CN111861060B - Production optimization scheduling method oriented to personalized production mode of daily chemical industry - Google Patents
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Abstract
The invention relates to a production optimization scheduling method for a personalized production mode of daily chemical industry, which takes an enterprise order as a drive to carry out modeling and optimization scheduling. Firstly, a product model is carried out on all products of an enterprise, and the received order is split. And the optimal scheduling function comprising information such as product selling price, product raw material cost, production line running time cost, production line switching cost, delayed delivery compensation cost and the like is constructed by taking the maximum enterprise benefit as a target, and constraint conditions such as raw material inventory constraint, delivery period constraint, order constraint, processing sequence constraint and the like are considered. In the process of optimizing and solving by using an artificial bee colony algorithm, the invention provides a definition and construction method of coding, fitness function, initialization feasible solution and neighborhood operation according to the production characteristics of daily chemical industry. The invention effectively solves the problems of product modeling, order modeling, optimizing scheduling modeling and the like of enterprises driven by orders, and obtains the selective receiving result and order scheduling order of the orders, thereby maximizing the enterprise income.
Description
Technical Field
The invention relates to daily chemical industry oriented production, in particular to a daily chemical industry oriented production optimization scheduling method of a personalized production mode.
Background
With the rapid development of manufacturing technology and informatization technology and the improvement of living standard of people, the demands of people on flexible products are higher and higher, and the demands of flexibility and diversity of the products cannot be met by the traditional mass production mode. The intelligent factory for flexible production can realize large-scale customized production, has the characteristics of high efficiency and low cost for large-scale production, and can meet the flexible demands of consumers. Meanwhile, the characteristics of low cost and high efficiency of enterprises can be guaranteed, so that the enterprises can timely and flexibly change the operation modes in a new competitive environment, and the enterprises are continuously improved and innovated under the strong market competition.
Daily chemical enterprise products are seriously homogenized, and the demands of users are more diversified and personalized, the contradiction between the two demands that the new product of the enterprise is developed, produced and marketed faster and faster, and the large-scale pipeline production mode before the whole industry can not meet the market demands. More and more enterprises begin to explore intelligent factory modes for user-oriented personalized needs. The daily chemical products are complex in design and diversified in product form, and the traditional flexible production mode can meet market demands in part of industries, but basically is manufactured by a single process of single equipment, has limitations on product form, yield and the like, and lacks large-scale customization application of multiple equipment, multiple processes and multiple products; along with the increasing demand of flexible production, a brand new intelligent factory mode which can adapt to the flexible demands of different customers in multiple varieties, multiple processes and small batches is urgently needed to realize the flexible rapid manufacturing of the consumer product industry.
To realize intelligent production, enterprises must solve the technical problems of product modeling, quick reconstruction in the production process and the like in the prior large-scale production, thereby leading to the problem of quick connection between diversified and personalized orders and production flows. Daily chemical enterprises are a typical mixed manufacturing process combining flow manufacturing and discrete manufacturing, wherein the distribution management and the routine management technology of complex business in the flow manufacturing process are key to realizing rapid definition of products. The method is a typical discrete manufacturing process in the links of product subsequent filling, packaging, storage and the like. Compared with discrete manufacturing processes, the production process of flow manufacturing generally has various characteristics such as nonlinearity, randomness and the like, and is highly coupled in mathematics, so that the solution is very difficult. Meanwhile, due to continuity of production logistics, the buffer margin is small, and higher requirements are put on real-time performance, coordination, reliability and the like of resource scheduling.
Disclosure of Invention
The patent mainly aims at the production optimization scheduling problem of the personalized production mode in the daily chemical industry, models the product information, the order information and the optimization scheduling optimization model, adopts an artificial bee colony algorithm to carry out optimization solution, and provides definition and construction methods of coding, fitness function, initialization feasible solution and neighborhood operation according to the production characteristics of the daily chemical industry in the solving process.
The technical scheme adopted for realizing the invention is as follows: a production optimization scheduling method for personalized production modes of daily chemical industry comprises the following steps:
Step one: establishing a product model, wherein model information comprises product sales unit price, raw material consumption of each product and raw material unit price;
Step two: dividing the orders received by the enterprises into sub orders according to the product types, and storing the sub order information, wherein the sub order information comprises the total number of the divided orders, the product quantity of each order and the corresponding delivery period;
Step three: constructing an optimization function and constraint conditions with the maximum enterprise benefit for production targets, wherein the optimization function comprises product selling price, product raw material cost, production line running time cost, production line switching cost and delayed delivery compensation cost;
Step four: the method comprises the steps of defining codes of order optimizing solutions in a vector form, and defining a fitness function, wherein the fitness function of a honey source is an optimizing function considering the amount of honey source orders.
Step five: constructing an initial feasible solution of the problem according to the product category, delivery date and constraint conditions of the order;
step six: defining a neighborhood operation in the process of optimizing, dispatching and solving by adopting an artificial bee colony algorithm;
step seven: and carrying out optimal scheduling problem solving by adopting an artificial bee colony algorithm to obtain the selective receiving result of the order, the order scheduling sequence and the net income of the enterprise.
The optimization function is defined as:
Wherein, T r [k-1,k] is the line switching time in the production process, the line switching time is related to the product type of the sub-order produced at the k-1 position and the product type of the sub-order prepared to be produced at the next k position; t r [0,1] represents the preparation time of the sub-order at the first production position, and the switching time of the continuous production line of similar products is 0; x pk takes 1 when the p-th order is produced at the k-th position, or takes 0, and the polynomials on the right side of the optimization function are respectively the selling price of the product Product raw material cost/>Production line runtime cost/>Line switch cost s tTr [k-1,k], delay delivery compensation cost Production line operating cost per unit time for sub-order p, production time for sub-order p for b p, production time for the q-th position order for b [q],/>Representing the sum of all order production time of the first k positions, s t unit time production line switching cost and s d unit time order delay compensation cost; /(I)Representing the total amount of the m-th raw material in all orders; /(I) Representing the product quantity of sub-order p,/>Representing the sales unit price of the product of sub-order p, the various raw material amounts required for the p-th product are
The constraint conditions include:
Raw material inventory restrictions:
Delivery period limit:
and (3) processing sequence constraint:
Order constraint:
The lead time constraints apply only to orders with mandatory lead times, the process order constraints indicate that only one order can be processed at one location, and the order constraints indicate that only one order can be processed at one location.
The fitness function of the defining honey source is as follows
Where N [α] is defined as the length of the honey source α, p ε α.
The neighborhood operations include adjacent order exchange, order augmentation, order rejection.
The beneficial effects of the invention are as follows:
the invention provides a definition and construction method of coding, fitness function, initialization feasible solution and neighborhood operation according to the production characteristics of daily chemical industry. The invention effectively solves the problems of product modeling, order modeling, optimizing scheduling modeling and the like of enterprises by taking orders as drivers, and adopts the artificial bee colony algorithm to solve the optimizing scheduling problem, thereby obtaining the selective receiving result of the orders and the order scheduling order, and maximizing the enterprise income.
Drawings
FIG. 1 is a flow chart of a production optimization scheduling strategy in the invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are for the purpose of illustrating the invention but are not intended to limit the scope of the invention.
The embodiment comprises the following steps:
Step one: product information modeling
Assuming that the enterprise can produce N Y kinds of products together, the sales unit price of the jth kind of products isThe total raw materials are M kinds, and the unit price of the M kinds of raw materials is/>The amount of the various raw materials required for the j-th product is/>In order to uniformly express product information, even if one product does not use certain raw materials, the raw materials need to be written in a raw material consumption matrix, and the numerical value is set to zero.
Step two: order information modeling
Assuming that the enterprise receives a total of N orders, each order containing N i(1≤i≤N,1≤ni≤NY) products, each order is split into N i sub-orders for ease of production optimization. The total number of split orders isProduct quantity per split sub-order/>The order delivery period is represented by d p, and each sub-order delivery period is equal to the delivery period of the pre-split order.
Step three: optimizing scheduling problem modeling
A line switch time T r [k-1,k] during the production process is defined, which is related to both the product category of the sub-order to be produced at the k-1 position and the product category of the sub-order to be produced at the next k position. T r [0,1] represents the preparation time of the sub-order at the first production position, and the switching time of the continuous production line of the similar products is 0.
Production target with maximum enterprise benefit
S.t. stock limits:
Delivery period limit:
and (3) processing sequence constraint:
Order constraint:
the optimization target is the maximization of the net income of the enterprise, the optimization variables are the selective acceptance of orders and the production sequence, and the running time, the switching time and the compensation cost of delayed delivery of the production line are reduced through the optimization of order scheduling, so that the income of the enterprise is improved.
X pk takes 1 when the p-th order is produced at the k-th position, or takes 0, and the polynomials on the right side of the optimization objective function are respectively the selling price of the productProduct raw material cost/>Production line runtime cost/>Line switching costs/>Delay delivery Compensation fee/>Wherein/>Production line operating cost per unit time for sub order p, production time for order p for b p, order production time for position q of b [q],/>Representing the sum of all order production time of the first k positions, s t unit time production line switching cost and s d unit time order delay compensation cost. /(I)Representing the total amount of the m-th raw material in all orders; /(I)Represents the stock quantity of the mth raw material;
The calculation formula of (2) is as follows:
representing the product quantity of the sub-order p; /(I) Representing the product sales unit price of the sub-order p; the amount of various raw materials required for the p-th product is/>
The constraint conditions are stock inventory constraint, delivery period constraint, processing sequence constraint and order constraint, respectively. The lead time constraints apply only to orders with mandatory lead times, the process order constraints indicate that only one order can be processed at one location, and the order constraints indicate that only one order can be processed at one location.
Step four: defining coding and fitness functions
Assuming a problem instance with N a =5, when the optimization solution of the problem is expressed in the form of the encoding vector α= {5,2,3,1}, it is indicated that orders 1,2,3,5 are accepted and produced in the order of 5-2-3-1.
Defining fitness function of honey source individual as
Wherein N [α] is defined as the length of the honey source alpha; p.epsilon.alpha.
Step five: optimization scheduling problem solution initialization
Considering order scheduling characteristics of daily chemical industry, in order to improve the solving speed and the solving quality of the intelligent optimization algorithm, the following steps are executed:
Ordering the similar products according to the delivery period d p from short to long, and then randomly ordering orders of different types of products;
first, eliminating unsatisfied hard constraint conditions with forced delivery period Is to be placed;
such as stock inventory restrictions M is more than or equal to 1 and less than or equal to M, and orders can be randomly removed until all constraint conditions are met.
Thus, an initial feasible solution (intermediate solution of the coding vector alpha) of the optimization algorithm is formed, so that the switching time consumption of the production line can be reduced in the first place.
Step six: defining neighborhood operations
In the artificial bee colony algorithm, leading bees and following bees search for new honey sources in the neighborhood of the current honey source for optimization, and the defined field operation comprises three types of methods
A. Adjacent order exchange
For the current honey source (initial feasible solution), the orders of two adjacent processing positions are randomly selected for position exchange, and a new honey source is generated.
B. order augmentation
For the current honey source (initial feasible solution), an unaccepted order is randomly selected and randomly inserted into any processing position of the current honey source, and a new honey source is generated.
C. Order abandon
For the current honey source (initial feasible solution), an accepted order is randomly abandoned, and a new honey source is generated.
Step seven: optimization solution using artificial bee colony algorithm
① Defining various parameters of the artificial bee colony algorithm: the number of honey source individuals N s (feasible solution), the maximum times l m of honey source in continuous search in the leading peak stage and following bee stage are unchanged, the maximum times h m of unchanged optimal solution after multiple iteration loops, the maximum value of the initialized fitness function is μ=0, h=0,
② Initializing a honey source population (comprising a plurality of honey source individuals) according to the fifth step, and calculating the fitness of each honey source according to the fourth step;
③ Evolutionary process
A leading peak stage: for a honey source alpha i corresponding to a leading peak i (i=1, 2, … N s), randomly selecting one neighborhood operation in the step six to generate a new honey source, then judging whether the constraint condition of the step 3 is met at the same time, if so, calculating the fitness of the new honey source, if the fitness of the new honey source is higher than that of the original honey source, replacing the original honey source by the new honey source, recording the searching times l i of the leading peak that the honey source is not replaced, if so, setting l i to 0, otherwise, setting l i +1; obtaining a group of new leading peak honey sources;
b following the bee stage, selecting a honey source from the result of the step a by using a roulette algorithm aiming at the following bee j (j=1, 2, … N s), randomly selecting a neighborhood operation in the step six to generate a new honey source, then judging whether the constraint condition of the step 3 is met at the same time, if yes, calculating the adaptability of the new honey source, if the adaptability of the new honey source is higher than that of the original honey source, replacing the original honey source by the new honey source, recording the searching times l j of the following bee honey source which is not replaced, if replaced, setting l j to 0, otherwise l j +1; obtaining a group of new following honey sources; the honey source corresponding to the maximum value mu * of the fitness function is the optimal solution of the iteration;
c, bee investigation stage: i (i=1, 2, … N s), if l j＞lm, randomly generating a new honey source and replacing the original honey source alpha i according to step five.
D if μ * > μ, let μ≡μ *, h=0; otherwise h=h+1;
e if h reaches h m, the algorithm is terminated and the optimal solution is output, otherwise, the step a is returned to for iteration.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (2)
1. The production optimization scheduling method for the personalized production mode of the daily chemical industry is characterized by comprising the following steps of:
Step one: establishing a product model, wherein model information comprises product sales unit price, raw material consumption of each product and raw material unit price;
Step two: dividing the orders received by the enterprises into sub orders according to the product types, and storing the sub order information, wherein the sub order information comprises the total number of the divided orders, the product quantity of each order and the corresponding delivery period;
Step three: constructing an optimization function and constraint conditions with the maximum enterprise benefit for production targets, wherein the optimization function comprises product selling price, product raw material cost, production line running time cost, production line switching cost and delayed delivery compensation cost; the optimization function is defined as:
Wherein, T r [k-1,k] is the line switching time in the production process, the line switching time is related to the product type of the sub-order produced at the k-1 position and the product type of the sub-order prepared to be produced at the next k position; t r [0,1] represents the preparation time of the sub-order at the first production position, and the switching time of the continuous production line of similar products is 0; x pk takes 1 when the p-th order is produced at the k-th position, or takes 0, and the polynomials on the right side of the optimization function are respectively the selling price of the product Product raw material cost/>Production line runtime cost/>Line switching costs/>Delay delivery Compensation fee/> Production line operating cost per unit time for sub-order p, production time for sub-order p for b p, production time for the q-th position order for b [q],/>Representing the sum of all order production time of the first k positions, s t unit time production line switching cost and s d unit time order delay compensation cost; /(I)Representing the total amount of the m-th raw material in all orders; representing the product quantity of sub-order p,/> Representing the product sales unit price of the sub-order p, the various raw material amounts required for the p-th product are/>
The constraint conditions include:
Raw material inventory restrictions:
Delivery period limit:
and (3) processing sequence constraint:
Order constraint:
The delivery date constraint is only applicable to orders with forced delivery dates, the processing sequence constraint indicates that only one order can be processed at one location, and the order constraint indicates that only one order can be processed at one location;
step four: defining an order optimizing solution code in a vector form, and defining a fitness function, wherein the fitness function of the honey source is an optimizing function considering the honey source order quantity; the fitness function of the honey source is:
Wherein N [α] is defined as the length of the honey source alpha, p epsilon alpha;
step five: constructing an initial feasible solution of the problem according to the product category, delivery date and constraint conditions of the order;
step six: defining a neighborhood operation in the process of optimizing, dispatching and solving by adopting an artificial bee colony algorithm;
step seven: and carrying out optimal scheduling problem solving by adopting an artificial bee colony algorithm to obtain the selective receiving result of the order, the order scheduling sequence and the net income of the enterprise.
2. The production optimization scheduling method for personalized production patterns in daily chemical industry according to claim 1, wherein the neighborhood operation comprises adjacent order exchange, order addition and order discarding.
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