Resource-constrained parallel machine order production scheduling method based on progressive locking strategy

Abstract

The invention aims to provide a resource-constrained parallel machine order production scheduling method based on a progressive locking strategy. The method comprises the following steps: obtaining order data; calculating an order production scheduling scheme according to the order data by adopting a heuristic algorithm; and optimizing the order production scheduling scheme by adopting an order production scheduling intelligent algorithm based on a progressive locking strategy. According to the method, a scientific and effective order production scheduling scheme is efficiently generated in a resource-limited parallel airport scene, and the difficulty that the calculation amount is complex under complex relation mapping among orders, addresses and models is solved; and the problem of solution space splitting caused by various constraints needs to be considered. And meanwhile, production scheduling is carried out on the orders, so that the orders as many as possible meet offline direct sending conditions, offline direct sending is participated, and the inventory cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a resource-constrained parallel machine order scheduling method based on a progressive locking strategy. Background Art

[0002] In general, scale and cost advantages are important factors in the development of the home appliance industry. However, changes in the environment have put forward increasingly stringent requirements for the home appliance industry. Specifically, first of all, the market demand for small batches of multiple varieties has gradually replaced the original market demand for large batches of fewer varieties, making the traditional large-scale manufacturing model for inventory-oriented high production costs due to the diversification and rapid iteration of varieties. Secondly, in order to reduce the area of ​​finished product warehouses, enterprises adopt the offline direct delivery model that directly sends the same order to the required address after completion. However, in the face of fragmented order demand and highly dispersed address demand, the large-scale manufacturing production model will cause the orders at the same address to be completed in a dispersed manner, thereby increasing distribution costs. Therefore, it is necessary to improve the current large-scale manufacturing model under the condition of variety diversification and combined with the intensive demand for production and distribution. However, most manufacturers use third-party logistics for distribution, resulting in information asymmetry between the producer and the distributor, making it difficult to jointly consider the problem of intensive production and distribution, thereby increasing the revenue pressure of the enterprise.

[0003] While taking into account both variety diversity and intensive production, considering intensive distribution at multiple addresses and formulating production scheduling plans will undoubtedly greatly increase the difficulty of production scheduling for planners. The decision-making dimensions, factors that need to be considered, and the amount of data required in the production scheduling process are growing exponentially, which increases the scheduling time for planners and the risk of omissions. Summary of the invention

[0004] The purpose of the present invention is to provide a resource-constrained parallel machine order scheduling method based on a progressive locking strategy. The method efficiently generates a scientific and effective order scheduling solution for resource-constrained parallel machine scenarios, solves the difficulty of complex calculations under the complex relationship mapping between orders, addresses and models, and needs to consider the problem of solution space fragmentation caused by multiple constraints. At the same time, the orders are scheduled so that as many orders as possible meet the conditions for offline direct shipment, thereby participating in offline direct shipment and reducing inventory costs.

[0005] A resource-constrained parallel machine order scheduling method based on a progressive locking strategy, comprising:

[0006] Get order data;

[0007] Using a heuristic algorithm to calculate an order scheduling plan based on the order data;

[0008] An intelligent order scheduling algorithm based on a progressive locking strategy is used to optimize the order scheduling plan.

[0009] Preferably, the obtaining of order data comprises:

[0010] Obtain information on the number of machines in the factory, the models that the machines can produce, the machine start time information, the time required to produce a single model of product, the resources required to produce a single model of product, and customer order information.

[0011] Preferably, the using a heuristic algorithm to calculate the order scheduling plan according to the order data includes:

[0012] Get order information, model information, resource information, work calendar information and configuration information;

[0013] Convert order information, model information, resource information, work calendar information and configuration information into integer data by establishing key-value pair matching;

[0014] Summarize the order information by month and model to obtain the model set that needs to be produced;

[0015] According to the data preprocessing process, the redundant data in the model information and resource information of the current version is eliminated;

[0016] Initialize the list of idle machines;

[0017] Determine whether the current flight is the first flight;

[0018] According to the current shift information, choose to continuously undertake the lower die strategy or the upper die strategy based on domain knowledge and constraints for the machine;

[0019] Output model production schedule.

[0020] Preferably, the method of selecting a continuous lower die strategy or an upper die strategy based on domain knowledge and constraints for the machine according to the current shift information includes:

[0021] If the current shift is the first shift, select the continuous die transfer strategy for the machine, including:

[0022] Obtain the model production schedule of the previous shift, and pre-allocate the model production schedule of the previous shift as the mold arrangement plan for the current shift;

[0023] According to the production schedule of the previous shift, determine whether the molds that have been loaded have reached the maximum number of continuous production days;

[0024] If the maximum number of continuous production days is reached, check the corresponding relationship between the remaining model demand and the current capacity. The current available capacity = the number of shifts that can be molded * the maximum number of molds of the current model * the UPH of the current model. If the number of machines with the current model cannot meet the minimum continuous production days limit when continuing to produce and the remaining demand can be completed within the monthly range, perform mold removal on one of the machines and check the current step again.

[0025] If the maximum continuous production days have not been reached, determine whether the mold that has been molded has reached the maximum continuous production days. If the maximum continuous production days have been reached, check whether the orders within the monthly range of the corresponding model of the mold have all been completed. If they have all been completed, perform the mold removal process. If they have not been completed, do not perform the mold removal process.

[0026] Check the door resources of the mold arrangement plan for the current shift. If the door resources are insufficient to support the production of the corresponding model of the mold, the mold will be processed. If the door resources are sufficient to support the production of the model, the mold will not be processed.

[0027] Perform capacity verification on the remaining model information to verify whether the demand can be met within the available capacity range of the model. If it cannot be met, the model with constraints is selected first, and then the mold with a larger number of upper molds is selected for lower mold;

[0028] Save a collection of completed die models.

[0029] Preferably, the method of selecting a continuous lower die strategy or an upper die strategy based on domain knowledge and constraints for the machine according to the current shift information includes:

[0030] If the current shift is not the first shift, select a die-up strategy based on domain knowledge and constraints, including:

[0031] Summarize the list of all idle machines in the current shift, and take the order of the order volume of the same model from high to low as the model priority;

[0032] Prioritize the models that are subject to model mutual exclusion and machine production constraints, and then select the models with higher model priority according to the model priority. After sorting the model set to be produced according to the model priority, the list of candidate models for the lower upper mold in the current shift is obtained;

[0033] The model set of the lower mold in the process of pre-allocation of the production plan for the previous shift is removed from the list of candidate models for the lower mold in the current shift;

[0034] Summarize the list of molds that have been loaded in the non-idle machines in the current shift, and remove the mutually exclusive models under the production models of the non-idle machines from the list of candidate models for loading in the current shift;

[0035] In the current shift, the list of candidate mold models is sorted from high to low. According to the remaining demand quantity of the order, the corresponding mold model is selected for mold processing. The maximum number of products that can be arranged = min (box mold production capacity, door resource supply capacity) / single mold daily production capacity;

[0036] Determine whether the number of mold types exceeds the maximum number of mold types. If so, reselect the model for mold processing and remove the model corresponding to the current mold from the list of mold candidate models for the current shift.

[0037] If the maximum number of mold types is not exceeded, determine whether the molds corresponding to the models are mutually exclusive. If the mold corresponding to the model to be newly molded is mutually exclusive with other molds that have been molded in the current shift, remove the model corresponding to the mold from the list of candidate models for molds to be molded in the current shift P, and reselect the model for mold processing;

[0038] If the molds corresponding to the models are not mutually exclusive, determine whether the current mold-mounting machine and mold have mold-mounting area restrictions. If so, reselect the model from the mold-mounting alternative model list for the current shift to perform mold-mounting processing;

[0039] If there is no restriction on the mold loading area, determine whether there is an idle machine in the current shift. If there is an idle machine in the current shift and the list of mold loading candidate models in the current shift has not been completely traversed, continue to select the model for mold loading;

[0040] If there is no idle machine in the current shift or the list of upper die alternative models in the current shift has been completely traversed, it will jump to the next shift and re-perform the previous shift to pre-allocate the production schedule and the upper die strategy based on domain knowledge and constraints.

[0041] Preferably, the optimization of the order scheduling scheme by using an intelligent order scheduling algorithm based on a progressive locking strategy includes:

[0042] Obtain order information and production schedule for the previous model;

[0043] Initialize the number of iterations, population size, and mutation probability parameters;

[0044] Randomly generate initial solutions for order information based on population size;

[0045] Calculate the fitness value of each chromosome in the population and sort them;

[0046] Iterate according to the progressive locking intelligent algorithm iteration strategy and update the chromosome information in each population;

[0047] Mutate according to the probability of mutation;

[0048] The chromosome with the best fitness in the population is taken and decoded according to the model production schedule to obtain the optimized order production schedule.

[0049] Preferably, the step of taking the chromosome with the best fitness in the population and decoding it according to the model production scheduling plan to obtain an optimized order production scheduling plan includes:

[0050] Obtain the production order information of the order according to the code, traverse the orders one by one in order, and determine the orders that need to be scheduled at the moment;

[0051] According to the demand month of the current order that needs to be scheduled, find the model information of the order that needs to be scheduled and sort it in chronological order;

[0052] Establish an allocation relationship between the model and quantity required by the current order to be scheduled and the corresponding production capacity, and deduct the relevant production capacity information and time information until all orders are scheduled;

[0053] According to the time range required for direct delivery, the order scheduling results are divided into time ranges;

[0054] Select the orders that have been completed within the time range, and summarize the orders by address to obtain the address order summary;

[0055] Select an address in the address order set, sort the orders to which the address belongs in order of demand quantity from most to least, select these orders one by one to aggregate, and after the departure party is aggregated, record the current order set and the order numbers of the participating parties, and then select other orders that are not participating in the aggregation to aggregate;

[0056] If the remaining orders cannot be grouped together with the shipping party, the remaining orders will be grouped together into the largest party according to the number of parties participating in the grouping, from the smallest to the largest, without exceeding the largest party.

[0057] Preferably, the iterating according to the progressive locking intelligent algorithm iteration strategy and updating the chromosome information in each population includes:

[0058] Get the number of iterations, population size n, mutation probability, and asymptotic locking iteration number;

[0059] Summarize the models and production quantities in the model production plan for each shift;

[0060] According to the number of orders, the population is initialized by randomly generating an order priority sequence, and the population is sorted according to fitness to obtain the population;

[0061] Two different chromosomes S1 and S2 are selected by roulette wheel, and gene positions C1 and C2 are randomly selected in chromosomes S1 and S2 for crossover exchange;

[0062] Check whether there is duplication or missing of coding sequence numbers in S1 and S2 after crossing;

[0063] If there are repeated or missing numbers in the coding sequence, a repeated number is randomly selected to replace the missing number;

[0064] Decode the newly generated chromosomes and calculate the fitness value, and determine whether the chromosomes with better fitness values ​​in the population have the same gene sequence;

[0065] If the same gene sequence exists, the address concentration time range to which the gene sequence belongs is divided and the number of occurrences of its iterations is recorded. When the number of occurrences in the continuous iteration process exceeds the asymptotic locking iteration number, the current part of the order is fixed, the relevant production capacity in the model scheduling plan is deducted, and the iteration plan of the current part in the chromosome is deleted;

[0066] Select the best n individuals from all parent individuals and offspring individuals as the population for the next generation;

[0067] Check whether the current situation meets the mutation conditions. If so, randomly generate a new chromosome and insert it into the population, and remove the chromosome with a low fitness evaluation index.

[0068] Repeat the iteration until all chromosome gene positions are deleted or the upper limit of the iteration number is reached.

[0069] A resource-constrained parallel machine order scheduling system based on a progressive locking strategy, comprising:

[0070] Data acquisition module, used to obtain order data;

[0071] A data processing module, used to calculate an order scheduling plan based on the order data using a heuristic algorithm;

[0072] The optimization module is used to optimize the order scheduling plan by adopting an order scheduling intelligent algorithm based on a progressive locking strategy.

[0073] An electronic device comprises: a processor and a memory, wherein the memory is used to store computer program codes, the computer program codes include computer instructions, and when the processor executes the computer instructions, the electronic device executes a resource-constrained parallel machine order scheduling method based on a progressive locking strategy.

[0074] The beneficial effects of the present invention are as follows: 1. The core of the heuristic algorithm based on domain rules of the present invention is to configure relevant business parameters on the basis of combining the actual production environment conditions, and to mold the molds required for the models according to the work calendar shift by shift through the business parameters, so as to meet the model continuity under the condition of ensuring variety diversity. 2. The core of the intelligent order scheduling algorithm based on the progressive locking strategy of the present invention is to actually schedule the orders within the model capacity allocated in the first part on the basis of the model scheduling plan of the first part, and to improve the efficiency of the direct delivery of the orders as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0076] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0077] Figure 1 A flow chart of a method for scheduling resource-constrained parallel machine orders based on a progressive locking strategy according to the present invention;

[0078] Figure 2 It is a flow chart of the continuous succession lower mold strategy and the upper mold strategy based on domain knowledge and constraints of the present invention;

[0079] Figure 3 The flowchart of the model address intensive optimization algorithm based on the intelligent algorithm of the present invention;

[0080] Figure 4 It is a schematic diagram of the chromosome decoding process of the present invention;

[0081] Figure 5 A schematic diagram of the process of searching for an order set of the present invention;

[0082] Figure 6 It is a schematic diagram of the progressive locking process of the present invention. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0085] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0086] While taking into account both variety diversity and intensive production, considering intensive distribution at multiple addresses and formulating production scheduling plans will undoubtedly greatly increase the difficulty of production scheduling for planners. The decision-making dimensions, factors that need to be considered, and the amount of data required in the production scheduling process are growing exponentially, which increases the scheduling time for planners and the risk of omissions.

[0087] The core of the heuristic algorithm based on domain rules of the present invention is to configure relevant business parameters based on the actual production environment conditions, and to mold the molds required for the models according to the work calendar shift by shift through the business parameters, so as to meet the model continuity under the condition of ensuring variety diversity. The core of the intelligent order scheduling algorithm based on the progressive locking strategy of the present invention is to actually schedule the orders within the model capacity allocated in the first part based on the model scheduling plan of the first part, and to improve the efficiency of the direct delivery of the orders as much as possible.

[0088] Example 1

[0089] A resource-constrained parallel machine order scheduling method based on progressive locking strategy, reference Figure 1 ,include:

[0090] S100, obtaining order data;

[0091] S200, uses a heuristic algorithm to calculate the order scheduling plan based on order data;

[0092] S300 uses an intelligent order scheduling algorithm based on a progressive locking strategy to optimize the order scheduling plan.

[0093] Preferably, S100, obtaining order data includes:

[0094] Obtain information on the number of machines in the factory, the models that the machines can produce, the machine start time information, the time required to produce a single model of product, the resources required to produce a single model of product, and customer order information.

[0095] The present invention performs data preprocessing and data verification on the relevant basic data, and is characterized in that the number of machines in the factory, the model information that the machine can produce, the time information of the machine opening, the time information required for model production, the resource information required for model production, and the customer order information are obtained. Data preprocessing is to encode the data and convert the relevant data fields to facilitate subsequent program processing. Data verification is to eliminate relevant illegal data, such as order information without process route model requirements, so as to ensure the stability of program operation.

[0096] Since the actual production data is configured by planners, there are situations where planners mismatch or miss match in the actual situation. For example, in order to meet market demand, the company launches a new model for sale, and the customer places an order for the new model, but the corresponding supply route cannot be found in the system. In order to ensure the efficiency and stability of the algorithm and shield the interference of abnormal data, the production data will go through the data preprocessing process before entering the algorithm.

[0097] Through the user's quick code configuration, we can obtain information such as the maximum and minimum continuous production days, the machine's production matrix, etc., and establish a key-value pair relationship for the data to achieve standardized processing of converting character information into integer information.

[0098] The products in the order information table are aggregated according to the dimension of month to obtain the model set N that needs to be produced.

[0099] According to the model set N that needs to be produced, search whether there is a corresponding relationship in the information table with the model field. If there is a single model, the data does not exist in the model-related information table, but there is data in the model set N produced, then delete the corresponding data to improve the efficiency of the algorithm data retrieval; if there is a single model, the data does not exist in the model-related information table, but there is data in the model set N produced, then transfer the order corresponding to the model to the new order set D, and feedback the relevant information to the user after completing the entire production scheduling process.

[0100] Preferably, reference Figure 2 , S200, uses a heuristic algorithm to calculate the order scheduling plan based on order data, including:

[0101] In this module, the experience of production schedulers is summarized into a heuristic algorithm based on domain knowledge, and the model scheduling plan is obtained according to the algorithm flow of the heuristic algorithm. This can meet the variety of products and ensure production continuity, avoiding infeasible solutions caused by constraints. It is mainly divided into shift scheduling decision, continuous lower mold strategy, and upper mold strategy based on domain knowledge and constraints.

[0102] S210, obtaining order information, model information, resource information, work calendar information and configuration information;

[0103] S220, converting the order information, model information, resource information, work calendar information and configuration information into integer data by establishing a key-value pair matching method;

[0104] S230, summarizing the order information by month and model to obtain a set of models that need to be produced;

[0105] S240, according to the data preprocessing process, remove redundant data in the model information and resource information of the current version;

[0106] S250, initializing a list of idle machines;

[0107] S260, determining whether the current flight is the first flight;

[0108] S270, selecting a continuous lower die strategy or an upper die strategy based on domain knowledge and constraints for the machine according to the current shift information;

[0109] S280, output model production scheduling plan.

[0110] The present invention calls a heuristic algorithm based on domain knowledge to obtain a model scheduling plan, which is characterized by summarizing the model information required in customer orders, combining the model summary information with the shifts to be opened and the resource information required for production, and combining domain knowledge to schedule the required models shift by shift, thereby obtaining a model scheduling plan.

[0111] Pseudo code of production intensive model scheduling algorithm based on domain knowledge:

[0112] Input: order information Order_Info, model information Fac_Info, resource information Res_Info, work calendar information Data_Info, configuration information Config_Info;

[0113] Convert the order information Order_Info, model information Fac_Info, resource information Res_Info, work calendar information Data_Info, and configuration information Config_Info into integer data by establishing key-value pair matching;

[0114] Summarize the order information Order_Info by month and model to obtain the model set N that needs to be produced;

[0115] for i=1:length(N)

[0116] According to the data preprocessing process, irrelevant data in the model information Fac_Info and resource information Res_Info in the current version are removed;

[0117] end for

[0118] for i=1:length(Data_Info)

[0119] Initialize the idle machine list idle_List;

[0120] If the current flight is the first flight;

[0121] Inherit the mold information of the previous shift to the current shift, and according to the continuity of the mold placement strategy, place the mold on the machine, and update the idle machine list idle_List at the same time;

[0122] end if

[0123] According to the die-casting strategy based on domain knowledge and constraints, the idle machine list idle_List is die-casted;

[0124] end for;

[0125] Output model scheduling scheme Mould_scheme1.

[0126] Preferably, S270, selecting a continuous lower die strategy or an upper die strategy based on domain knowledge and constraints for the machine according to the current shift information includes:

[0127] Shift scheduling decision. This decision mainly determines whether the current shift can inherit the shift information. If it can inherit the shift information, it is the first shift. If it cannot inherit the shift information, it is not the first shift. If the current shift is the first shift, the upper mold strategy based on domain knowledge and constraints is executed. If the current shift is not the first shift, the previous shift scheduling information is obtained, and the current shift is pre-allocated according to the previous shift.

[0128] If the current shift is the first shift, select the continuous die transfer strategy for the machine, including:

[0129] Obtain the model production schedule of the previous shift, and pre-allocate the model production schedule of the previous shift as the mold arrangement plan for the current shift;

[0130] According to the production schedule of the previous shift, determine whether the molds that have been loaded have reached the maximum number of continuous production days;

[0131] If the maximum number of continuous production days is reached, check the corresponding relationship between the remaining model demand and the current capacity. The current available capacity = the number of shifts that can be molded * the maximum number of molds of the current model * the UPH of the current model. If the number of machines with the current model cannot meet the minimum continuous production days limit when continuing to produce and the remaining demand can be completed within the monthly range, perform mold removal on one of the machines and check the current step again.

[0132] If the maximum continuous production days have not been reached, determine whether the mold that has been molded has reached the maximum continuous production days. If the maximum continuous production days have been reached, check whether the orders within the monthly range of the corresponding model of the mold have all been completed. If they have all been completed, perform the mold removal process. If they have not been completed, do not perform the mold removal process.

[0133] Check the door resources of the mold arrangement plan for the current shift. If the door resources are insufficient to support the production of the corresponding model of the mold, the mold will be processed. If the door resources are sufficient to support the production of the model, the mold will not be processed.

[0134] Perform capacity verification on the remaining model information to verify whether the demand can be met within the available capacity range of the model. If it cannot be met, the model with constraints is selected first, and then the mold with a larger number of upper molds is selected for lower mold;

[0135] Save a collection of completed die models.

[0136] Preferably, S270, selecting a continuous lower die strategy or an upper die strategy based on domain knowledge and constraints for the machine according to the current shift information includes:

[0137] If the current shift is not the first shift, select a die-up strategy based on domain knowledge and constraints, including:

[0138] Summarize the list of all idle machines in the current shift, and take the order of the order volume of the same model from high to low as the model priority;

[0139] Prioritize the models that are subject to model mutual exclusion and machine production constraints, and then select the models with higher model priority according to the model priority. After sorting the model set to be produced according to the model priority, the list of candidate models for the lower upper mold in the current shift is obtained;

[0140] The model set of the lower mold in the process of pre-allocation of the production plan for the previous shift is removed from the list of candidate models for the lower mold in the current shift;

[0141] Summarize the list of molds that have been loaded in the non-idle machines in the current shift, and remove the mutually exclusive models under the production models of the non-idle machines from the list of candidate models for loading in the current shift;

[0142] In the current shift, the list of candidate mold models is sorted from high to low. According to the remaining demand quantity of the order, the corresponding mold model is selected for mold processing. The maximum number of products that can be arranged = min (box mold production capacity, door resource supply capacity) / single mold daily production capacity;

[0143] Determine whether the number of mold types exceeds the maximum number of mold types. If so, reselect the model for mold processing and remove the model corresponding to the current mold from the list of mold candidate models for the current shift.

[0144] If the maximum number of mold types is not exceeded, determine whether the molds corresponding to the models are mutually exclusive. If the mold corresponding to the model to be newly molded is mutually exclusive with other molds that have been molded in the current shift, remove the model corresponding to the mold from the list of candidate models for molds to be molded in the current shift P, and reselect the model for mold processing;

[0145] If the molds corresponding to the models are not mutually exclusive, determine whether the current mold-mounting machine and mold have mold-mounting area restrictions. If so, reselect the model from the mold-mounting alternative model list for the current shift to perform mold-mounting processing;

[0146] If there is no restriction on the mold loading area, determine whether there is an idle machine in the current shift. If there is an idle machine in the current shift and the list of mold loading candidate models in the current shift has not been completely traversed, continue to select the model for mold loading;

[0147] If there is no idle machine in the current shift or the list of upper die alternative models in the current shift has been completely traversed, it will jump to the next shift and re-perform the previous shift to pre-allocate the production schedule and the upper die strategy based on domain knowledge and constraints.

[0148] Preferably, reference Figure 3 , S300, uses an intelligent order scheduling algorithm based on a progressive locking strategy to optimize the order scheduling solution, including:

[0149] The order scheduling algorithm is based on the model scheduling algorithm, and it allocates several orders in a certain order. During the allocation process, orders can be split and produced at will. In addition, during the production process, the orders need to be completed as quickly as possible to ensure that more order sets can be found to participate in the offline direct delivery. Therefore, the orders will be evenly distributed to the machines that can produce the model at the current time for production. Based on this, the order scheduling process adopts the method of summarizing the models, that is, the shift and time of each machine's opening, and the number of units that can be produced are simplified to the number of units that can be produced for each model under the current shift and time. In this regard, the algorithm determines the time range occupied by order scheduling by determining the order of order production, and then determines whether the order participates in offline direct delivery by finding orders that are completed within the address intensive time range.

[0150] S310, obtaining order information and the production schedule of the previous model;

[0151] S320, initialization iteration number, population size, and mutation probability parameters;

[0152] S330, randomly generating an initial solution for the order information according to the population size;

[0153] S340, calculating the fitness value of each chromosome in the population and sorting them;

[0154] The order scheduling problem is encoded using a sequence encoding method. The encoding needs to ensure the priority relationship between orders. During the generation and iteration process, it needs to ensure that each order is a feasible solution. For the encoding of order scheduling, it consists of multiple different numbers. The encoding example is shown in Table 1.

[0155] Table 1 Chromosome coding table

[0156]

[0157] S350, iterating according to the progressive locking intelligent algorithm iteration strategy and updating the chromosome information in each population;

[0158] S360, mutation according to mutation probability;

[0159] S370, taking the chromosome with the best fitness in the population and decoding it according to the model production scheduling plan to obtain an optimized order production scheduling plan.

[0160] The present invention calls an intelligent algorithm for order scheduling based on a progressive locking strategy. The intelligent algorithm for order scheduling based on a progressive locking strategy is characterized in that a scheduling plan for model scheduling is obtained, and the order scheduling sequence is formulated through a genetic algorithm, thereby formulating a scheduling plan for the order, and calculating the number of orders that can meet the conditions for offline direct shipment based on the order scheduling plan, and then iterating the order sequence according to the genetic algorithm. The condition for offline direct shipment of an order is a set of orders completed at the same address within a certain time range. The total number of square meters of the set satisfies the range from the departure square to the maximum square meter of the truck, that is, offline direct shipment. For example, assuming that the departure square meter is 200 square meters, the maximum square meter is 300 square meters, the demand quantity of the model corresponding to order one is 50 units, the unit volume is 1.1, and the shipping address is Guangzhou. The demand quantity of the model corresponding to order two is 200 units, the unit volume is 1.2, and the shipping address is Guangzhou. Then order one and order two can be put together as order set one to participate in offline direct shipment.

[0161] Intelligent order scheduling algorithm for intensive distribution:

[0162] Input: Order information Order_Info, previous model production scheduling scheme Mould_scheme2;

[0163] Initialize parameters such as the number of iterations count, population size c, and mutation probability a;

[0164] Randomly generate an initial solution for the order information Order_Info according to the population size c

[0165] Calculate the fitness value of each chromosome in the population and sort them

[0166] for i=1:length(count);

[0167] Iterate according to the progressive locking intelligent algorithm iteration strategy and update the chromosome information chromosome in each population;

[0168] Mutate according to the mutation probability a;

[0169] end for

[0170] Take the chromosome with the best fitness in the population and decode it according to the model scheduling scheme Mould_scheme2 to obtain the order scheduling scheme Order_scheme;

[0171] Output model scheduling scheme Order_scheme.

[0172] Preferably, reference Figure 4 and Figure 5, S370, take the chromosome with the best fitness in the population and decode it according to the model scheduling plan, and get the optimized order scheduling plan including:

[0173] Obtain the production order information of the order according to the code, traverse the orders one by one in order, and determine the orders that need to be scheduled at the moment;

[0174] According to the demand month of the current order that needs to be scheduled, find the model information of the order that needs to be scheduled and sort it in chronological order;

[0175] Establish an allocation relationship between the model and quantity required by the current order to be scheduled and the corresponding production capacity, and deduct the relevant production capacity information and time information until all orders are scheduled;

[0176] According to the time range required for direct delivery, the order scheduling results are divided into time ranges;

[0177] Select the orders that have been completed within the time range, and summarize the orders by address to obtain the address order summary;

[0178] Select an address in the address order set, sort the orders to which the address belongs in order of demand quantity from most to least, select these orders one by one to aggregate, and after the departure party is aggregated, record the current order set and the order numbers of the participating parties, and then select other orders that are not participating in the aggregation to aggregate;

[0179] If the remaining orders cannot be grouped together with the shipping party, the remaining orders will be grouped together into the largest party according to the number of parties participating in the grouping, from the smallest to the largest, without exceeding the largest party.

[0180] During the chromosome decoding process, orders are scheduled one by one in the coding order, and the specific orders involved in the offline direct delivery process are decided. Therefore, the quality of decoding largely determines the quality of the solution. Given that business rules

[43] are essentially the precise distillation of a certain business characteristic, they play a vital role as a guideline for decision-making, standardizing business activities, and controlling practical operations. In real scenarios, planners will complete orders in the shortest possible time so that more orders can meet the offline direct delivery conditions. Therefore, the chromosome decoding process integrates the algorithm efficiency optimization strategy based on the actual order scheduling of planners, so that more orders can meet the offline direct delivery decision while taking into account the feasibility and efficiency of domain knowledge, thereby improving the quality of the algorithm solution.

[0181] Preferably, reference Figure 6 , S350, iterates according to the progressive locking intelligent algorithm iteration strategy, and updates the chromosome information in each population including:

[0182] Get the number of iterations, population size n, mutation probability, and asymptotic locking iteration number;

[0183] Summarize the models and production quantities in the model production plan for each shift;

[0184] According to the number of orders, the population is initialized by randomly generating an order priority sequence, and the population is sorted according to fitness to obtain the population;

[0185] Two different chromosomes S1 and S2 are selected by roulette wheel, and gene positions C1 and C2 are randomly selected in chromosomes S1 and S2 for crossover exchange;

[0186] Check whether there is duplication or missing of coding sequence numbers in S1 and S2 after crossing;

[0187] If there are repeated or missing numbers in the coding sequence, a repeated number is randomly selected to replace the missing number;

[0188] Decode the newly generated chromosomes and calculate the fitness value, and determine whether the chromosomes with better fitness values ​​in the population have the same gene sequence;

[0189] If the same gene sequence exists, the address concentration time range to which the gene sequence belongs is divided and the number of occurrences of its iterations is recorded. When the number of occurrences in the continuous iteration process exceeds the asymptotic locking iteration number, the current part of the order is fixed, the relevant production capacity in the model scheduling plan is deducted, and the iteration plan of the current part in the chromosome is deleted;

[0190] Select the best n individuals from all parent individuals and offspring individuals as the population for the next generation;

[0191] Check whether the current situation meets the mutation conditions. If so, randomly generate a new chromosome and insert it into the population, and remove the chromosome with a low fitness evaluation index.

[0192] Repeat the iteration until all chromosome gene positions are deleted or the upper limit of the iteration number is reached.

[0193] Since the present invention adopts the iterative strategy of genetic algorithm for iteration. The solution steps of genetic algorithm are mainly divided into selection, crossover, mutation and population update. The most commonly used selection methods include: tournament selection method, roulette selection method and the like. The present invention uses roulette selection algorithm to screen chromosomes probabilistically, assigns different selection probabilities according to individual fitness, simulates the natural selection process, retains and further evolves high-quality chromosomes, and improves the efficiency of global optimization. Due to the high complexity of decoding actions, when using traditional intelligent algorithms, it is easy to cause problems such as long algorithm running time and poor solution quality in the scenario of large-scale orders. Therefore, the present invention proposes an improved genetic algorithm under the progressive locking strategy. The core idea of ​​the progressive locking strategy is to judge whether the solution effect of offline direct delivery can be relatively good within the address concentration time range in the process of intelligent algorithm optimization. If the solution effect of offline direct delivery is relatively good and the gene corresponding to the address concentration time range is not damaged during multiple iterations, then the part of the order set is fixed in the corresponding machine.

[0194] Example 2

[0195] A resource-constrained parallel machine order scheduling system based on a progressive locking strategy, comprising:

[0196] Data acquisition module, used to obtain order data;

[0197] A data processing module is used to calculate the order scheduling plan based on the order data using a heuristic algorithm;

[0198] The optimization module is used to optimize the order scheduling plan by using an intelligent order scheduling algorithm based on a progressive locking strategy.

[0199] Example 3

[0200] An electronic device comprises: a processor and a memory, wherein the memory is used to store computer program codes, and the computer program codes include computer instructions. When the processor executes the computer instructions, the electronic device executes a resource-constrained parallel machine order scheduling method based on a progressive locking strategy.

[0201] The core of the heuristic algorithm based on domain rules of the present invention is to configure relevant business parameters based on the actual production environment conditions, and to mold the molds required for the models according to the work calendar shift by shift through the business parameters, so as to meet the model continuity under the condition of ensuring variety diversity. The core of the intelligent order scheduling algorithm based on the progressive locking strategy of the present invention is to actually schedule the orders within the model capacity allocated in the first part based on the model scheduling plan of the first part, and to improve the efficiency of the direct delivery of the orders as much as possible.

[0202] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A resource-constrained parallel machine order scheduling method based on progressive locking strategy, It is characterized in that include: Get order data; Using a heuristic algorithm to calculate an order scheduling plan based on the order data; An intelligent order scheduling algorithm based on a progressive locking strategy is used to optimize the order scheduling plan.

2. A resource-constrained parallel machine order scheduling method based on a progressive locking strategy according to claim 1, It is characterized in that The obtaining of order data comprises: Obtain information on the number of machines in the factory, the models that the machines can produce, the machine start time information, the time required to produce a single model of product, the resources required to produce a single model of product, and customer order information.

3. The method for scheduling resource-constrained parallel machine orders based on a progressive locking strategy according to claim 1, It is characterized in that The method of using a heuristic algorithm to calculate an order scheduling plan according to the order data includes: Get order information, model information, resource information, work calendar information and configuration information; Convert order information, model information, resource information, work calendar information and configuration information into integer data by establishing key-value pair matching; Summarize the order information by month and model to obtain the model set that needs to be produced; According to the data preprocessing process, the redundant data in the model information and resource information of the current version is eliminated; Initialize the list of idle machines; Determine whether the current flight is the first flight; According to the current shift information, choose to continuously take over the lower die strategy or the upper die strategy based on domain knowledge and constraints for the machine; Output model production schedule.

4. The method for scheduling resource-constrained parallel machine orders based on a progressive locking strategy according to claim 3, It is characterized in that The method of selecting a continuous lower die strategy for the machine according to the current shift information or an upper die strategy based on domain knowledge and constraints includes: If the current shift is the first shift, select the continuous die transfer strategy for the machine, including: Obtain the model production schedule of the previous shift, and pre-allocate the model production schedule of the previous shift as the mold arrangement plan for the current shift; According to the production schedule of the previous shift, determine whether the molds that have been loaded have reached the maximum number of continuous production days; If the maximum number of continuous production days is reached, check the corresponding relationship between the remaining model demand and the current capacity. The current available capacity = the number of shifts that can be molded * the maximum number of molds of the current model * the UPH of the current model. If the number of machines with the current model cannot meet the minimum continuous production days limit when continuing to produce and the remaining demand can be completed within the monthly range, perform mold removal on one of the machines and check the current step again. If the maximum continuous production days have not been reached, determine whether the mold that has been molded has reached the maximum continuous production days. If the maximum continuous production days have been reached, check whether the orders within the monthly range of the corresponding model of the mold have all been completed. If they have all been completed, perform the mold removal process. If they have not been completed, do not perform the mold removal process. Check the door resources of the mold arrangement plan for the current shift. If the door resources are insufficient to support the production of the corresponding model of the mold, the mold will be processed. If the door resources are sufficient to support the production of the model, the mold will not be processed. Perform capacity verification on the remaining model information to verify whether the demand can be met within the available capacity range of the model. If it cannot be met, the model with constraints is selected first, and then the mold with a larger number of upper molds is selected for lower mold; Save a collection of completed die models.

5. The method for scheduling resource-constrained parallel machine orders based on a progressive locking strategy according to claim 3, It is characterized in that The method of selecting a continuous lower die strategy for the machine according to the current shift information or an upper die strategy based on domain knowledge and constraints includes: If the current shift is not the first shift, select a die-up strategy based on domain knowledge and constraints, including: Summarize the list of all idle machines in the current shift, and take the order of the order volume of the same model from high to low as the model priority; Prioritize the models that are subject to model mutual exclusion and machine production constraints, and then select the models with higher model priority according to the model priority. After sorting the model set to be produced according to the model priority, the list of candidate models for the lower upper mold in the current shift is obtained; The model set of the lower mold in the process of pre-allocation of the production plan for the previous shift is removed from the list of candidate models for the lower mold in the current shift; Summarize the list of molds that have been loaded in the non-idle machines in the current shift, and remove the mutually exclusive models under the production models of the non-idle machines from the list of candidate models for loading in the current shift; In the current shift, the list of candidate mold models is sorted from high to low. According to the remaining demand quantity of the order, the corresponding mold model is selected for mold processing. The maximum number of products that can be arranged = min (box mold production capacity, door resource supply capacity) / single mold daily production capacity; Determine whether the number of mold types exceeds the maximum number of mold types. If so, reselect the model for mold processing and remove the model corresponding to the current mold from the list of mold candidate models for the current shift. If the maximum number of mold types is not exceeded, determine whether the molds corresponding to the models are mutually exclusive. If the mold corresponding to the model to be newly molded is mutually exclusive with other molds that have been molded in the current shift, remove the model corresponding to the mold from the list of candidate models for molds to be molded in the current shift P, and reselect the model for mold processing; If the molds corresponding to the models are not mutually exclusive, determine whether the current mold-mounting machine and mold have mold-mounting area restrictions. If so, reselect the model from the mold-mounting alternative model list for the current shift to perform mold-mounting processing; If there is no restriction on the mold loading area, determine whether there is an idle machine in the current shift. If there is an idle machine in the current shift and the list of mold loading candidate models in the current shift has not been completely traversed, continue to select the model for mold loading; If there is no idle machine in the current shift or the list of upper die alternative models in the current shift has been completely traversed, it will jump to the next shift and re-perform the previous shift to pre-allocate the production schedule and the upper die strategy based on domain knowledge and constraints.

6. The method for scheduling resource-constrained parallel machine orders based on a progressive locking strategy according to claim 1, It is characterized in that The optimization of the order scheduling scheme by using an intelligent order scheduling algorithm based on a progressive locking strategy includes: Obtain order information and production schedule for the previous model; Initialize the number of iterations, population size, and mutation probability parameters; Randomly generate initial solutions for order information based on population size; Calculate the fitness value of each chromosome in the population and sort them; Iterate according to the progressive locking intelligent algorithm iteration strategy and update the chromosome information in each population; Mutate according to the probability of mutation; The chromosome with the best fitness in the population is taken and decoded according to the model production schedule to obtain the optimized order production schedule.

7. A resource-constrained parallel machine order scheduling method based on a progressive locking strategy according to claim 6, It is characterized in that The step of taking the chromosome with the best fitness in the population and decoding it according to the model production scheduling plan to obtain the optimized order production scheduling plan includes: Obtain the production order information of the order according to the code, traverse the orders one by one in order, and determine the orders that need to be scheduled at the moment; According to the demand month of the current order that needs to be scheduled, find the model information of the order that needs to be scheduled and sort it in chronological order; Establish an allocation relationship between the model and quantity required by the current order to be scheduled and the corresponding production capacity, and deduct the relevant production capacity information and time information until all orders are scheduled; According to the time range required for direct delivery, the order scheduling results are divided into time ranges; Select the orders that have been completed within the time range, and summarize the orders by address to obtain the address order summary; Select an address in the address order set, sort the orders to which the address belongs in order of demand quantity from most to least, select these orders one by one to aggregate, and after the departure party is aggregated, record the current order set and the order numbers of the participating orders, and then select other orders that are not participating in the aggregation to aggregate; If the remaining orders cannot be grouped together with the shipping party, the remaining orders will be grouped together into the largest party according to the number of parties participating in the grouping, from the smallest to the largest, without exceeding the largest party.

8. The method for scheduling resource-constrained parallel machine orders based on a progressive locking strategy according to claim 6, It is characterized in that The iterating according to the progressive locking intelligent algorithm iterative strategy and updating the chromosome information in each population includes: Get the number of iterations, population size n, mutation probability, and asymptotic locking iteration number; Summarize the models and production quantities in the model production plan for each shift; According to the number of orders, the population is initialized by randomly generating an order priority sequence, and the population is sorted according to fitness to obtain the population; Two different chromosomes S1 and S2 are selected by roulette wheel, and gene positions C1 and C2 are randomly selected in chromosomes S1 and S2 for crossover exchange; Check whether there is duplication or missing of coding sequence numbers in S1 and S2 after crossing; If there are repeated or missing numbers in the coding sequence, a repeated number is randomly selected to replace the missing number; Decode the newly generated chromosomes and calculate the fitness value, and determine whether the chromosomes with better fitness values ​​in the population have the same gene sequence; If the same gene sequence exists, the address concentration time range to which the gene sequence belongs is divided and the number of occurrences of its iterations is recorded. When the number of occurrences in the continuous iteration process exceeds the asymptotic locking iteration number, the current part of the order is fixed, the relevant production capacity in the model scheduling plan is deducted, and the iteration plan of the current part in the chromosome is deleted; Select the best n individuals from all parent individuals and offspring individuals as the population for the next generation; Check whether the current situation meets the mutation conditions. If so, randomly generate a new chromosome and insert it into the population, and remove the chromosome with a low fitness evaluation index. Repeat the iteration until all chromosome gene positions are deleted or the upper limit of the iteration number is reached.

9. A resource-constrained parallel machine order scheduling system based on progressive locking strategy, It is characterized in that include: Data acquisition module, used to obtain order data; A data processing module, used to calculate an order scheduling plan based on the order data using a heuristic algorithm; The optimization module is used to optimize the order scheduling plan by adopting an order scheduling intelligent algorithm based on a progressive locking strategy.

10. An electronic device, It is characterized in that include: A processor and a memory, wherein the memory is used to store computer program code, wherein the computer program code includes computer instructions, and when the processor executes the computer instructions, the electronic device executes a resource-constrained parallel machine order scheduling method based on a progressive locking strategy as described in any one of claims 1 to 8.

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