[0030] 2.4: The standardized results obtained according to step 2.3 , calculate the maximum value vector and minimum value vector of the standardized technical indicators of the current equipment supplier.

[0031] Among them, the maximum vector Z of the standardized technical indicators of the manufacturer supplying equipment No. i is i + and the minimum vector Z i - for .

[0032] 2.5: Based on the results of steps 2.3 and 2.4, score each supplier of the current numbered equipment;

[0033] Among them, the score of the jth supplier of equipment No. i can be expressed as:

[0034]

[0035] 2.6: Determine whether the equipment number i is less than the equipment quantity N. If so, it means that there is equipment whose supplier has not been rated. In this case, the equipment number is increased by 1, that is, i = i + 1, and return to step 2.2; if not, it means that all equipment suppliers have been rated, and then go to step 3.

[0036] Step 3: Based on the technical indicators of the equipment suppliers of the fire trucks described in step 1 and the scores of the equipment suppliers of the fire trucks described in step 2, a multi-objective optimization mathematical model for the selection of equipment on the fire trucks is established. The mathematical model includes optimization variables, constraints and optimization objective functions.

[0037] 3.1: For the equipment on the fire truck, define the optimization variables that represent whether the supplier of the equipment on the fire truck is selected.

[0038] Let the optimization variable representing whether the j-th manufacturer supplying equipment No. i is selected be α i,j , α i,j is a binary optimization variable of 0 or 1. i,j =1, it means that the equipment No. i on the fire truck is the equipment provided by the jth manufacturer; if α i,j =0, it means that the equipment No. i on the fire truck does not use the equipment provided by the j-th manufacturer.

[0039] 3.2: Based on the operational requirements of the fire truck, define the constraints for the equipment selection on the fire truck;

[0040] Taking the vehicle load as an example, let the maximum load of the fire truck be H, and the weight of the i-th equipment provided by the j-th manufacturer on the vehicle be expressed as h i,j , then the constraints of the fire truck's operating load can be expressed as:

[0041]

[0042] Taking the volume of the entire vehicle as an example, V represents the maximum volume space of the fire truck. The volume of the i-th equipment provided by the j-th manufacturer on the vehicle is expressed as V i,j , then the constraints on the operating volume of the fire truck can be expressed as:

[0043]

[0044] Taking the vehicle operation response time as an example, T represents the minimum response time required for the operation of the fire truck. The startup time of the i-th equipment provided by the j-th manufacturer on the vehicle is expressed as T i,j , then the constraints of the fire truck's operational response can be expressed as:

[0045]

[0046] Taking the energy loss of the whole vehicle as an example, E represents the minimum energy loss required for the operation of the fire truck. The energy loss of the i-th equipment provided by the j-th manufacturer on the vehicle is expressed as E i,j , then the constraints of the fire truck's operational response can be expressed as:

[0047]

[0048] Taking the service life of the whole vehicle as an example, L represents the service life of the whole fire truck. The service life of the i-th equipment provided by the j-th manufacturer on the vehicle is expressed as L i,j , then the constraints on the service life of the fire truck can be expressed as:

[0049]

[0050] The other index constraints of the fire truck are established in the same way as above.

[0051] Step 3.3 From the perspective of fire truck construction cost and performance score, establish the optimization objective function for the equipment selection on the fire truck, and form a multi-objective optimization problem for the equipment selection on the fire truck.

[0052] Let the cost of the jth manufacturer supplying equipment No. i be c i,j , combined with step 2 to obtain the equipment performance score S of the jth manufacturer supplying the i-th equipment i,j , establish the optimization target J1 of the fire truck construction cost and the optimization target J2 of the fire truck performance score.

[0053]

[0054]

[0055] Step 4: If Figure 3As shown, according to the multi-objective optimization mathematical model for fire truck equipment selection described in step 3, the two optimization objective functions of fire truck construction cost and performance score are combined into a single optimization objective through random weighting. The fire truck construction cost and performance score are solved for different weights using the 0-1 programming method. The fluctuations of the fire truck construction cost and performance score under different weights are analyzed, and the standard deviation of the fluctuations of the fire truck construction cost and performance score are calculated to form a single optimization objective function based on the volatility of the multi-objective function. Finally, the 0-1 programming method is again used to solve and select the manufacturers that provide each fire truck equipment, completing the optimization of the fire truck equipment selection.

[0056] 4.1: Randomly generate several groups of weight coefficients with the fire truck construction cost as the optimization target and the fire truck performance score as the optimization target in the range of 0 to 1, and the sum of the two is 1. Let the number of random generation be T, and the weight coefficient with the fire truck construction cost as the optimization target in the tth random generation be expressed as ω 1,t , the weight coefficient of the fire truck performance score as the optimization target is expressed as ω 2,t = 1-ω 1,t , t=1, 2,…, T;

[0057] 4.2: Based on the weight coefficients generated in step 4.1, the two optimization objective functions are combined in a weighted summation manner to form a single optimization objective function. The 0-1 programming method is used to solve the fire truck construction cost and performance score under different weights. Specifically,

[0058] Under the tth random weight, the single optimization objective function formed by combining the two optimization objective functions in a weighted summation manner is , and use the 0-1 programming method to obtain the fire truck construction cost value J1(t) and the fire truck performance score value J2(t) under the t-th random weight.

[0059] 4.3: Based on the fire truck construction cost values ​​and fire truck performance score values ​​under different weight coefficients obtained in step 4.2, use the standard deviation formula in statistics to calculate the standard deviation of the fire truck construction cost value and the standard deviation of the fire truck performance score value.

[0060] 4.4 Based on the standard deviation of the fire truck construction cost and the standard deviation of the fire truck performance score obtained in step 4.3, the final single optimization objective function for the equipment selection on the fire truck is formed. The 0-1 programming method is used to solve the optimization variables described in step 3.1 to form the optimal solution for the equipment selection on the fire truck.

[0061] Let the standard deviation of the fire truck construction cost be σ1 and the standard deviation of the fire truck performance score be σ2, then the final single optimization objective function of the equipment selection on the fire truck is , use the 0-1 programming method to solve the 0-1 optimization variable α described in step 3.1 i,j , α i,j The optimization variable that represents whether the j-th manufacturer that supplies the i-th equipment is selected forms the best solution for equipment selection on the fire truck.

[0062] The above-mentioned method for optimizing the selection of equipment for fire trucks is implemented based on an equipment selection optimization system, which includes an information collection module, a scoring module, a multi-objective optimization modeling module, and a multi-objective optimization calculation module.

[0063] The information collection module is used to store the information of each piece of equipment on the fire truck (name, number, model and performance index), the supplier information of each piece of equipment (name, address) and the performance index and cost of each manufacturer's equipment.

[0064] The scoring module is based on the method in step 2, and after positive processing and standardization of the technical indicators of each equipment supplier, the maximum and minimum vectors of the technical indicators are calculated to further score each equipment supplier;

[0065] The multi-objective optimization modeling module is used to establish a multi-objective optimization mathematical model for equipment selection on fire trucks;

[0066] The multi-objective optimization calculation module is based on a multi-objective optimization mathematical model. Through random weights and combination fusion, the 0-1 programming method is used to solve the vehicle construction cost and performance score of the fire truck under different weights, and then calculates the standard deviation of the fluctuation of the vehicle construction cost and performance score of the fire truck, forming a single optimization objective function based on the volatility of the multi-objective function, and selecting the manufacturer that provides various equipment on the fire truck.

[0067] Based on the above system, the present invention should finally clarify that the embodiments described are only part of the embodiments of this application, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

Claims

1. A method for optimizing the selection of equipment for fire trucks, characterized by: The steps are: Step 1: The information collection unit for fire truck equipment includes obtaining the equipment requirements, equipment suppliers, and equipment technical indicators and costs of each manufacturer based on the functional requirements of the fire truck; Step 2: Based on the technical indicators of each equipment supplier on the fire truck described in step 1, score the equipment supplied by each manufacturer respectively to form a score for different suppliers of the same type of equipment; Step 3: Based on the technical indicators of the equipment suppliers of the fire trucks in step 1 and the scores of the equipment suppliers of the fire trucks in step 2, a multi-objective optimization mathematical model for the selection of equipment on the fire trucks is established. The mathematical model includes optimization variables, constraints and optimization objective functions; among them, the optimization variable is defined as α i,j, Indicates whether the j-th manufacturer supplying the i-th equipment is selected; α i,j A binary optimization variable that is 0 or 1; Step 4: Based on the multi-objective optimization mathematical model for the selection of equipment on fire trucks described in step 3, the two optimization objective functions of the fire truck whole vehicle construction cost and performance score are combined into a single optimization objective through random weighting, and the fire truck whole vehicle construction cost and performance score are solved under different weights through the 0-1 programming method; the standard deviation of the fluctuation of the fire truck whole vehicle construction cost and performance score is further calculated to form a single optimization objective function based on the volatility of the multi-objective function; finally, the optimization variables are solved again through the 0-1 programming method to complete the selection optimization of the equipment on the fire truck.

2. The method for optimizing the selection of equipment for a fire truck according to claim 1, characterized in that: The specific scoring method for step 2 is: a: Number each piece of equipment on the fire truck, initializing the number i = 1; b: Perform positive processing based on the technical parameters of the current equipment supplier; if the kth technical indicator is a negative indicator, the value of the kth technical indicator remains unchanged; if the kth technical indicator is a positive indicator, the kth technical indicator is expressed as the maximum value of the kth technical indicators of all suppliers minus the original value k; c. Standardize the positive technical indicators: Where, The result y of the positive transformation of the kth technical indicator of the jth manufacturer supplying the ith equipment is i,j,k The results after standardization; M i is the total number of equipment suppliers of the i-th equipment; d. The standardized result obtained according to step c , calculate the standardization of the current equipment supplier Vectors of maximum and minimum values ​​of technical indicators: Where Z i + With Z i - Supply the first i The maximum and minimum vectors of the standardized technical indicators of the manufacturer of the equipment; e. Based on the results of steps b and c, score each supplier of the current numbered equipment: Where, is the score of the j-th supplier for equipment No. i; f. Determine whether the equipment number i is less than the equipment quantity N. If so, increase the equipment number by 1 and return to step b; if not, proceed to step 3.

3. The method for optimizing the selection of equipment for a fire truck according to claim 1, characterized in that: In step 3, the optimization target J1 for the construction cost of the fire truck and the optimization target J2 for the performance score of the fire truck are established: Where, α i,j is the optimization variable; c i,j is the cost of the jth manufacturer supplying equipment No. i; S i,j To supply the i The performance score of the equipment of the jth manufacturer of the equipment; M i is the total number of equipment suppliers for the i-th equipment; N is the number of equipment.

4. The method for optimizing the selection of equipment for a fire truck according to claim 1, wherein: In step 4, a number of weight coefficients with the fire truck construction cost as the optimization objective and the fire truck performance score as the optimization objective are randomly generated in the range of 0 to 1, and the sum of the two is 1; based on the generated weight coefficients, the two optimization objective functions are combined in a weighted summation manner to form a single optimization objective function: in, and are two weight coefficients respectively; and They are the optimization objectives of the fire truck construction cost and performance score respectively.

5. The method for optimizing the selection of equipment for a fire truck according to claim 1, characterized in that: This is achieved through an equipment selection optimization system, including an information collection module, a scoring module, a multi-objective optimization modeling module, and a multi-objective optimization calculation module; The information collection module is used to store information about each piece of equipment on the fire truck, information about the supplier of each piece of equipment, and performance indicators and costs of each manufacturer's equipment; The scoring module performs positive and normalized processing on the technical indicators of each equipment supplier, calculates the maximum and minimum vectors of the technical indicators, and further scores each equipment supplier; The multi-objective optimization modeling module is used to establish a multi-objective optimization mathematical model for equipment selection on fire trucks, including optimization variables, constraints and optimization objective functions; The multi-objective optimization calculation module is based on the established multi-objective optimization mathematical model. Through random weights and combination fusion, the 0-1 programming method is used to solve the fire truck construction cost and performance score under different weights, and then calculates the standard deviation of the fluctuation of the fire truck construction cost and performance score, forming a single optimization objective function based on the volatility of the multi-objective function, and selecting the manufacturer that provides various equipment on the fire truck.

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