CN116822815A - Advanced planning and production system for lithium iron phosphate workshop - Google Patents
Advanced planning and production system for lithium iron phosphate workshop Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 88
- 238000013439 planning Methods 0.000 title claims abstract description 66
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 title claims abstract description 30
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 68
- 239000000463 material Substances 0.000 claims abstract description 65
- 238000007726 management method Methods 0.000 claims abstract description 58
- 238000003860 storage Methods 0.000 claims abstract description 11
- 238000000034 method Methods 0.000 claims description 63
- 238000011156 evaluation Methods 0.000 claims description 20
- 238000012986 modification Methods 0.000 claims description 17
- 230000004048 modification Effects 0.000 claims description 17
- 238000012384 transportation and delivery Methods 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 12
- 238000012795 verification Methods 0.000 claims description 12
- 238000002360 preparation method Methods 0.000 claims description 10
- 238000007599 discharging Methods 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 6
- 238000007619 statistical method Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 4
- 238000012217 deletion Methods 0.000 description 4
- 230000037430 deletion Effects 0.000 description 4
- 229910001416 lithium ion Inorganic materials 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a high-level planning and scheduling system for a lithium iron phosphate workshop, and belongs to the technical field of high-level planning and scheduling of workshops. The system comprises a scheduling plan executing module, a basic data module, a knowledge base storage module, a scheduling plan management module, a scheduling statistics module, a system setting module and a scheduling plan output module; the scheduling plan executing module is used for receiving and executing the scheduling plan output by the scheduling plan output module; the basic data module is used for being connected with the knowledge base storage module. The invention solves the problems of low scheduling efficiency of the existing production plan of the lithium iron phosphate workshop, and dynamically calculates the equipment running state, material supply condition, capacity load condition and personnel state of the lithium iron phosphate workshop through the advanced plan scheduling technology based on the constraint algorithm, thereby improving the scheduling efficiency of the workshop.
Description
Technical Field
The invention relates to the technical field of advanced planning and production scheduling of workshops, in particular to an advanced planning and production scheduling system of a lithium iron phosphate workshops.
Background
Compared with other secondary batteries, the lithium ion battery has obvious advantages in energy density, cycle life, environmental protection and the like. With the vigorous development of the lithium ion battery industry, the application field of the lithium ion battery is gradually expanded from the original digital and electric tool fields to the electric automobile field. In recent years, the demand of power batteries has been increasing in an explosive manner, and automation of battery production has been a trend. In a lithium ion battery production workshop, APS advanced planning and scheduling are used for solving the problems of production scheduling and production scheduling, which are often called sequencing problems or resource allocation problems, in discrete industry, APS is used for solving the problem of optimal scheduling of multiple procedures and multiple resources, and in process industry, APS is used for solving the problem of sequential optimization, and the problem of optimizing the sequence and scheduling is solved simultaneously by a mixed model of processes and discrete, so that the APS has important significance for solving the problem of minimizing key chains and cost time for project management and project manufacturing.
In the prior art, the application number is as follows: CN113034051AA discloses an intelligent factory dynamic plan scheduling system, which can prioritize the production plan receiving orders, and combine the production conditions of actual production workshops to judge the plan schedule to be out of limit, give reasonable plan schedule, and judge the plan schedule to be out of limit, and re-schedule the out-of-limit plan schedule to avoid the out-of-limit production plan.
However, in the above-described technique, the existing lithium iron phosphate plant production schedule is prioritized, but the plant scheduling efficiency is not high.
In view of these drawbacks, it is necessary to design an advanced planning and production system for lithium iron phosphate workshops.
Disclosure of Invention
The invention aims to provide a high-level plan scheduling system for a lithium iron phosphate workshop, which can solve the problems that the scheduling efficiency of the workshop is not high although the production plan of the lithium iron phosphate workshop in the prior art is subjected to priority scheduling.
In order to achieve the above purpose, the present invention provides the following technical solutions: the advanced planning and production scheduling system for the lithium iron phosphate workshop comprises a production scheduling plan execution module, a basic data module, a knowledge base storage module, a production scheduling plan management module, a production scheduling statistics module, a system setting module and a production scheduling plan output module;
the scheduling plan executing module is used for receiving and executing the scheduling plan output by the scheduling plan output module;
the basic data module is used for being connected with the knowledge storage module and storing various real-time resource information of material main data, process route main data, BOM main data and equipment tool main data related to planning and scheduling;
the scheduling plan management module comprises order management, procedure management, component management and scheduling;
the order management is used for managing the specific condition of each order in the planning scope on the required resources, and comprises a data preparation module, an order inquiry module and an order modification module;
the data preparation module is used for importing order data in batches, and importing the data of the prepared Excel table into an order management;
the order inquiry module is used for inquiring specific order information through searching an order number or a material number;
the order modification module is used for modifying information of the planned quantity, the planned starting time and the planned ending time of the order;
the process management is used for managing different processes of the same order;
the component management is used for managing all parts in the scheduling plan;
the planning scheduling is used for checking one or more orders and scheduling the orders according to the sequence of planning time and sequence;
the scheduling statistical module is used for carrying out statistical analysis and display on the scheduling result generated by the plan management module in a stacking bar chart mode;
the system setting module is used for setting and managing a scheduling strategy, a scheduling range, a scheduling strategy, a priority and a weight factor;
the scheduling strategy is used for performing advanced scheduling by executing a planning method for sequence adjustment;
the scheduling range is used for selecting different order objects according to time and state;
the scheduling strategy is used for setting different planning strategies of an inventory balance strategy and a device utilization rate maximization strategy to carry out advanced scheduling;
the priority is used for setting the minimum waiting time priority or the minimum die change priority for high-level production scheduling;
the weight factors are used for setting weights of different capacity constraints, and specifically include advanced production of equipment, materials, tools, productivity and personnel states, wherein the weights are occupied in production.
Further, the order management further comprises an order auditing module, an order state module, an order processing module, an order tracking module, an order adding module and a refund bill module, wherein the order auditing module, the order state module, the order processing module, the order tracking module, the order adding module and the refund bill module are respectively used for order auditing, order state, order processing, order tracking, order adding and refund bill.
Further, the basic data module comprises a login module, the login module comprises customer information, office information and employee information, the customer information is used for storing names, contact ways and numbers of customers, the office information is used for storing basic information and working information of various departments of an enterprise, the employee information is used for storing or modifying basic information and working states of employees, the basic data module is in signal connection with engineering data and inventory data, the engineering data is used for batch storage and management of bill of materials data and product workshop data, and the inventory data is used for inventory inquiry, inventory statistics, allocation notification, inventory early warning and replenishment notification of a production workshop.
Further, the order modification comprises order addition, order deletion and order refreshing, wherein the order addition is used for adding new orders, the order deletion is used for deleting orders which are invalid or invalid, the order refreshing is used for refreshing the interface of the order management module, and when one or more orders are added or deleted, the interface is required to be refreshed, so that real-time update of the orders is realized, and the delay is reduced.
Further, the system also comprises a production condition verification module for checking whether production conditions are met before production is executed; the scheduling condition verification module comprises:
the material demand generation sub-module is used for calling a material form of a corresponding product according to the product and the product quantity demand related to the order, and generating a discharging product material demand table by combining the product quantity and the material form of the order;
the material stock checking sub-module is used for retrieving stock material data information, checking various material demands in the discharging material demand list with stock material data information one by one, and checking whether stock materials meet the discharging demands;
the production equipment state checking sub-module is used for retrieving state information of all equipment on a production line corresponding to the ordered product and checking whether the equipment is abnormal one by one;
the verification result feedback sub-module is used for summarizing the stock materials which do not meet the production scheduling requirement and the lack quantity to generate a material purchasing table; and summarizing the abnormal equipment to generate an equipment maintenance requirement table.
Further, the process management includes process basic information including order number, shop number, work order number, process name, process state, equipment group, plan number, preparation man-hour, processing man-hour, detailed plan start time, detailed plan end time, plan use equipment, and the like, and process query and process modification, wherein the process query carries out multiple search query by the order number and the process number, and the process modification is modification of the shop number and the plan number of the process.
Furthermore, the stacking bar chart reflects the difference of data by using the length of the bar, and all the scheduling results are visualized, so that the data expression is more visual, and the scheduling plan can be conveniently checked and adjusted by a planner.
Further, the scheduling strategy comprises scheduling according to the current time, scheduling based on the earliest starting time of the order, inverted scheduling based on the scheduling time range, inverted scheduling based on the delivery period of the order and order priority; the scheduling scope comprises a scheduling date scope, a working procedure scope and an order scope, wherein the order scope comprises only selected orders and all orders; the scheduling strategy comprises an inventory balancing strategy and a device utilization maximizing strategy; the priorities include a minimum idle time priority, an earliest delivery period priority, a first-come-first-process, a second-come-last-process, a shortest operating time priority, a longest operating time priority, a shortest remaining time priority, and a longest remaining time priority; the weight factors include equipment capacity, personnel skills, materials, tooling and productivity.
Further, after the order is subjected to the scheduling policy selection, the system setting module is in signal connection with the scheduling statistics module, the scheduling statistics module can automatically generate preliminary scheduling statistics, the preliminary scheduling statistics comprises calculation of time consumed by scheduling, statistics of the number of scheduling orders and statistics of the number of scheduling procedures, the scheduling policy signal is connected with scheme evaluation, the scheme evaluation is used for comparing and evaluating multiple targets of a scheduling result scheme, a new scheme is generated by selecting a new scheme, and the new scheme is compared with a previous scheme, so that scheme comparison is performed.
Further, the plan evaluation includes a plan evaluation index including order on-time delivery rate, critical order on-time delivery rate, yield, inventory availability, equipment utilization, average number of equipment changes, and total overtime, an indication definition, and an index remark, the plan comparison is used to compare the comparison between the plans using different scheduling strategies, and a comparison analysis of the plan evaluation index data by referring to the index definition results in a better scheduling plan.
Further, the scheduling plan management module further comprises a month plan algorithm module, a day plan algorithm module and a shift plan algorithm module;
the month planning algorithm module is used for generating an optimal monthly production scheme according to the order management information and the algorithm tool;
the daily planning algorithm module is used for generating a daily production sequence plan according to specific management constraint and control parameters of production based on month planning information;
the shift planning algorithm module is used for generating a production sequence plan of each shift based on daily planning information according to actual production conditions.
Further, the scheduling strategy selects a decision model algorithm from a preset decision model library to make a decision, and the decision model library is pre-stored with a plurality of decision model algorithms;
the equipment, personnel and materials required by order scheduling are adopted as element forming element sets, and the membership value of a decision model algorithm and elements is calculated by adopting the following algorithm:
in the above-mentioned method, the step of,representing membership values of a kth decision model algorithm and an ith element; x is x ij Representing a j-th non-inferior solution of the decision model algorithm relative to the i-th element; />Representing the maximum value of the kth decision model algorithm; />Representing a minimum value of a kth decision model algorithm;
the normalized function of the j-th non-bad solution is expressed as follows:
in the above, ω j A normalization function representing a j-th non-inferior solution; n represents the total number of decision model algorithms existing in the decision model library; m represents the total number of non-inferior solutions of the decision model algorithm;
and selecting a decision model algorithm with the maximum normalized function value as an optimal decision model algorithm, and adopting the optimal decision model algorithm to carry out priority decision of the scheduling strategy.
Compared with the prior art, the invention has the following beneficial effects:
1. the advanced planning and production system for the lithium iron phosphate workshops provided by the invention sequentially generates the optimal solution in the potential solution population by setting constraint conditions, key resources, weights and the like, and solves the scheduling problem of the lithium iron phosphate workshops.
2. According to the advanced planning scheduling system for the lithium iron phosphate workshop, provided by the invention, the equipment running state, the material supply condition, the capacity load condition and the personnel state of the lithium iron phosphate workshop are dynamically calculated through the advanced planning scheduling technology based on the constraint algorithm, so that the scheduling efficiency of the workshop is improved.
3. The advanced planning and scheduling system for the lithium iron phosphate workshop fully considers various real-time resource content conditions, combines related parameter configuration, automatically schedules production tasks of the workshop by using a constraint algorithm, and provides scientific basis for daily operation planning decisions of planning staff of the workshop.
4. According to the advanced planning production scheduling system for the lithium iron phosphate workshop, the data of the prepared Excel list is imported into the booking management, and the production scheduling statistical module is used for carrying out statistical analysis and display on the production scheduling result generated by the planning management module in a stacking bar chart mode, so that the data are imported rapidly, and convenience is brought to personnel to check and adjust.
5. The invention provides a high-level planning scheduling system for a lithium iron phosphate workshop, wherein a scheduling strategy comprises scheduling according to the current time, scheduling based on the earliest starting time of an order, scheduling based on the scheduling time range, scheduling based on the scheduling of an order delivery period and order priority, a scheduling strategy signal is connected with a scheme evaluation, the scheme evaluation is used for comparing and evaluating multiple targets of a scheduling result scheme, a new scheme is generated by selecting the scheduling strategy, and the new scheme is compared with the previous scheme, so that scheme comparison is performed.
Drawings
FIG. 1 is an overall system flow diagram of the present invention;
FIG. 2 is a flow chart of a scheduling management module according to the present invention;
FIG. 3 is a flow chart of a basic data module of the present invention;
FIG. 4 is a flow chart of a process management module according to the present invention;
FIG. 5 is a flow chart of a system setup module of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the technical problems of low scheduling efficiency of the existing lithium iron phosphate workshop production plan, although priority scheduling is performed, referring to fig. 1-5, the following technical scheme is provided:
the advanced planning and production scheduling system for the lithium iron phosphate workshop comprises a production scheduling plan execution module, a basic data module, a knowledge base storage module, a production scheduling plan management module, a production scheduling statistics module, a system setting module and a production scheduling plan output module;
the scheduling plan execution module is used for receiving and executing the scheduling plan output by the scheduling plan output module;
the basic data module is used for being connected with the knowledge storage module and storing various real-time resource information of material main data, process route main data, BOM main data and equipment tool main data related to planning and scheduling;
the scheduling plan management module comprises order management, procedure management, component management and scheduling;
the order management is used for managing the specific condition of each order in the planning scope on the required resources, and comprises a data preparation module, an order inquiry module and an order modification module;
the data preparation module is used for importing order data in batches, and importing the data of the prepared Excel table into an order management;
the order inquiry module is used for inquiring the specific order information through the retrieval of the order number or the material number;
the order modification module is used for modifying information of the planned quantity, the planned starting time and the planned ending time of the order;
the process management is used for managing different processes of the same order;
the component management is used for managing all parts in the scheduling plan;
planning scheduling is used for checking one or more orders and scheduling the orders according to the sequence of planning time and sequence;
the scheduling statistical module is used for carrying out statistical analysis and display on the scheduling result generated by the plan management module in a stacking bar chart mode;
the system setting module is used for setting and managing a scheduling strategy, a scheduling range, a scheduling strategy, a priority and a weight factor;
the scheduling strategy is used for performing advanced scheduling by executing a planning method for sequence adjustment;
the scheduling range is used for selecting different order objects according to time and state;
the scheduling strategy is used for setting different planning strategies of an inventory balance strategy and a device utilization rate maximization strategy to carry out advanced scheduling;
the priority is used for setting the minimum waiting time priority or the minimum die change priority for high-level production scheduling;
the weight factors are used for setting weights of different capacity constraints, and specifically include advanced production scheduling of equipment, materials, tools, productivity and personnel states by the weights in the production scheduling.
The order management further comprises an order auditing module, an order state module, an order processing module, an order tracking module, an order adding module and a refund bill module, wherein the order auditing module, the order state module, the order processing module, the order tracking module, the order adding module and the refund bill module are respectively used for order auditing, order state, order processing, order tracking, order adding and refund bill.
The order modification comprises order addition, order deletion and order refreshing, wherein the order addition is used for adding new orders, the order deletion is used for deleting the orders which are invalid or invalid, the order refreshing is used for refreshing the interface of the order management module, and when one or more orders are added or deleted, the interface is required to be refreshed, so that the real-time update of the orders is realized, and the delay is reduced.
The system is provided with a production condition verification module which is used for checking whether production conditions are met before production is executed; the production scheduling condition verification module comprises a material demand generation sub-module, a material inventory verification sub-module, a production equipment state verification sub-module and a verification result feedback sub-module.
The material demand generation sub-module is used for connecting the material database to call a material form of a corresponding product according to the product and the product quantity demand related to the order, and generating a discharging product material demand table by combining the product quantity and the material form of the order;
the material stock checking submodule is used for calling stock material data information, checking various material demands in the discharging material demand list with stock material data information one by one, and checking whether stock materials meet the discharging demands;
the production equipment state checking submodule is used for connecting a production line control system, calling state information of all equipment on a production line corresponding to an order product, and checking whether the equipment is abnormal one by one;
the verification result feedback submodule is used for summarizing the stock materials which do not meet the production scheduling requirement and the lack quantity to generate a material purchasing table; and summarizing the abnormal equipment to generate an equipment maintenance requirement table. By automatically verifying the order requirement, checking whether the production materials and equipment conditions can be met, if the conditions can not be met, respectively listing the unsatisfied production materials and equipment into a material purchasing table and an equipment maintenance requirement table, so that material purchasing can be arranged according to the material purchasing table, and an equipment maintenance task list can be issued according to the equipment maintenance requirement table in time, so that the materials can be prepared uniformly and the equipment can be adjusted to an optimal state before the start date of the production, and smooth start and completion of the production order can be ensured.
The stacked bar graph reflects the difference of data by utilizing the length of the bar, and all the scheduling results are visualized, so that the data representation is more visual, and the scheduling plan can be conveniently checked and adjusted by a planner.
The scheduling plan management module also comprises a month plan algorithm module, a day plan algorithm module and a shift plan algorithm module;
the month planning algorithm module is used for generating an optimal monthly production scheme according to the order management information and the algorithm tool;
the daily planning algorithm module is used for generating a daily production sequence plan according to the specific management constraint and control parameters of production based on the month planning information;
the shift planning algorithm module is used for generating a production sequence plan of each shift based on the daily planning information according to actual production conditions.
Specifically, the login module is used for logging in the system with the identity of an administrator, after an account number and a password are input, each functional module of the system interface is managed and maintained, then data preparation is carried out, an external Excel order data form is imported into the order management system through a selection file, order selection is used for scheduling, scheduling is carried out according to the time priority order, then advanced scheduling is carried out, a scheduling strategy module of the system interface is selected and entered, strategy selection is carried out according to a scheduling plan, finally scheme evaluation and comparison are carried out, and a better scheduling scheme is obtained.
The basic data module comprises a login module, the login module comprises customer information, office information and employee information, the customer information is used for storing names, contact ways and numbers of customers, the office information is used for storing basic information and working information of various departments of an enterprise, the employee information is used for storing or modifying basic information and working states of employees, the basic data module is in signal connection with engineering data and inventory data, the engineering data is used for batch storage and management of bill of materials data and product workshop data, and the inventory data is used for inventory inquiry, inventory statistics, allocation notification, inventory early warning and replenishment notification of a production workshop.
The process management includes process basic information including order number, workshop, work order number, process name, process state, equipment group, plan number, preparation man-hour, processing man-hour, detailed plan start time, detailed plan end time, plan use equipment and other information, process inquiry, multiple search inquiry is carried out by the order number and the process number, and the process modification is to modify the workshop and plan number of the process.
The scheduling strategy comprises scheduling according to the current time, scheduling based on the earliest starting time of the order, inverted scheduling based on the scheduling time range, inverted scheduling based on the delivery date of the order and order priority; the order scope includes an order date scope, a process scope and an order scope, and the order scope includes only selected orders and all orders; the scheduling strategy comprises an inventory balancing strategy and a device utilization maximizing strategy; the priorities include a minimum idle time priority, an earliest delivery period priority, a first-come-first-process, a second-come-last-process, a shortest operating time priority, a longest operating time priority, a shortest remaining time priority, and a longest remaining time priority; the weight factors include equipment capacity, personnel skills, materials, tooling and productivity.
After the order is subjected to scheduling strategy selection, the system setting module is in signal connection with the scheduling statistics module, the scheduling statistics module can automatically generate preliminary scheduling statistics, the preliminary scheduling statistics comprise calculation of time consumed by scheduling, statistics of the number of scheduling orders and statistics of the number of scheduling procedures, the scheduling strategy signal is connected with scheme evaluation, the scheme evaluation is used for comparing and evaluating multiple targets of a scheduling result scheme, a new scheme is generated by selecting the scheduling strategy, and the new scheme is compared with the previous scheme, so that scheme comparison is performed.
The scheme evaluation comprises a scheme evaluation index, an indication definition and an index remark, wherein the scheme evaluation index comprises order on-time delivery rate, key order on-time delivery rate, yield, inventory availability, equipment utilization rate, equipment average change times and total overtime time, the scheme comparison is used for comparing the comparison between schemes using different scheduling strategies, and the better scheduling scheme is obtained through the comparison analysis of the scheme evaluation index data by referring to the index definition.
The scheduling strategy is to select a decision model algorithm to make a decision by a preset decision model library, wherein the decision model library is pre-stored with a plurality of decision model algorithms;
the equipment, personnel and materials required by order scheduling are adopted as element forming element sets, and the membership value of a decision model algorithm and elements is calculated by adopting the following algorithm:
in the above-mentioned method, the step of,representing membership values of a kth decision model algorithm and an ith element; x is x ij Representing a j-th non-inferior solution of the decision model algorithm relative to the i-th element; />Representing the maximum value of the kth decision model algorithm; />Representing a minimum value of a kth decision model algorithm;
the normalized function of the j-th non-bad solution is expressed as follows:
in the above, ω j A normalization function representing a j-th non-inferior solution; n represents the total number of decision model algorithms existing in the decision model library; m represents the total number of non-inferior solutions of the decision model algorithm;
and selecting a decision model algorithm with the maximum normalized function value as an optimal decision model algorithm, and adopting the optimal decision model algorithm to carry out priority decision of the scheduling strategy.
The system stores decision model algorithms in the decision model algorithms according to the element conditions and the order conditions, and the scheduling strategies adopt the algorithms to select the decision model algorithms first, so that the optimal decision model algorithm is selected from the decision model libraries to realize scheduling.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should be covered by the protection scope of the present invention by making equivalents and modifications to the technical solution and the inventive concept thereof.
Claims (10)
1. The advanced plan scheduling system for the lithium iron phosphate workshop comprises a scheduling plan execution module, a basic data module, a knowledge base storage module, a scheduling plan management module, a scheduling statistics module, a system setting module and a scheduling plan output module, and is characterized in that,
the scheduling plan executing module is used for receiving and executing the scheduling plan output by the scheduling plan output module;
the basic data module is used for being connected with the knowledge storage module and storing various real-time resource information of material main data, process route main data, BOM main data and equipment tool main data related to planning and scheduling;
the scheduling plan management module comprises order management, procedure management, component management and scheduling;
the order management is used for managing the specific condition of each order in the planning scope on the required resources, and comprises a data preparation module, an order inquiry module and an order modification module;
the data preparation module is used for importing order data in batches, and importing the data of the prepared Excel table into an order management;
the order inquiry module is used for inquiring specific order information through searching an order number or a material number;
the order modification module is used for modifying information of the planned quantity, the planned starting time and the planned ending time of the order;
the process management is used for managing different processes of the same order;
the component management is used for managing all parts in the scheduling plan;
the planning scheduling is used for checking one or more orders and scheduling the orders according to the sequence of planning time and sequence;
the scheduling statistical module is used for carrying out statistical analysis and display on the scheduling result generated by the plan management module in a stacking bar chart mode, the stacking bar chart reflects the difference of data by utilizing the length of the bar, and all the scheduling results are visualized, so that the data representation is more visual, and the planner can conveniently check and adjust the scheduling plan;
the system setting module is used for setting and managing a scheduling strategy, a scheduling range, a scheduling strategy, a priority and a weight factor;
the scheduling strategy is used for performing advanced scheduling by executing a planning method for sequence adjustment;
the scheduling range is used for selecting different order objects according to time and state;
the scheduling strategy is used for setting different planning strategies of an inventory balance strategy and a device utilization rate maximization strategy to carry out advanced scheduling;
the priority is used for setting the minimum waiting time priority or the minimum die change priority for high-level production scheduling;
the weight factors are used for setting weights of different capacity constraints, and specifically include advanced production of equipment, materials, tools, productivity and personnel states, wherein the weights are occupied in production.
2. The lithium iron phosphate shop advanced planning production system according to claim 1, wherein the order management further comprises an order auditing module, an order status module, an order processing module, an order tracking module, an order adding module and a return order module, the order auditing module, the order status module, the order processing module, the order tracking module, the order adding module and the return order module are respectively used for order auditing, order status, order processing, order tracking, order adding and return order, the order modification comprises order adding, order deleting and order refreshing, the order adding is used for adding new orders, the order deleting is used for deleting orders which are invalid or invalid, the order refreshing is used for refreshing an interface of the order management module, and when one or more orders are added or deleted, the interface is refreshed, so that real-time updating of the orders is realized.
3. The lithium iron phosphate plant advanced planning production system according to claim 1, wherein the basic data module comprises a login module, the login module comprises client information, office information and employee information, the client information is used for storing names, contact ways and numbers of clients, the office information is used for storing basic information and working information of various departments of an enterprise, the employee information is used for storing or modifying basic information and working states of employees, the basic data module is in signal connection with engineering data and inventory data, the engineering data is used for batch storage and management of bill of materials data and product plant data, and the inventory data is used for inventory inquiry, inventory statistics, transfer notification, inventory pre-warning and replenishment notification of a production plant.
4. The advanced planning production system for a lithium iron phosphate shop according to claim 1, further comprising a production condition verification module for checking whether production conditions are met before the execution of the production; the scheduling condition verification module comprises:
the material demand generation sub-module is used for calling a material form of a corresponding product according to the product and the product quantity demand related to the order, and generating a discharging product material demand table by combining the product quantity and the material form of the order;
the material stock checking sub-module is used for retrieving stock material data information, checking various material demands in the discharging material demand list with stock material data information one by one, and checking whether stock materials meet the discharging demands;
the production equipment state checking sub-module is used for retrieving state information of all equipment on a production line corresponding to the ordered product and checking whether the equipment is abnormal one by one;
the verification result feedback sub-module is used for summarizing the stock materials which do not meet the production scheduling requirement and the lack quantity to generate a material purchasing table; and summarizing the abnormal equipment to generate an equipment maintenance requirement table.
5. The advanced planning scheduling system for lithium iron phosphate workshops according to claim 1, wherein the procedure management includes procedure basic information including order number, workshops, work order numbers, procedure names, procedure states, equipment groups, planning numbers, preparation man-hours, processing man-hours, detailed planning start times, detailed planning end times, planning use equipment and the like, and procedure query and procedure modification, wherein the procedure query carries out multiple search query by the order number and the procedure number, and the procedure modification is modification of workshops and planning numbers of procedures.
6. The lithium iron phosphate shop advanced planning scheduling system of claim 1, wherein the scheduling strategy comprises scheduling according to the current time, scheduling based on the earliest start time of the order, scheduling based on the scheduling time range, scheduling based on the delivery date of the order and order priority; the scheduling scope comprises a scheduling date scope, a working procedure scope and an order scope, wherein the order scope comprises only selected orders and all orders; the scheduling strategy comprises an inventory balancing strategy and a device utilization maximizing strategy; the priorities include a minimum idle time priority, an earliest delivery period priority, a first-come-first-process, a second-come-last-process, a shortest operating time priority, a longest operating time priority, a shortest remaining time priority, and a longest remaining time priority; the weight factors include equipment capacity, personnel skills, materials, tooling and productivity.
7. The advanced planning and scheduling system for lithium iron phosphate workshops according to claim 6, wherein after a scheduling strategy selection is performed on orders, the system setting module is in signal connection with the scheduling statistics module, the scheduling statistics module automatically generates preliminary scheduling statistics including calculation of time spent for scheduling, statistics of the number of scheduling orders and statistics of the number of scheduling procedures, the scheduling strategy signal is connected with a scheme evaluation, the scheme evaluation is used for comparing and evaluating multiple targets of a scheduling result scheme, a new scheme is generated by selecting the scheduling strategy, and the new scheme is compared with the previous scheme to perform scheme comparison.
8. The advanced planning and scheduling system for lithium iron phosphate workshops of claim 7, wherein: the scheme evaluation comprises a scheme evaluation index, an indication definition and an index remark, wherein the scheme evaluation index comprises order on-time delivery rate, key order on-time delivery rate, yield, inventory availability, equipment utilization rate, average equipment change times and total overtime, the scheme comparison is used for comparing the comparison between schemes using different scheduling strategies, and a better scheduling scheme is obtained through comparing and analyzing the scheme evaluation index data by referring to the index definition.
9. The advanced planning and scheduling system for lithium iron phosphate workshops of claim 8, wherein: the scheduling plan management module further comprises a month plan algorithm module, a day plan algorithm module and a shift plan algorithm module;
the month planning algorithm module is used for generating an optimal monthly production scheme according to the order management information and the algorithm tool;
the daily planning algorithm module is used for generating a daily production sequence plan according to specific management constraint and control parameters of production based on month planning information;
the shift planning algorithm module is used for generating a production sequence plan of each shift based on daily planning information according to actual production conditions.
10. The advanced planning and scheduling system for lithium iron phosphate workshops according to claim 1, wherein: the scheduling strategy is to select a decision model algorithm to make a decision by a preset decision model library, and the decision model library is pre-stored with a plurality of decision model algorithms;
the equipment, personnel and materials required by order scheduling are adopted as element forming element sets, and the membership value of a decision model algorithm and elements is calculated by adopting the following algorithm:
in the above-mentioned method, the step of,representing membership values of a kth decision model algorithm and an ith element; x is x ij Representing a j-th non-inferior solution of the decision model algorithm relative to the i-th element; />Representing the maximum value of the kth decision model algorithm; />Representing a minimum value of a kth decision model algorithm;
the normalized function of the j-th non-bad solution is expressed as follows:
in the above, ω j A normalization function representing a j-th non-inferior solution; n represents the total number of decision model algorithms existing in the decision model library; m represents the total number of non-inferior solutions of the decision model algorithm;
and selecting a decision model algorithm with the maximum normalized function value as an optimal decision model algorithm, and adopting the optimal decision model algorithm to carry out priority decision of the scheduling strategy.
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