A double-layer production plan optimization control method based on an intelligent optimization algorithm

An intelligent optimization algorithm and production planning technology, applied in computing, manufacturing computing systems, instruments, etc., can solve the problems of no production plan, simple production plan, and low production efficiency in factories, and achieve good economic and social benefits.

Inactive Publication Date: 2019-05-03
ZHENGZHOU UNIVERSITY OF AERONAUTICS
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a two-layer production plan optimization control method based on an intelligent optimization algorithm, which effectively solves the problem of low production efficiency caused by the lack of production plan in the current factory.

Method used

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  • A double-layer production plan optimization control method based on an intelligent optimization algorithm
  • A double-layer production plan optimization control method based on an intelligent optimization algorithm
  • A double-layer production plan optimization control method based on an intelligent optimization algorithm

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Experimental program
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Effect test

Embodiment 1

[0098] Embodiment 1, the present invention is a two-tiered production planning optimization control method based on an intelligent optimization algorithm, characterized in that the two-tiered production planning optimization control method based on an intelligent optimization algorithm includes the following two layers:

[0099] The first layer: Order sorting, based on the principle of delivery time priority, to ensure that the total penalty of the order is the smallest, sort the orders, and generate the production plan in the factory. The second layer is workshop scheduling, and minimize the maximum completion time with the machine. The maximum utilization rate is the goal, and the main factors in actual production are considered, and a detailed workshop production plan is made for the order. However, only the first-level order sorting lists the current order order, but some orders are still postponed, and the order The production plan of the company is not detailed, and it is...

Embodiment 2

[0111] Embodiment 2, on the basis of Embodiment 1, the problem modeling of the order sorting of the first layer is as follows:

[0112] There are N orders to be processed in the factory, each order has only one product, the quantity is Mi, the individual processing time of each product decreases with the increase of the number of products in the order, and the processing time of the i-th order is T i . The delivery date of the i-th order is W i ;K i Indicates the start processing time of the i-th order; Ei represents the end processing time of the i-th order; the parallelism between orders is 30%. Taking delivery date as the priority principle, order delivery ahead of schedule or late delivery will result in fines. According to the actual production in the factory, it is more inclined to produce ahead of schedule, so the weight of early or late delivery is set as 1:2, with the minimum penalty C min is the objective function:

[0113] K i-1 +T i-1 *70%=K i (4-1)

[0...

Embodiment 3

[0127] Embodiment 3, on the basis of Embodiment 2, in order to further strengthen the global search capability of the particles, the pseudo code of the hybrid variable neighborhood particle swarm optimization algorithm is as follows:

[0128] Input: Accepted Orders

[0129] Output: order sorted

[0130] 1 Generate initial population

[0131] 2 for i=1: NIND*NINIA

[0132] 3 a: Randomly mix and generate initial particles according to the delivery date

[0133] 4 end

[0134] 5 for i=NIND*NINIA+1: NIND

[0135] 6 b: Randomly generate initial particles

[0136] 7 end

[0137] 8 Calculation of fitness

[0138] 9 Particle inertia W weight update

[0139] 10 Particle learning factor C1, C2 update

[0140] 11 Particle Neighborhood Search Range NS Update

[0141] 12 for i=1: maxgen

[0142] 13 particle speed update;

[0143] 14 Particle position update

[0144] 15 particles for adaptive variable neighborhood search

[0145] 16 Calculating fitness

[0146] 17 Particle in...

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Abstract

The invention relates to a double-layer production plan optimization control method based on an intelligent optimization algorithm, and effectively solves the problem of low production efficiency caused by no production plan and simple production plan in the existing factory. According to the technical scheme, the artificial leather comprises the following two layers: a first layer and a second layer, wherein the first layer is used for ordering orders, ensuring that the total penalty cost of the orders is minimum by taking delivery date priority as a principle, ordering the orders to generateproduction plans in a factory, and the second layer is used for scheduling the workshops by taking minimization of maximum completion time and maximum machine utilization rate as targets, and makingdetailed workshop production plans for the orders by taking main factors in actual production into consideration; the second layer: using a genetic algorithm to solve a workshop scheduling model withmultiple time constraints; the problems of long delivery time, long production period, low machine utilization rate, large product quantity and the like in multi-order production in a factory are solved, and good economic effects and social benefits can be achieved.

Description

technical field [0001] The invention relates to the technical field of workshop control planning, in particular to a two-layer production planning optimization control method based on an intelligent optimization algorithm. Background technique [0002] The current factory is in production. After the order is selected and confirmed by the sales department, it is sent to the production department. The planner manually divides it into sub-orders according to experience and compiles it into a production plan. The paper production notice is issued in order of date to the warehouse for batching. , and then distributed to the stamping, bending and other process groups in the workshop for production. At present, most of the workshops in the factory do not have workshop dispatchers. The execution of the production plan mainly depends on the communication between the factory manager and the team leaders of each process group, and the team leaders organize the workers in the group to c...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/04
CPCY02P90/30
Inventor 张国辉胡一凡齐浩淳崔溢范孙靖贺
Owner ZHENGZHOU UNIVERSITY OF AERONAUTICS
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