CN-121981652-A - High-end equipment manufacturing material distribution optimization method and device

CN121981652ACN 121981652 ACN121981652 ACN 121981652ACN-121981652-A

Abstract

The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a material distribution optimization method and device for manufacturing high-end equipment. The method comprises the steps of S1, constructing a material distribution optimization model from a supplier to a production line, which is manufactured by high-end equipment, wherein decision variables of the material distribution optimization model comprise distribution intervals and distribution amounts of various materials, targets of the material distribution optimization model comprise the lowest logistics total cost, the logistics total cost comprises external logistics cost representing cost from the supplier to a manufacturer warehouse and internal distribution cost representing cost from the manufacturer warehouse to the production line, S2, solving the material distribution optimization model to obtain optimal distribution intervals and distribution amounts of various materials, and S3, carrying out material distribution according to the optimal distribution intervals and distribution amounts of various materials, which are obtained in S2. The invention solves the technical problem of capital occupation and stock backlog caused by the planning misalignment of the distribution period in the traditional material distribution mode in the prior art.

Inventors

CHANG LIPING
ZHANG ZHIWEN
YANG XIAOYING
LI JUNXING
LI YAJIE
LV FENG
YANG JIANJUN
LIU CHUNYANG
ZHANG SHUTING

Assignees

河南科技大学
龙门实验室

Dates

Publication Date: 20260505
Application Date: 20260205

Claims (10)

1. A method for optimizing distribution of materials for manufacturing high-end equipment, the method comprising: S1, constructing a material distribution optimization model from a supplier of high-end equipment manufacturing to a production line; the decision variables of the material distribution optimization model comprise distribution intervals and distribution amounts of various materials; the objectives of the material delivery optimization model include a minimum of total logistics costs including an external logistics cost representing a cost of suppliers to manufacturer warehouses and an internal delivery cost representing a cost of manufacturer warehouses to production lines; s2, solving the material distribution optimization model to obtain optimal distribution intervals and distribution amounts of various materials; and S3, carrying out material distribution according to the optimal distribution interval and distribution amount of various materials obtained in the step S2.
2. The high-end equipment manufacturing material distribution optimization method according to claim 1, wherein the targets of the material distribution optimization model further include a minimum carbon emission amount including an external carbon emission amount generated by material distribution from a supplier to a manufacturer warehouse and an internal carbon emission amount representing material distribution from the manufacturer warehouse to each production line.
3. The high-end equipment manufacturing material delivery optimization method according to claim 1 or 2, characterized in that in S2 the material delivery optimization model is solved using a modified multi-objective dayf algorithm, the improvement comprising introducing a reverse learning of randomly selecting a candidate solution and computing a reverse solution of the candidate solution after mating variation generates the candidate solution, the reverse solution being used to update the Pareto solution set at each iteration.
4. A high-end equipment manufacturing material delivery optimization method as set forth in claim 3, wherein the probability of random selection decreases with increasing convergence times.
5. A high-end equipment manufacturing material distribution optimization method according to claim 3, wherein the probability of random selection is obtained according to the following formula: wherein t is the current iteration number; the probability at the t-th iteration; And The probability upper bound and the probability lower bound are respectively; The maximum iteration number and k is the attenuation coefficient larger than 1.
6. A high-end equipment manufacturing material distribution optimization method according to claim 3, wherein the inverse solution is calculated according to the following formula: Wherein, the And The j-th dimensional values of the candidate solution and the inverse solution corresponding to the candidate solution are respectively; And The populations at the t-th iteration are respectively Minimum and maximum dimensions; Is a random number.
7. The method of optimizing distribution of materials for manufacturing high-end equipment according to claim 3, wherein the improvement further comprises randomly selecting a set proportion of individual ones of the Pareto solutions when the set number of successive iterations of the Pareto solutions is not updated, and executing the Lewy flight disturbance.
8. The high-end equipment manufacturing material distribution optimization method according to claim 2, wherein the external logistics cost EC and the internal distribution cost PC are obtained according to the following formula: Wherein, the When the production tact of the manufacturer is r, the single dispensing amount of the supplier S m , Adaptively adjusting the dynamic beats r set by a manufacturer for different models of machine tools in a planning period; =0 or 1, when the material M j provided by the supplier i is selected by the manufacturer, =0, Whereas, =1; The supply ratio for the supplier S m ; The single machine assembly amount of the material M j required by the single machine type P i ; Yield for model P i ; the cost of a single delivery of material M j to supplier S m ; A unit inventory maintenance cost for the manufacturer warehouse w to store the material M j dispensed by the supplier S m ; Storing a supplier S m for the manufacturer warehouse w to distribute the material safety stock coefficient, wherein M is the number of distributed vehicles; When the production takt of a manufacturer is r, the AGV single delivery amount of the material M j from the warehouse to the assembly station L p ; For a manufacturer's tact of r, a single delivery of AGVs from the warehouse to the assembly station L p , Adaptively adjusting the dynamic beats r set by a manufacturer for different models of machine tools in a planning period; When material M j belongs to the line edge material of assembly station L p , =1, Whereas, =0; =0 Or 1, when the station is being equipped with a machine tool of model P i , =1, Whereas, = 0;E denotes the number of times the AGV dispenses the material required to produce machine tool model P i from warehouse to assembly line; Stock costs per line side for the manufacturer's assembly station L p ; To the assembly station for manufacturer warehouse The AGV single material distribution cost; When the takt of the manufacturer is r, the line edge safety stock quantity of the material M j of the assembly station L is calculated; The single delivery time is completed for the AGV; The method comprises the steps of starting the distribution time for the AGV, wherein n is the number of types of machine types to be produced, J is the number of types of materials, and h is the distribution sequence of the AGV; the number of batches of material for the AGV; the external carbon emission ECE and the internal carbon emission PCE are obtained according to the following formulas: Wherein, the When the supplier S m dispenses the materials using the fuel vehicle, Conversely, the method can be used for controlling the temperature of the liquid crystal display device, Mu is the carbon emission factor of the electric vehicle, eta is the carbon emission factor of the fuel vehicle; for distributing the vehicle travel speed; The time required for the supplier S m to complete a single material delivery; the time required for the supplier S m to complete the single material delivery, loading and unloading; Carbon emission factors for the breath of the dispensing personnel during operation; the time required for the AGV to finish single material distribution; the running speed of the AGV is the running speed of the AGV; is AGV carbon emission factor; sorting time for a single delivery to a manufacturer warehouse; the method comprises the steps of finishing single material distribution loading and unloading time at the line edge of a station, wherein M is the number of distribution vehicles, and l is the number of AGVs.
9. The high-end equipment manufacturing material delivery optimization method of claim 1 or 2, wherein the constraints of the material delivery optimization model include one of a supplier delivery capacity constraint, a supplier delivery time constraint, an AGV delivery capacity constraint, an AGV delivery time constraint, and a delivery on-time constraint, or a combination of two or more; the supplier delivery capacity constraint is that the single delivery capacity of the supplier does not exceed the maximum bearing capacity of delivery vehicles and the maximum storage capacity of materials corresponding to a warehouse; the supplier delivery time constraint is that the supplier delivery spacer is not less than the sum of the vehicle delivery time and the loading and unloading time; the AGV distribution capacity constraint is that the single distribution amount of the AGV does not exceed the maximum load capacity of the AGV; The AGV delivery time constraint is that the AGV delivery interval is not less than the sum of sorting time of a warehouse, AGV delivery completion time and phase change material loading and unloading time; the delivery on-time constraint is that the supplier must send material to the manufacturer warehouse in a corresponding prescribed time and the AGV must send material to the production line in a corresponding prescribed time.
10. A high-end equipment manufacturing material distribution optimization apparatus comprising a processor, wherein the processor is configured to execute a computer program to implement the steps of the high-end equipment manufacturing material distribution optimization method of any one of claims 1-9.

Description

High-end equipment manufacturing material distribution optimization method and device Technical Field The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a material distribution optimization method and device for manufacturing high-end equipment. Background The main purpose of the material delivery optimization method is to provide decision-makers with decisions on optimal delivery intervals and delivery numbers. In the field of high-end equipment manufacturing, along with the rising of service type manufacturing concepts, manufacturing enterprises pursue to meet diversified demands of clients and realize efficient manufacturing of equipment, and logistics is required to be capable of efficiently, accurately and orderly carrying out material distribution so as to meet dynamic demands of production. The high-end equipment manufacturing industry is used as a carbon emission intensive industry, and the carbon emission generated in the material distribution link is increasing at a speed of 5.8% per year. The conventional material distribution mode often causes phenomena of capital occupation, stock backlog, rapid increase of carbon emission and the like due to misalignment of distribution period planning (namely, deviation of distribution time and distribution quantity planning). Disclosure of Invention The invention aims to provide a material distribution optimization method and device for manufacturing high-end equipment, which are used for solving the technical problems of capital occupation and stock backlog caused by the fact that a distribution period planning is out of alignment in a traditional material distribution mode in the prior art. In order to solve the technical problems, the technical scheme of the high-end equipment manufacturing material distribution optimization method provided by the invention is that the high-end equipment manufacturing material distribution optimization method comprises the following steps: S1, constructing a material distribution optimization model from a supplier of high-end equipment manufacturing to a production line; the decision variables of the material distribution optimization model comprise distribution intervals and distribution amounts of various materials; the objectives of the material delivery optimization model include a minimum of total logistics costs including an external logistics cost representing a cost of suppliers to manufacturer warehouses and an internal delivery cost representing a cost of manufacturer warehouses to production lines; s2, solving the material distribution optimization model to obtain optimal distribution intervals and distribution amounts of various materials; and S3, carrying out material distribution according to the optimal distribution interval and distribution amount of various materials obtained in the step S2. The technical scheme has the advantages that the multi-objective optimization model is built by comprehensively considering two objectives of the material distribution cost of the inner and outer two stages for the first time from the actual production characteristics of high-end equipment manufacturing enterprises, and the optimization model is solved to obtain the distribution scheme, namely the optimal distribution interval and distribution quantity of various materials. The method can effectively optimize the material distribution interval and distribution quantity, realize balanced optimization among a plurality of targets, and the obtained result can reduce the total cost of logistics and has good universality and adaptability. The invention solves the technical problem of capital occupation and stock backlog caused by the planning misalignment of the distribution period in the traditional material distribution mode in the prior art. Further, the objectives of the material delivery optimization model include minimum carbon emissions, including external carbon emissions from the delivery of material from the supplier to the manufacturer warehouse and internal carbon emissions representing the delivery of material from the manufacturer warehouse to the various production lines. Further, the material delivery optimization model is solved in S2 by adopting an improved multi-objective dayf algorithm, wherein the improvement comprises the steps of introducing reverse learning, randomly selecting candidate solutions after mating variation generates the candidate solutions and calculating reverse solutions of the candidate solutions, and the reverse solutions are used for updating a Pareto solution set at each iteration. Further, the probability of random selection decreases with increasing convergence times. Further, the probability of random selection is derived from: wherein t is the current iteration number; the probability at the t-th iteration; And The probability upper bound and the probability lower bound are respectively; The maximum iteration number and k i