CN108647914B - Production scheduling method and device, computer equipment and storage medium - Google Patents

Production scheduling method and device, computer equipment and storage medium Download PDF

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CN108647914B
CN108647914B CN201810288929.7A CN201810288929A CN108647914B CN 108647914 B CN108647914 B CN 108647914B CN 201810288929 A CN201810288929 A CN 201810288929A CN 108647914 B CN108647914 B CN 108647914B
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徐骄
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Guangzhou Shiyuan Electronics Thecnology Co Ltd
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Abstract

The application relates to a production scheduling method, a production scheduling device, computer equipment and a storage medium. The method comprises the following steps: acquiring customer order information; analyzing the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, wherein the number of target parameters of the global solutions is at least 2; searching a global solution with the minimum difference between target parameters in the global solution, and taking the global solution with the minimum difference between the target parameters as an optimal solution; and performing production scheduling according to the optimal solution. According to the production scheduling method, the global solution of the production scheduling corresponding to the customer order is solved by the preset tabu search algorithm, the target parameter of the global solution is not less than 2, then the global solution with the minimum target parameter difference in the global solution, namely the optimal solution, is obtained through the balancer, the production scheduling is carried out according to the optimal solution, waste generated in the production scheduling process is reduced, and the effect of carrying out the optimal production scheduling under the condition of comprehensively considering various production scheduling targets can be achieved.

Description

Production scheduling method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of supply chain production technologies, and in particular, to a production scheduling method, an apparatus, a computer device, and a storage medium.
Background
MTO (Make To Order) refers To Order type production in the field of supply chain production, that is, an enterprise performs production arrangement according To orders instead of market demands, so that redundant stock is avoided, and a certain factory is arranged according To how many orders are. The production schedule refers to the allocation of orders, and an enterprise generally has a plurality of plants for manufacturing, and the cost price, address, capacity, and production line of different plants are different, so from the viewpoint of enterprise cost and lean production, it is more desirable to maximize the production cost and customer satisfaction of the enterprise on the premise of maintaining the cooperation among the plants. In the current production scheduling or system of supply chain enterprises, a scheduling scheme is first designed to make production tasks complete customer demands on time according to specific business processes (e.g., MTO, MTR) of the supply chain. For example, the current production state is uploaded by the M factories respectively, the order manager of the supply chain enterprise adds a new order to the production schedule of the factories according to the actual sales order without affecting the existing planning of the factories, and then sends a new task to each factory. This process targets one of the models for production scheduling, taking into account cost or capacity considerations, and the qualification of the plant for a particular model.
Most enterprises want to satisfy several requirements simultaneously: 1. the cost is minimum; 2. the capacity of each production line of the factory is called as completely as possible, so that the capacity loss is the lowest as possible; 3. the geographical location of the plant is as close as possible to the customer's ship-to site assigned to the order for the plant, making transportation costs as low as possible. However, the current scheduling system can only satisfy 1 or 1 and 2, i.e. there is a problem of wasting resources.
Disclosure of Invention
In view of the above, there is a need to provide a production scheduling method, apparatus, computer device and storage medium capable of meeting various production scheduling requirements at the same time.
A method of production scheduling, comprising the steps of:
acquiring customer order information;
analyzing the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, wherein the number of target parameters of the global solutions is at least 2;
searching a global solution with the minimum difference between target parameters in the global solution, and taking the global solution with the minimum difference between the target parameters as an optimal solution;
and performing production scheduling according to the optimal solution.
In one embodiment, the step of finding the global solution with the minimum difference between the target parameters in the global solutions and taking the global solution with the minimum difference between the target parameters as the optimal solution specifically includes:
calculating the absolute value of the difference between the target parameters in the global solution;
and searching a global solution corresponding to the minimum sum of the absolute values of the differences among the target parameters in all the global solutions, and taking the global solution with the minimum sum of the absolute values of the differences among the target parameters as an optimal solution.
In one embodiment, the step of importing the customer order information into a preset tabu search algorithm and obtaining a global solution of the tabu search algorithm to the order information further includes:
searching historical production data of each factory and position information of each factory;
determining the capacity loss of each finished order of each factory and the required production cost based on the historical production data, and determining the distance information between the client and the factory according to the position of the client in the order and the position information of each factory;
obtaining an objective function according to the capacity loss, the required production cost and the distance information between the client and the factory;
and constructing a tabu search algorithm according to a preset edge function and the target function.
In one embodiment, before obtaining the objective function according to the capacity loss, the required production cost, and the distance information between the client and the factory, the method further comprises:
and normalizing the capacity loss of each factory finished order, the production cost of each factory finished order and the distance between each factory and the required client position.
In one embodiment, the objective function specifically includes:
minΣiΣjΣk(Capability+Cost+Distance}＝minΣiΣjΣk{|PCj/Lj-PCijk·xijk|+Cij·xijk+Dij
·xijk}
where Capability represents capacity loss, Cost represents production Cost, Distance represents Distance of the desired customer location from the plant, PCjRepresenting j days of the factory average capacity; l isjRepresenting the number of production lines that plant j can provide; PC (personal computer)ijkIndicating the capacity supply required by the order i to be produced on the production line k of the factory j; cijRepresents the cost required for order i to be produced at plant j; dijIndicating customer distance worker to which order i belongsDistance of plant j. And x thereinijkIs a decision variable when xijk1 denotes that order i is assigned to production line k, x of plant jijk0 indicates that the order i is not assigned to the production line k of the plant j.
In one embodiment, the tabu search algorithm includes a preset edge function and an objective function, and the step of analyzing the customer order information by using the preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information specifically includes:
converting the customer order information into order data according to the preset edge function;
and obtaining a global solution according to the objective function and the order data, wherein the number of objective parameters of the global solution is at least 2.
In one embodiment, before analyzing the customer order information by using a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, the method further includes:
and constructing a preset tabu search algorithm through the R language.
A production scheduling apparatus, the apparatus comprising:
the order information acquisition module is used for acquiring the order information of the customer;
the global solution acquisition module is used for analyzing the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, and the number of target parameters of the global solutions is at least 2;
the optimal solution acquisition module is used for searching a global solution with the minimum difference between target parameters in the global solution and taking the global solution with the minimum difference between the target parameters as the optimal solution;
and the production scheduling module is used for carrying out production scheduling according to the optimal solution.
A computer device comprising a memory storing a computer program and a processor implementing the steps of any one of the above methods when the computer program is executed.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of any of the methods described above.
According to the production scheduling method, the global solution of the production scheduling corresponding to the customer order is solved by the preset tabu search algorithm, the target parameter of the global solution is not less than 2, then the global solution with the minimum target parameter difference in the global solution, namely the optimal solution, is obtained through the balancer, the production scheduling is carried out according to the optimal solution, waste generated in the production scheduling process is reduced, and the effect of carrying out the optimal production scheduling under the condition of comprehensively considering various production scheduling targets can be achieved.
Drawings
FIG. 1 is a flow diagram illustrating an embodiment of a production scheduling method;
FIG. 2 is a flow diagram illustrating an embodiment of a production scheduling method;
FIG. 3 is a block diagram of an embodiment of a production scheduling apparatus;
FIG. 4 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The production scheduling method provided by the application can be applied to the production environment of order type production, namely, production enterprises receive orders of clients, then carry out production scheduling according to the actual conditions of production co-production of all the production plants, and distribute the received orders to all the production plants for production. Assuming that a production enterprise carries out production scheduling by taking days as units, and placing yesterday orders to each factory; and each production line of the factory passes the inspection before production, and no fault occurs in the production process.
In one embodiment, as shown in fig. 2, a method for scheduling production is provided, which includes the steps of:
and S200, obtaining the customer order information.
The customer order information may specifically include: customer ID, customer address, model number of the product desired by the customer, number of production lots of the product, date of completion of the product, date of beginning of the production plan, etc. And the production enterprise receives the reservation of the customer for the product and generates corresponding customer order information.
S400, analyzing the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, wherein the number of target parameters of the global solutions is at least 2.
The tabu search algorithm is a meta-heuristic random search algorithm, and selects a series of specific search directions as heuristics from an initial feasible solution to realize the movement that the specific target function value changes most. The conventional tabu search algorithm is an algorithm for single-target optimization, so that the tabu search algorithm needs to be improved to realize multi-target optimization. The preset taboo search algorithm target parameter in the application is at least 2, but the number of solutions obtained by the preset taboo search algorithm is not limited to one due to the fact that a plurality of target parameters are set. The target parameter refers to a target parameter optimized by a tabu search algorithm, and in one embodiment, the target parameter may specifically include capacity loss, required production cost, and distance from the factory to the customer.
Analyzing the order information of the customer through a preset tabu search algorithm to obtain a global solution which accords with an optimization target and corresponds to the order of the customer. In one embodiment, the analytic global solution may be all solutions that meet the minimum sum of target parameters.
S600, searching a global solution with the minimum difference among the target parameters in the global solution, and taking the global solution with the minimum difference among the target parameters as an optimal solution.
In the process of multi-objective optimization, it is not realistic to minimize each objective at the same time, and in fact, the multi-objective optimization problem sought is to optimize a plurality of objectives as much as possible at the same time, and there are many schemes for achieving the optimization as much as possible, and in such many schemes, the solution with the smallest objective function value is required. In one embodiment, the value of the integrated objective function is 10, (a1, a2, a3) respectively represents capacity loss, cost, distance, and then one set of possible global solutions is (10, 0, 0) or (1, 4, 5) or (8, 1, 1) or (3, 4, 3), and the total objective function values of the four sets of global solutions all satisfy the minimum value, but the single solution is not the optimal solution in a single target, e.g., the cost and distance of the set of solutions is 0 (10, 0, 0), and obviously cannot exist in real business, and as another example, the cost and distance of (8, 1, 1) are very small values, and the capacity loss is large, and obviously there is a better solution combination than this. Of these 4 global solutions, (3, 4, 3) is the solution with the least direct difference in target parameters, so it can be taken as the global solution for this arrangement.
And the target parameters in different global solutions have differences, and the optimal solution is obtained by comparing the differences of the target parameters in all the global solutions after the global solutions are obtained.
And S800, performing production scheduling according to the optimal solution.
After the optimal solution is obtained, production scheduling is performed according to the optimal solution, and in one embodiment, a production scheduling plan that achieves the optimal solution in the tabu search algorithm may be searched for, and production scheduling is performed according to the plan.
According to the production scheduling method, the global solution of the production scheduling corresponding to the customer order is solved by the preset tabu search algorithm, the target parameter of the global solution is not less than 2, then the global solution with the minimum target parameter difference in the global solution, namely the optimal solution, is obtained through the balancer, the production scheduling is carried out according to the optimal solution, waste generated in the production scheduling process is reduced, and the effect of carrying out the optimal production scheduling under the condition of comprehensively considering various production scheduling targets can be achieved.
As shown in fig. 2, in one embodiment, the step S600 of finding the global solution with the minimum difference between the target parameters in the global solution, and the step of taking the global solution with the minimum difference between the target parameters as the optimal solution specifically includes:
and S620, calculating the absolute value of the difference between the target parameters in the global solution.
And S640, searching the global solution corresponding to the minimum sum of the absolute values of the difference values of the target parameters in all the global solutions, and taking the global solution with the minimum sum of the absolute values of the difference values of the target parameters as the optimal solution.
After the global solution is obtained, the global solution with the minimum sum of the absolute values of the differences between the target parameters can be used as the optimal solution, even if the absolute values of the differences between the parameters in the global solution are obtained. The global solution with the minimum absolute value of the difference between the parameters in the global solution is the global solution with the minimum difference between the target parameters. In one embodiment, the process of solving the optimal solution may be implemented by a balancer, and assuming that there are three objectives of the tabu search algorithm, the working process of the balancer specifically includes the following steps:
1) the three targets are represented by obj1, obj2, obj3 as d12, d13 and d23 as absolute values of differences among the three targets, respectively;
2) calculating any one of d12, d13 or d23, storing the value as a key value pair in a list, wherein the length L of the list is set by a preset parameter of a tabu search algorithm, and specifically can be set according to a multi-target distance parameter in the preset tabu search algorithm;
3) searching for a slightly smaller value in the direction of max (obj1, obj2, obj3) to obtain a new set of target values, returning to step 1), and returning the key with the smallest value in the list to the main target function as a solution of the tabu search iteration in the round when the L +1 th value is calculated or when the value calculated in step 2) has not changed for 3 times (3 is the target number). In one embodiment, each time a key value pair in L is calculated, the key value pair can be found according to the direction of the previous key value pair, and the minimum value can be found quickly.
In one embodiment, step S400: analyzing the customer order information through a preset tabu search algorithm, and before obtaining a global solution corresponding to the customer order information, the method further comprises the following steps:
s320, searching historical production data of each factory and position information of each factory.
S340, determining the capacity loss of the finished order of each factory and the required production cost based on the historical production data, and determining the distance information between the client and the factory according to the position of the client in the order and the position information of each factory.
And S360, obtaining an objective function according to the capacity loss, the required production cost and the distance information between the client and the factory.
And S380, constructing a tabu search algorithm according to the preset edge function and the target function.
The production records of relevant products are recorded in each production factory of the production enterprise, the capacity loss of the order completed by each factory and the required production cost can be determined by analyzing the historical records of the production factories, and the distance information of the client from the factories can be determined according to the position information of each factory. And then constructing an objective function according to the capacity loss, the required production cost and the distance between the client and the factory. And constructing a tabu search algorithm according to a preset edge function and an objective function. The preset edge function is mainly used for processing and converting the customer order information, and the target function calculates the target related to the optimization problem according to the new order data processed by the edge function, so that the method is convenient and quick.
In one embodiment, step S360 is preceded by the steps of:
and normalizing the capacity loss of each factory finished order, the production cost of each factory finished order and the distance between each factory and the required client position.
The normalization process is to convert the three dimensional quantities of dimensional capacity loss, cost and distance between the client and the factory into dimensionless quantities, and let the result values of the 3 targets be at the same level, for example, the capacity loss is 10000, the cost is 10, the distance between the client and the factory is 50, and to make them additive and not affect each other, they are normalized mathematically, i.e. converted into numbers between 0 and 1.
In one embodiment, the objective function specifically includes:
minΣiΣjΣk(Capability+Cost+Distance}
＝minΣiΣjΣk{|PCj/Lj-PCijk·xijk|+Cij·xijk+Dij
·xijk}
where Capability represents capacity loss, Cost represents production Cost, Distance represents Distance of the desired customer location from the plant, PCjRepresenting j days of the factory average capacity; l isjRepresenting the number of production lines that plant j can provide; PC (personal computer)ijkIndicating the capacity supply required by the order i to be produced on the production line k of the factory j; cijRepresents the cost required for order i to be produced at plant j; dijIndicating the distance of the customer to which order i belongs from factory j. And x thereinijkIs a decision variable when xijk1 denotes that order i is assigned to production line k, x of plant jijk0 indicates that the order i is not assigned to the production line k of the plant j.
The objective function represents the target form pursued by the production schedule by designing 3 variables of capacity, production cost, and customer location distance from the factory. And obtaining the global solution of the target function to the customer order by combining the target function with the customer order information processed by the preset edge function.
In one embodiment, the tabu search algorithm includes a preset edge function and an objective function, and the step of analyzing the customer order information by using the preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information specifically includes:
s410, converting the customer order information into order data according to a preset edge function;
and S430, obtaining a global solution according to the objective function and the order data.
The preset edge function is mainly used for converting customer order information into order data which can be processed by the objective function, and after the customer order information is collected and processed by the preset edge function, the order data is processed by the objective function to obtain the global solution of the objective function to the production schedule of the day
In one embodiment, the step of analyzing the customer order information by using a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information further includes:
and constructing a preset tabu search algorithm through the R language.
The production scheduling method can be realized by using the R language, the traditional tabu search algorithm can only realize the maximum target in the R language, and the minimum search can be completed while the multi-objective optimization is realized by the aid of the redesigned tabu search algorithm. The implementation process of the production scheduling method of the present application may further include a front-end interface for implementing production scheduling. The R language has a lightweight front end Shiny, an improved tabu search algorithm can be realized by using the R language, the front end of the application is realized by using the Shiny, the framework is light, and the R language is open source software and is low in cost.
In one embodiment, the production scheduling method of the present application includes the following steps:
and S200, obtaining the customer order information.
S320, searching historical production data of each factory and position information of each factory.
S340, determining the capacity loss of the finished order of each factory and the required production cost based on the historical production data, and determining the distance information between the client and the factory according to the position of the client in the order and the position information of each factory.
And normalizing the capacity loss of each factory finished order, the production cost of each factory finished order and the distance between each factory and the required client position.
And S360, obtaining an objective function according to the capacity loss, the required production cost and the distance information between the client and the factory.
And S380, constructing a tabu search algorithm according to the preset edge function and the target function.
S410, converting the customer order information into order data according to a preset edge function;
and S430, obtaining a global solution according to the objective function and the order data.
And S620, calculating the absolute value of the difference between the target parameters in the global solution.
And S640, searching the global solution corresponding to the minimum sum of the absolute values of the difference values of the target parameters in all the global solutions, and taking the global solution with the minimum sum of the absolute values of the difference values of the target parameters as the optimal solution.
And S800, performing production scheduling according to the optimal solution.
The preset edge function is mainly used for converting customer order information and converting the customer order information into order data which can be processed by the target function, and specifically comprises the following steps:
function 1: pm _ orders, which is used for processing the customer order information of the current day, inputting a new order of the current day and outputting aggregated order data, specifically calculating the number and the sum of each different product model.
Function 2: idxbin, a function of binary conversion into integer values, as an index for allocating factories to obtain corresponding factories, where binary conversion is required because the input of tabu search can only be binary representation, in one embodiment, a binary string can be used to represent factories, the number of bits of the binary string depends on the number of factories, for example, 3-bit binary can represent 0-7, which can be used to represent up to 8 factories;
function 3: dispatch, converting a string of binary numbers into a string of factory names, specifically, initializing a vector f to represent the number and the sum of orders of different product models obtained in a certain day in a summary manner, namely, the function of a pm _ orders function, converting a string of binary numbers (the number is a multiple of 3, and each 3 binary numbers can represent a factory index) into a string of factory names, calling an idxbin function to obtain an index value of a factory by traversing the binary string, thereby obtaining a string of factory index values, assigning the index value to the ith vector of f, and representing the factory index corresponding to the ith product model.
Function 4: caploss, calculating the total capacity loss of the appointed factory of each round of the scheduling;
function 5: cost, calculating the specified production cost of each round of the schedule;
function 6: distance, calculate the distance between the customer and the factory in each round of scheduling.
The objective function specifically includes:
minΣiΣjΣk(Capability+Cost+Distance}
＝minΣiΣjΣk{|PCj/Lj-PCijk·xijk|+Cij·xijk+Dij
·xijk}
where Capability represents capacity loss, Cost represents production Cost, Distance represents Distance of the desired customer location from the plant, PCjRepresenting j days of the factory average capacity; l isjRepresenting the number of production lines that plant j can provide; PC (personal computer)ijkIndicating the capacity supply required by the order i to be produced on the production line k of the factory j; cijRepresents the cost required for order i to be produced at plant j; dijIndicating the distance of the customer to which order i belongs from factory j. And x thereinijkIs a decision variable when xijk1 denotes that order i is assigned to production line k, x of plant jijk0 indicates that the order i is not assigned to the production line k of the plant j.
According to the production scheduling method, the global solution of the production scheduling corresponding to the customer order is solved by the preset tabu search algorithm, the target parameter of the global solution is not less than 2, then the global solution with the minimum target parameter difference in the global solution, namely the optimal solution, is obtained through the balancer, the production scheduling is carried out according to the optimal solution, and the effect of carrying out the optimal production scheduling under the condition of comprehensively considering various production scheduling targets can be achieved.
It should be understood that although the various steps in the flow diagrams of fig. 1-2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 3, there is provided a production scheduling apparatus, comprising:
an order information obtaining module 200, configured to obtain order information of a customer;
the global solution obtaining module 400 is configured to analyze the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, where the number of target parameters of the global solutions is at least 2;
the optimal solution obtaining module 600 is configured to find a global solution with the minimum difference between the target parameters in the global solution, and use the global solution with the minimum difference between the target parameters as the optimal solution.
And a production scheduling module 800 for performing production scheduling according to the optimal solution.
According to the production scheduling device, the global solution of the production scheduling corresponding to the customer order is solved by the preset tabu search algorithm, the target parameter of the global solution is not less than 2, then the global solution with the minimum target parameter difference in the global solution, namely the optimal solution, is obtained through the balancer, the production scheduling is carried out according to the optimal solution, waste generated in the production scheduling process is reduced, and the effect of carrying out the optimal production scheduling under the condition of comprehensively considering various production scheduling targets can be achieved.
In one embodiment, the optimal solution obtaining module 600 specifically includes:
the absolute value calculating unit is used for calculating the absolute value of the difference value between the target parameters in the global solution;
and the optimal solution searching unit is used for searching the global solution corresponding to the minimum sum of the absolute values of the differences among the target parameters in all the global solutions, and taking the global solution with the minimum sum of the absolute values of the differences among the target parameters as the optimal solution.
In one embodiment, the production scheduling apparatus further includes a tabu search algorithm building module, and the tabu search algorithm building module includes:
the historical data query unit is used for querying the historical production data of each factory and the position information of each factory;
the factory data confirmation unit is used for determining capacity loss of each finished order of each factory and required production cost according to historical production data, and determining distance information between a client and the factory according to the position of the client in the order and the position information of each factory;
the target function constructing unit is used for obtaining a target function according to the capacity loss, the required production cost and the distance information between the client and the factory;
and the tabu search algorithm construction unit is used for constructing a tabu search algorithm according to the preset edge function and the target function.
In one embodiment, the tabu search algorithm building module further includes:
and the normalization unit is used for normalizing the capacity loss of each factory finished order, the production cost of each factory finished order and the distance between each factory and the required client position.
In one embodiment, the global solution obtaining module 400 specifically includes:
the order data conversion unit is used for converting the customer order information into order data according to a preset edge function;
and the global solution acquisition unit is used for acquiring a global solution according to the objective function and the order data.
For the specific limitations of the production scheduling apparatus, reference may be made to the above limitations of the production scheduling method, which are not described herein again. The modules in the production scheduling apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a production scheduling method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring customer order information;
analyzing the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, wherein the number of target parameters of the global solutions is at least 2;
and searching the global solution with the minimum difference between the target parameters in the global solution, and taking the global solution with the minimum difference between the target parameters as the optimal solution.
And performing production scheduling according to the optimal solution.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating the absolute value of the difference between the target parameters in the global solution;
and searching the global solution corresponding to the minimum sum of the absolute values of the differences among the target parameters in all the global solutions, and taking the global solution with the minimum sum of the absolute values of the differences among the target parameters as the optimal solution.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inquiring historical production data of each factory and position information of each factory;
determining capacity loss and required production cost of each finished order of each factory according to historical production data, and determining distance information between a client and each factory according to the position of the client in the order and the position information of each factory;
obtaining an objective function according to the capacity loss, the required production cost and the distance information between the client and the factory;
and constructing a tabu search algorithm according to the preset edge function and the target function.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and normalizing the capacity loss of each factory finished order, the production cost of each factory finished order and the distance between each factory and the required client position.
In one embodiment, the computer program when executed by the processor further performs the steps of:
converting the customer order information into order data according to a preset edge function;
and obtaining a global solution according to the objective function and the order data.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring customer order information;
analyzing the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, wherein the number of target parameters of the global solutions is at least 2;
and searching the global solution with the minimum difference between the target parameters in the global solution, and taking the global solution with the minimum difference between the target parameters as the optimal solution.
And performing production scheduling according to the optimal solution.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating the absolute value of the difference between the target parameters in the global solution;
and searching the global solution corresponding to the minimum sum of the absolute values of the differences among the target parameters in all the global solutions, and taking the global solution with the minimum sum of the absolute values of the differences among the target parameters as the optimal solution.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inquiring historical production data of each factory and position information of each factory;
determining capacity loss and required production cost of each finished order of each factory according to historical production data, and determining distance information between a client and each factory according to the position of the client in the order and the position information of each factory;
obtaining an objective function according to the capacity loss, the required production cost and the distance information between the client and the factory;
and constructing a tabu search algorithm according to the preset edge function and the target function.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and normalizing the capacity loss of each factory finished order, the production cost of each factory finished order and the distance between each factory and the required client position.
In one embodiment, the computer program when executed by the processor further performs the steps of:
converting the customer order information into order data according to a preset edge function;
and obtaining a global solution according to the objective function and the order data.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for scheduling production, comprising the steps of:
acquiring customer order information;
analyzing the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, wherein the number of target parameters of the global solutions is at least 2, and the target parameters comprise capacity loss, required production cost and the distance from a customer to a factory;
searching a global solution with the minimum difference between target parameters in the global solution, and taking the global solution with the minimum difference between the target parameters as an optimal solution;
and performing production scheduling according to the optimal solution.
2. The method according to claim 1, wherein the step of finding the global solution with the minimum difference between the target parameters in the global solutions and using the global solution with the minimum difference between the target parameters as the optimal solution specifically comprises:
calculating the absolute value of the difference between the target parameters in the global solution;
and searching a global solution corresponding to the minimum sum of the absolute values of the differences among the target parameters in all the global solutions, and taking the global solution with the minimum sum of the absolute values of the differences among the target parameters as an optimal solution.
3. The method according to claim 1, wherein the step of importing the customer order information into a preset tabu search algorithm and obtaining a global solution of the tabu search algorithm to the order information further comprises:
searching historical production data of each factory and position information of each factory;
determining the capacity loss of each finished order of each factory and the required production cost based on the historical production data, and determining the distance information between the client and the factory according to the position of the client in the order and the position information of each factory;
obtaining an objective function according to the capacity loss, the required production cost and the distance information between the client and the factory;
and constructing a tabu search algorithm according to a preset edge function and the target function.
4. The method of claim 3, wherein before obtaining the objective function according to the capacity loss, the required production cost and the distance information of the customer from the factory, the method further comprises:
and normalizing the capacity loss of each factory finished order, the production cost of each factory finished order and the distance between each factory and the required client position.
5. The method of claim 3,
the objective function specifically includes:
min∑i∑j∑k{Capability+Cost+Distance}
＝min∑i∑j∑k{|PCj/Lj-PCijk·xijk|+Cij·xijk+Dij·xijk}
where Capability represents capacity loss, Cost represents production Cost, Distance represents Distance of the desired customer location from the plant, PCjRepresenting j days of the factory average capacity; l isjRepresenting the number of production lines that plant j can provide; PC (personal computer)ijkIndicating the capacity supply required by the order i to be produced on the production line k of the factory j; cijRepresents the cost required for order i to be produced at plant j; dijIndicating the distance of the customer to which order i belongs from factory j. And x thereinijkIs a decision variable when xijk1 denotes that order i is assigned to production line k, x of plant jijk0 indicates that the order i is not assigned to the production line k of the plant j.
6. The method according to claim 4, wherein the tabu search algorithm includes a preset edge function and an objective function, and the step of analyzing the customer order information by the preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information specifically includes:
converting the customer order information into order data according to the preset edge function;
and obtaining a global solution according to the objective function and the order data, wherein the number of objective parameters of the global solution is at least 2.
7. The method according to claim 1, wherein the step of inputting the customer order information into a preset tabu search algorithm and obtaining a global solution of the customer order information by the tabu search algorithm further comprises the steps of:
and constructing a preset tabu search algorithm through the R language.
8. A production scheduling apparatus, the apparatus comprising:
the order information acquisition module is used for acquiring the order information of the customer;
the global solution acquisition module is used for analyzing the client order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the client order information, the number of target parameters of the global solutions is at least 2, and the target parameters comprise capacity loss, required production cost and the distance from a client to a factory;
the optimal solution acquisition module is used for searching a global solution with the minimum difference between target parameters in the global solution and taking the global solution with the minimum difference between the target parameters as the optimal solution;
and the production scheduling module is used for carrying out production scheduling according to the optimal solution.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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