CN112947319B

CN112947319B - Batch collection and scheduling optimization method and system for multiple production lines of cold rolling area of iron and steel enterprise

Info

Publication number: CN112947319B
Application number: CN201911267495.3A
Authority: CN
Inventors: 胡兵; 孙海燕; 沈春锋; 杨海飞; 支文瑜
Original assignee: Shanghai Baosight Software Co Ltd
Current assignee: Shanghai Baosight Software Co Ltd
Priority date: 2019-12-11
Filing date: 2019-12-11
Publication date: 2024-03-29
Anticipated expiration: 2039-12-11
Also published as: CN112947319A

Abstract

The invention provides a batch collection and scheduling optimization method and a system for a multi-production line of a cold rolling area of a steel enterprise, wherein the method comprises the following steps: step M1: acquiring the steel coil information to be produced on a cold rolling area production line, and creating an initial batch collecting and scheduling plan; step M2: calculating the total penalty of the initial aggregate batch scheduling plan; step M3: optimizing the initial batch scheduling plan according to the calculated total penalty of the initial batch scheduling plan; step M4: and selecting the final batch scheduling plan with the minimum optimization target. The invention solves the problem of batch collection and scheduling of multiple production lines in a cold rolling area of a steel enterprise; in the optimization process, GPU heterogeneous parallel computation is added, so that the optimization speed is greatly increased; the resulting rational scheduling plan will directly affect the quality of the product, the efficiency of the production and the cost of the production.

Description

Batch collection and scheduling optimization method and system for multiple production lines of cold rolling area of iron and steel enterprise

Technical Field

The invention relates to the technical field of batch processing, in particular to a batch collection and scheduling optimization method and system for a multi-production line of a cold rolling area of a steel enterprise.

Background

Taking a continuous annealing production line of a cold rolling area of a steel enterprise as an example, aiming at batch scheduling, not only is the process constraint of batch process considered, but also steel coils with the same characteristics are concentrated together for processing. It is also necessary to consider whether the welding relationship of the front and rear steel coils between the two batches meets the process requirements: the width jump change and the thickness jump change during welding between steel coils in the collection batch are required to be within the range of technological convention, otherwise, the front and rear opened steel coils cannot be successfully welded into continuous strip steel; when welding steel coils among the collection batches, not only the requirement of dimensional jump change is met, but also the jump change of temperature is required to be within the scope of technological convention. Otherwise, the temperature change is too large, and in order to enable the annealing furnace to reach the required annealing temperature, transition steel coils can only be inserted between two collection batches. Thus, unnecessary waste in the production process is caused, the production operation cost is increased, and delayed delivery is possibly caused, so that customer service is influenced.

The batch scheduling of multiple production lines is more complicated. Besides meeting the process requirements of each production line, the mutual influence between the processes immediately before and immediately after needs to be considered. For example, the production beats between the immediately preceding and the immediately following processes are inconsistent, which may cause problems of insufficient inventory, poor performance of steel coil, serious defects, frequent switching of production line production, and the like.

At present, the production schedule of the cold rolling area is finished manually according to experience. Because the production process is complex and has numerous constraints, the manual sorting is difficult to balance the influencing factors in all aspects within a limited time, and a better scheduling plan is found. The existing scheduling method only considers the multi-production line row and the batch scheduling at the same time. Therefore, the automatic batch-collecting scheduling optimization method of the multi-production line is particularly important.

The patent document CN101334660A (application number: 200810012090.0) discloses a steel coil optimizing and sorting method and system of a cold rolling continuous annealing machine set, and belongs to the technical field of metal material processing information, wherein the optimizing method comprises the following steps of firstly, sorting candidate steel coils from high to low and from low to high according to annealing temperature respectively to form two initial sorting schemes, and optimizing each initial sorting scheme by adopting a width priority sorting method or a thickness priority sorting method to obtain a plurality of groups of initial feasible sorting schemes; 2. selecting a sequencing scheme with the minimum optimized target value from the initial steel coil sequencing scheme as an initial feasible production plan; 3. for the initial feasible production plan, with the aim of optimizing the minimum target value of the sorting model, the invention uses the switching neighborhood tabu search and the alternating path transformation neighborhood search to adjust, and only considers the single machine group: the continuous annealing unit is not suitable for the scheduling of multiple production lines without considering the influence of the connection relation, the stock and the like between the previous and the next working procedures. Different from the present application: 1) This patent only considers scheduling of a single team (one production line); 2) The optimization modes are different; 3) GPU parallel computing is not used;

Patent document CN104376424A (application number: 201410705831.9) discloses a coordinated scheduling method for multi-production-line steel coils in a cold rolling area of a steel enterprise, which comprises the following steps: acquiring the information of steel coils to be discharged of each production line of a cold rolling area of an iron and steel enterprise; establishing a coordinated scheduling model of a multi-production-line steel coil in a cold rolling area of a steel enterprise; obtaining an initial coordinated scheduling scheme of the multi-production-line steel coil in the cold rolling area of the iron and steel enterprise by using a heuristic algorithm; carrying out real-time correction on the multi-production-line coordination scheduling scheme of the initial cold rolling area; and transmitting the modified multi-production-line coordination scheduling scheme of the cold rolling area to each production-line automatic control system of the cold rolling area of the steel enterprise to complete the coordination scheduling of the multi-production-line steel coils of the cold rolling area. The invention considers continuous scheduling of the multi-production-line batch collecting process, but does not consider using GPU heterogeneous parallel computing aiming at a large number of new scheduling schemes generated by each optimization, and the optimization speed is to be improved. Different from the present application: 1) The optimization modes are different; 2) GPU parallel computing is not used;

disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide a batch collection and scheduling optimization method and system for a plurality of production lines of a cold rolling area of a steel enterprise.

The invention provides a batch collection and scheduling optimization method for a multi-production line of a cold rolling area of a steel enterprise, which comprises the following steps:

Step M1: acquiring the steel coil information to be produced on a cold rolling area production line, and creating a candidate batch scheduling plan;

step M2: calculating the total penalty of the candidate set batch scheduling plan;

step M3: optimizing the candidate set batch scheduling plan according to the calculated total penalty of the candidate set batch scheduling plan;

step M4: and selecting the final batch scheduling plan with the minimum optimization total penalty.

Preferably, the step M1 includes:

step M1.1: sorting Cheng Gangjuan to be sorted on each production line according to the sequence of delivery time;

step M1.2: after sequencing according to the sequence of delivery time, finding the position of the first steel coil according to the steel coils with the same batch number, and forwardly adjusting the steel coils arranged at the back according to the preset processing upper limit number of each batch process to be concentrated into one batch for processing.

Preferably, the step M2 includes:

the total penalty of the initial aggregate batch scheduling plan includes a total penalty of a soft constraint and a total penalty of a hard constraint;

the total penalty of the soft constraint includes: punishment of equipment processing preference, jump punishment of thickness or width change of front and rear steel coils and punishment of batch switching;

the total penalty of the hard constraint includes: punishment of waiting time between two processes, punishment of steel coil delay delivery, punishment of steel coil quantity in collection batch not meeting upper limit or lower limit preset by process and punishment of inventory not meeting preset safety inventory or maximum inventory;

The total penalty formula for calculating the initial aggregate batch scheduling plan is as follows:

wherein c represents a penalty; the superscript m indicates a device tooling preference; the superscript tw indicates jump of thickness or width variation of the front and rear steel coils; superscript c denotes batch switching; the upper mark w represents the waiting time between two adjacent processes; the upper mark d represents the steel coil delivery time; the upper mark q represents the upper limit or the lower limit of the number of steel coils in a collection batch preset by a collection batch process; superscript s denotes inventory; p represents: the total number of production lines p; n represents: the total number n of steel coils;

representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment is generated on equipment processing preference of the steel coil i; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the steel coil i generates jump punishment due to the thickness or width change of the steel coil; />Representing: at the kthOn the individual production lines, whether the steel coil i is processed immediately before the steel coil j or not, wherein the steel coil i is punished due to batch collection and switching; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the waiting time between the two adjacent processes is larger than or smaller than the punishment of the preset time;

x _kij Representing: on the kth production line, whether the steel coil i is processed immediately before the steel coil j;representing: punishment generated by delayed delivery of the steel coil i; />Representing: the steel coil i does not meet punishment generated by the upper limit or the lower limit of the quantity preset by the batch collecting process; />Representing: the stock of the steel coil i does not meet the punishment generated by the preset safety stock or the maximum stock; constraint conditions:

wherein the decision variable x _kij Indicating whether coil i is immediately prior to coil j at the kth line and determining variable x if coil i is immediately prior to coil j _kij The values are bars, and when the steel coil i is not processed immediately before the steel coil j, the decision variable x is determined _kij Is 0; q represents the number of collection batches; s represents the stock quantity;indicating the start time of coil j in process k, < >>The finishing processing time of the steel coil i in the process k is represented; />Indicating the end processing time of coil i on the immediately preceding process k-strip, < >>The processing starting time of the steel coil i in the following procedure k is shown;

preferably, the step M3 includes: optimizing the candidate set batch scheduling plan, taking the minimum total penalty as a target, and carrying out cyclic iteration adjustment by utilizing heuristic neighborhood search and heuristic set batch transformation until the total penalty is no longer reduced;

Step M3.1: utilizing heuristic neighborhood search to continuously optimize a single scheduling plan to obtain a new candidate set batch scheduling plan;

step M3.2: obtaining a new candidate set batch scheduling plan through heuristic neighborhood searching, and adding the candidate scheduling plan by utilizing heuristic set batch transformation;

step M3.3: performing loop iteration adjustment by utilizing heuristic neighborhood search and heuristic batch-to-batch transformation until the total penalty of the selected candidate scheduling plan is no longer reduced;

the heuristic neighborhood search is continuously optimized for a single scheduling plan, and GPU parallel computing candidate set scheduling plan total penalty is adopted in the process of iterative optimization;

the heuristic batch transformation prevents the result from sinking to changes made by locally optimally increasing the number of candidate scheduling plans.

Preferably, the step M3.1 includes:

step M3.1.1: selecting the neighborhood search optimization with minimum total penalty from the candidate set batch scheduling plan;

step M3.1.2: traversing each batch from front to back in sequence to find out a steel coil with waiting time exceeding the longest waiting time preset by the process between the steel coil and the preceding working procedure; the current steel coil is sequentially moved forward for one step until the current steel coil is moved to the first processing of the current collection batch; traversing each batch from front to back in sequence to find out the steel coil with shortest waiting time which is insufficient in process constraint with the waiting time between the preceding working procedures; the current steel coil is sequentially moved backwards by one step until the current steel coil is moved to the last processing of the current collection batch; generating a new candidate set batch scheduling plan in one step every time the candidate set batch scheduling plan moves, occupying a thread, and waiting for calculating the total penalty of the candidate set batch scheduling plan;

Step M3.1.3: traversing each collection batch from front to back in sequence to find out steel coils which cause the stock of a unit to exceed a preset upper limit at a certain moment; the current steel coil is sequentially moved forward for one step until the current steel coil is moved to the first processing of the current collection batch; traversing each collection batch from front to back in sequence to find out steel coils which cause the shortage of safety stock of a unit at a certain moment; the current steel coil is sequentially moved backwards by one step until the current steel coil is moved to the last of the current collection batch; generating a new candidate batch scheduling plan in one step every time the candidate batch scheduling plan moves, recording the new candidate batch scheduling plan into a thread, and waiting for calculating the total penalty of the candidate batch scheduling plan;

step M3.1.4: traversing each collection batch from front to back in sequence to find out steel coils delayed for delivery; the current steel coil is sequentially moved forward for one step until the current steel coil is moved to the first processing of the current collection batch; generating a new candidate batch scheduling plan in one step every time the candidate batch scheduling plan moves, recording the new candidate batch scheduling plan into a thread, and waiting for calculating the total penalty of the candidate batch scheduling plan;

step M3.1.5: performing one-time GPU heterogeneous parallel calculation of the total penalty value of each candidate batch scheduling plan when generating a plurality of new candidate scheduling plans with preset values, and selecting one candidate batch scheduling plan with the minimum total penalty value as the new candidate scheduling plan;

Step M3.1.6: performing loop iteration adjustment by using heuristic neighborhood search until the total penalty of the selected candidate set batch scheduling plan is no longer reduced;

the step M3.2 includes:

step M3.2.1: obtaining a new candidate batch scheduling plan through heuristic neighborhood search, selecting from the new candidate batch scheduling plan, and performing heuristic batch transformation from the heuristic batch scheduling plan; and optimizing a scheduling plan with minimum total penalty;

step M3.2.2: traversing each collection batch from front to back in sequence to find out one collection batch with the largest number of steel coils delayed for delivery; splitting the current collection into two collections: a collection batch of all delay delivery steel coils and a collection batch of all on-time delivery steel coils; inserting small batches of all the delayed delivery steel coils into a current unit and processing before the batch with different batch numbers;

step M3.2.3: traversing each batch from front to back in sequence, finding out the batch with the number of steel coils exceeding the number preset by the batch process, and inserting the steel coils arranged at the back into the next batch for processing;

step M3.2.4: traversing each batch from front to back in sequence, finding out a batch with insufficient steel coil quantity in the batch, which is preset by a batch collecting process, and inserting the steel coil of the next batch in front into the batch for processing;

Step M3.2.5: adding n candidate batch scheduling plans generated after heuristic batch collection transformation into the candidate batch scheduling plans;

step M3.2.6: and deleting the preset number with the largest total penalty when the total candidate scheduling plan number exceeds the preset value, and keeping the total candidate number equal to the preset value.

According to the steel production method provided by the invention, the final batch scheduling plan with the minimum total punishment is obtained by using the batch scheduling optimization method of the multi-production line of the cold rolling area of the steel enterprise.

The invention provides a batch collection and scheduling optimization system for a multi-production line of a cold rolling area of a steel enterprise, which comprises the following components:

module M1: acquiring the steel coil information to be produced on a cold rolling area production line, and creating a candidate batch scheduling plan;

module M2: calculating the total penalty of the candidate set batch scheduling plan;

module M3: optimizing the candidate set batch scheduling plan according to the calculated total penalty of the candidate set batch scheduling plan;

module M4: and selecting the final batch scheduling plan with the minimum optimization total penalty.

Preferably, the module M1 comprises:

module M1.1: sorting Cheng Gangjuan to be sorted on each production line according to the sequence of delivery time;

Module M1.2: after sequencing according to the sequence of delivery time, finding the position of the first steel coil according to the steel coils with the same batch number, and forwardly adjusting the steel coils arranged at the back according to the preset processing upper limit number of each batch process to be concentrated into one batch for processing.

Preferably, the module M2 comprises:

Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment is generated on equipment processing preference of the steel coil i; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the steel coil i generates jump punishment due to the thickness or width change of the steel coil; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment of the steel coil i is generated due to batch collection and switching; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the waiting time between the two adjacent processes is larger than or smaller than the punishment of the preset time;

preferably, the module M3 comprises: optimizing the candidate set batch scheduling plan, taking the minimum total penalty as a target, and carrying out cyclic iteration adjustment by utilizing heuristic neighborhood search and heuristic set batch transformation until the total penalty is no longer reduced;

module M3.1: utilizing heuristic neighborhood search to continuously optimize a single scheduling plan to obtain a new candidate set batch scheduling plan;

module M3.2: obtaining a new candidate set batch scheduling plan through heuristic neighborhood searching, and adding the candidate scheduling plan by utilizing heuristic set batch transformation;

module M3.3: performing loop iteration adjustment by utilizing heuristic neighborhood search and heuristic batch-to-batch transformation until the total penalty of the selected candidate scheduling plan is no longer reduced;

Preferably, the module M3.1 comprises:

module M3.1.1: selecting the neighborhood search optimization with minimum total penalty from the candidate set batch scheduling plan;

module M3.1.2: traversing each batch from front to back in sequence to find out a steel coil with waiting time exceeding the longest waiting time preset by the process between the steel coil and the preceding working procedure; the current steel coil is sequentially moved forward for one step until the current steel coil is moved to the first processing of the current collection batch; traversing each batch from front to back in sequence to find out the steel coil with shortest waiting time which is insufficient in process constraint with the waiting time between the preceding working procedures; the current steel coil is sequentially moved backwards by one step until the current steel coil is moved to the last processing of the current collection batch; generating a new candidate set batch scheduling plan in one step every time the candidate set batch scheduling plan moves, occupying a thread, and waiting for calculating the total penalty of the candidate set batch scheduling plan;

module M3.1.3: traversing each collection batch from front to back in sequence to find out steel coils which cause the stock of a unit to exceed a preset upper limit at a certain moment; the current steel coil is sequentially moved forward for one step until the current steel coil is moved to the first processing of the current collection batch; traversing each collection batch from front to back in sequence to find out steel coils which cause the shortage of safety stock of a unit at a certain moment; the current steel coil is sequentially moved backwards by one step until the current steel coil is moved to the last of the current collection batch; generating a new candidate batch scheduling plan in one step every time the candidate batch scheduling plan moves, recording the new candidate batch scheduling plan into a thread, and waiting for calculating the total penalty of the candidate batch scheduling plan;

Module M3.1.4: traversing each collection batch from front to back in sequence to find out steel coils delayed for delivery; the current steel coil is sequentially moved forward for one step until the current steel coil is moved to the first processing of the current collection batch; generating a new candidate batch scheduling plan in one step every time the candidate batch scheduling plan moves, recording the new candidate batch scheduling plan into a thread, and waiting for calculating the total penalty of the candidate batch scheduling plan;

module M3.1.5: performing one-time GPU heterogeneous parallel calculation of the total penalty value of each candidate batch scheduling plan when generating a plurality of new candidate scheduling plans with preset values, and selecting one candidate batch scheduling plan with the minimum total penalty value as the new candidate scheduling plan;

module M3.1.6: performing loop iteration adjustment by using heuristic neighborhood search until the total penalty of the selected candidate set batch scheduling plan is no longer reduced;

the module M3.2 comprises:

module M3.2.1: obtaining a new candidate batch scheduling plan through heuristic neighborhood search, selecting from the new candidate batch scheduling plan, and performing heuristic batch transformation from the heuristic batch scheduling plan; and optimizing a scheduling plan with minimum total penalty;

module M3.2.2: traversing each collection batch from front to back in sequence to find out one collection batch with the largest number of steel coils delayed for delivery; splitting the current collection into two collections: a collection batch of all delay delivery steel coils and a collection batch of all on-time delivery steel coils; inserting small batches of all the delayed delivery steel coils into a current unit and processing before the batch with different batch numbers;

Module M3.2.3: traversing each batch from front to back in sequence, finding out the batch with the number of steel coils exceeding the number preset by the batch process, and inserting the steel coils arranged at the back into the next batch for processing;

module M3.2.4: traversing each batch from front to back in sequence, finding out a batch with insufficient steel coil quantity in the batch, which is preset by a batch collecting process, and inserting the steel coil of the next batch in front into the batch for processing;

module M3.2.5: adding n candidate batch scheduling plans generated after heuristic batch collection transformation into the candidate batch scheduling plans;

module M3.2.6: and deleting the preset number with the largest total penalty when the total candidate scheduling plan number exceeds the preset value, and keeping the total candidate number equal to the preset value.

Compared with the prior art, the invention has the following beneficial effects:

1. solves the problem of batch collection and scheduling of multiple production lines in a cold rolling area of an iron and steel enterprise;

2. in the optimization process, GPU heterogeneous parallel computation is added, so that the optimization speed is greatly increased; the resulting rational scheduling plan will directly affect the quality of the product, the efficiency of production and the cost of production;

3. and the field utilization rate is improved.

Drawings

Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:

FIG. 1 is a flow chart of a method for optimizing a multi-line batch schedule in a cold rolling zone;

FIG. 2 is a flow chart of a heuristic neighborhood search optimization method;

FIG. 3 is a flow chart of a heuristic batch transformation optimization method;

fig. 4 is a schematic diagram of one-step movement of steel coils in a collection batch to generate a new production schedule in heuristic neighborhood search;

FIG. 5 is a schematic diagram of batch splitting to generate a new production plan;

fig. 6 is a schematic diagram of the new production schedule generated by moving the steel coils in the collection lot when the number of steel coils exceeds the process requirement;

fig. 7 is a schematic diagram of the new production schedule generated by the movement of the steel coils in the lot when the number of steel coils is lower than the process requirement.

Detailed Description

The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.

A batch collecting and scheduling optimization method for multiple production lines in a cold rolling area of a steel enterprise is used for realizing automatic batch collecting and scheduling of the multiple production lines. Often, the cold rolling line requires centralized processing, i.e. batching, of workpieces having certain specific properties. For example, when the paint is changed frequently, the time and the cost are wasted, and steel coils with the same paint color can be concentrated together for processing.

According to the invention, as shown in fig. 1, the batch collection and scheduling optimization method for the multi-production line of the cold rolling area of the steel enterprise comprises the following steps:

specifically, the step M1 includes:

step M1.1: for the to-be-discharged Cheng Gangjuan on each production line: sorting Cheng Gangjuan to be sorted on each production line according to the sequence of delivery time;

Each batch process defines an upper limit on the number of coils in the batch. The waiting until Cheng Gangjuan is initially ordered in order of delivery time, so that each batch does not reach the upper processing limit previously agreed upon by the batch process. Therefore, the steel coils arranged in the following same batch collecting process are adjusted forwards, and a batch of processing is integrated.

Step M2: calculating the total penalty of the candidate set batch scheduling plan; a penalty is cumulatively calculated for the current scheduling scheme whenever a constraint is not satisfied. If the constraints are satisfied, the penalty is equal to 0.

Specifically, the step M2 includes:

representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment is generated on equipment processing preference of the steel coil i; / >Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the steel coil i generates jump punishment due to the thickness or width change of the steel coil; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment of the steel coil i is generated due to batch collection and switching; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the waiting time between the two adjacent processes is larger than or smaller than the punishment of the preset time;

representing: on the kth production line, whether the steel coil i is processed immediately before the steel coil j; />Representing: punishment generated by delayed delivery of the steel coil i; />Representing: the steel coil i does not meet punishment generated by the upper limit or the lower limit of the quantity preset by the batch collecting process; />Representing: the stock of the steel coil i does not meet the punishment generated by the preset safety stock or the maximum stock; constraint conditions:

specifically, the step M3 includes: optimizing the candidate set batch scheduling plan, taking the minimum total penalty as a target, and carrying out cyclic iteration adjustment by utilizing heuristic neighborhood search and heuristic set batch transformation until the total penalty is no longer reduced;

step M3.1: as shown in fig. 2, the heuristic neighborhood search is utilized to continuously optimize a single scheduling plan, and the total penalty of the scheduling plan is calculated by adopting a GPU parallel calculation method in the process of iterative optimization each time, so as to obtain a new candidate set batch scheduling plan;

specifically, the step M3.1 includes:

step M3.1.1: selecting the neighborhood search optimization with minimum total penalty from a series of candidate set batch scheduling plans;

step M3.1.4: traversing each collection batch from front to back in sequence to find out steel coils delayed for delivery; performing transformation as shown in fig. 4, and sequentially moving the current steel coil forward for one step until the current steel coil moves to the first processing of the current batch; generating a new candidate batch scheduling plan in one step every time the candidate batch scheduling plan moves, recording the new candidate batch scheduling plan into a thread, and waiting for calculating the total penalty of the candidate batch scheduling plan;

step M3.1.5: performing one-time GPU heterogeneous parallel calculation on the total penalty value of each candidate batch scheduling plan when 2000 new candidate scheduling plans are generated, and selecting one candidate batch scheduling plan with the minimum total penalty value as the new candidate scheduling plan;

Step M3.1.6: performing loop iteration adjustment by using heuristic neighborhood search, and iterating the steps M3.1.2 to M3.1.5 until the total penalty of the selected candidate set batch scheduling plan is no longer reduced;

step M3.2: as shown in fig. 3, a new candidate set batch scheduling plan is obtained through heuristic neighborhood searching, and the candidate scheduling plan is increased by using heuristic set batch transformation;

the step M3.2 includes:

step M3.2.1: obtaining a new candidate batch scheduling plan through heuristic neighborhood search, selecting from a new series of candidate batch scheduling plans, and performing heuristic batch transformation from never performed; and optimizing a scheduling plan with minimum total penalty;

step M3.2.2: traversing each collection batch from front to back in sequence to find out one collection batch with the largest number of steel coils delayed for delivery; the transformation shown in fig. 5 is performed to split the current batch into two batches: a collection batch of all delay delivery steel coils and a collection batch of all on-time delivery steel coils; inserting small batches of all the delayed delivery steel coils into a current unit and processing before the batch with different batch numbers;

step M3.2.3: traversing each batch from front to back in sequence, finding out the batch with the number of steel coils in the batch exceeding the number preset by the batch process, carrying out transformation as shown in fig. 6, and inserting the steel coils arranged at the back into the next batch for processing;

Step M3.2.4: traversing each batch from front to back in sequence, finding out the batch with the quantity of steel coils in the batch being insufficient and preset by the batch collecting process, carrying out transformation as shown in fig. 7, and inserting the steel coils in the next batch in front into the batch for processing;

step M3.2.6: when the total candidate schedule number exceeds 10, the first m of the total penalties are deleted, keeping the total number of candidates equal to 10.

Only one candidate scheduling plan is initially selected, the optimal one is searched and screened through the heuristic neighborhood, and only one candidate scheduling plan is still selected, at this time, heuristic batch conversion is performed on the candidate scheduling plan, n candidate scheduling plans are generated, and the n candidate scheduling plans are added into the candidate batch scheduling plan. Each time, selecting a plan which is never subjected to heuristic batch conversion from the candidate scheduling plans to carry out heuristic batch conversion;

This is a cyclical process, alternating until the optimal scheduling scheme is generated;

step M4: comparing a series of candidate scheduling plans, and selecting the final aggregate scheduling plan with the smallest optimization total penalty.

Many schedules are generated during the optimization process, and it is a series of candidate schedules herein. The heuristic neighborhood search is continuously optimized aiming at a scheduling plan, and the heuristic batch-collecting transformation is carried out to prevent the result from being trapped in local optimization; here, two loops, the first one is a large loop, the second one is a small loop inside the heuristic neighborhood search, and three heuristic directions, each of which generates a plurality of new scheduling schemes, but every 2000 new schemes are generated, the best one is selected to continue the heuristic neighborhood search until the heuristic neighborhood search is optimal.

According to the invention, as shown in fig. 1, the batch collection and scheduling optimization system for the multi-production line of the cold rolling area of the steel enterprise comprises:

specifically, the module M1 includes:

module M1.1: for the to-be-discharged Cheng Gangjuan on each production line: sorting Cheng Gangjuan to be sorted on each production line according to the sequence of delivery time;

Module M2: calculating the total penalty of the candidate set batch scheduling plan; a penalty is cumulatively calculated for the current scheduling scheme whenever a constraint is not satisfied. If the constraints are satisfied, the penalty is equal to 0.

Specifically, the module M2 includes:

Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment is generated on equipment processing preference of the steel coil i; />Representing: in the kth production line, if coil i is processed immediately before coil j, coil i is due to coil thickness or widthJump penalty generated by the change in degree; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment of the steel coil i is generated due to batch collection and switching; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the waiting time between the two adjacent processes is larger than or smaller than the punishment of the preset time;

specifically, the module M3 includes: optimizing the candidate set batch scheduling plan, taking the minimum total penalty as a target, and carrying out cyclic iteration adjustment by utilizing heuristic neighborhood search and heuristic set batch transformation until the total penalty is no longer reduced;

module M3.1: as shown in fig. 2, the heuristic neighborhood search is utilized to continuously optimize a single scheduling plan, and the total penalty of the scheduling plan is calculated by adopting a GPU parallel calculation method in the process of iterative optimization each time, so as to obtain a new candidate set batch scheduling plan;

specifically, the module M3.1 comprises:

module M3.1.1: selecting the neighborhood search optimization with minimum total penalty from a series of candidate set batch scheduling plans;

Module M3.1.4: traversing each collection batch from front to back in sequence to find out steel coils delayed for delivery; performing transformation as shown in fig. 4, and sequentially moving the current steel coil forward for one step until the current steel coil moves to the first processing of the current batch; generating a new candidate batch scheduling plan in one step every time the candidate batch scheduling plan moves, recording the new candidate batch scheduling plan into a thread, and waiting for calculating the total penalty of the candidate batch scheduling plan;

module M3.1.5: performing one-time GPU heterogeneous parallel calculation on the total penalty value of each candidate batch scheduling plan when 2000 new candidate scheduling plans are generated, and selecting one candidate batch scheduling plan with the minimum total penalty value as the new candidate scheduling plan;

module M3.1.6: performing loop iteration adjustment by using heuristic neighborhood search, and iterating the loop modules M3.1.2 to M3.1.5 until the total penalty of the selected candidate set batch scheduling plan is no longer reduced;

module M3.2: as shown in fig. 3, a new candidate set batch scheduling plan is obtained through heuristic neighborhood searching, and the candidate scheduling plan is increased by using heuristic set batch transformation;

the module M3.2 comprises:

module M3.2.1: obtaining a new candidate batch scheduling plan through heuristic neighborhood search, selecting from a new series of candidate batch scheduling plans, and performing heuristic batch transformation from never performed; and optimizing a scheduling plan with minimum total penalty;

Module M3.2.2: traversing each collection batch from front to back in sequence to find out one collection batch with the largest number of steel coils delayed for delivery; the transformation shown in fig. 5 is performed to split the current batch into two batches: a collection batch of all delay delivery steel coils and a collection batch of all on-time delivery steel coils; inserting small batches of all the delayed delivery steel coils into a current unit and processing before the batch with different batch numbers;

module M3.2.3: traversing each batch from front to back in sequence, finding out the batch with the number of steel coils in the batch exceeding the number preset by the batch process, carrying out transformation as shown in fig. 6, and inserting the steel coils arranged at the back into the next batch for processing;

module M3.2.4: traversing each batch from front to back in sequence, finding out the batch with the quantity of steel coils in the batch being insufficient and preset by the batch collecting process, carrying out transformation as shown in fig. 7, and inserting the steel coils in the next batch in front into the batch for processing;

module M3.2.6: when the total candidate schedule number exceeds 10, the first m of the total penalties are deleted, keeping the total number of candidates equal to 10.

module M4: comparing a series of candidate scheduling plans, and selecting the final aggregate scheduling plan with the smallest optimization total penalty.

In the description of the present application, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientations or positional relationships illustrated in the drawings, merely to facilitate description of the present application and simplify the description, and do not indicate or imply that the devices or elements being referred to must have a specific orientation, be configured and operated in a specific orientation, and are not to be construed as limiting the present application.

Those skilled in the art will appreciate that the systems, apparatus, and their respective modules provided herein may be implemented entirely by logic programming of method steps such that the systems, apparatus, and their respective modules are implemented as logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc., in addition to the systems, apparatus, and their respective modules being implemented as pure computer readable program code. Therefore, the system, the apparatus, and the respective modules thereof provided by the present invention may be regarded as one hardware component, and the modules included therein for implementing various programs may also be regarded as structures within the hardware component; modules for implementing various functions may also be regarded as being either software programs for implementing the methods or structures within hardware components.

The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.

Claims

1. The batch collection and scheduling optimization method for the multi-production line of the cold rolling area of the iron and steel enterprise is characterized by comprising the following steps of:

step M4: selecting the final batch scheduling plan with the minimum optimization total penalty;

the step M1 includes:

step M1.2: after sequencing according to the sequence of delivery time, finding the position of the first steel coil according to the steel coils with the same batch number, and forwardly adjusting the steel coils arranged at the back according to the preset processing upper limit number of each batch process to be concentrated into one batch for processing;

the step M2 includes:

the total penalty of the initial aggregate batch scheduling plan includes the total penalty of the soft constraint and the total penalty of the hard constraint;

wherein c represents a penalty; the superscript m indicates a device tooling preference; the superscript tw indicates jump of thickness or width variation of the front and rear steel coils; superscript c denotes batch switching; the upper mark w represents the waiting time between two adjacent processes;

the upper mark d represents the steel coil delivery time; the upper mark q represents the upper limit or the lower limit of the number of steel coils in a collection batch preset by a collection batch process; superscript s denotes inventory; p represents: the total number of production lines p; n represents: the total number n of steel coils;

representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment is generated on equipment processing preference of the steel coil i; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the steel coil i generates jump punishment due to the thickness or width change of the steel coil; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment of the steel coil i is generated due to batch collection and switching; / >Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the waiting time between the two adjacent processes is larger than or smaller than the punishment of the preset time;

x _kij representing: on the kth production line, whether the steel coil i is processed immediately before the steel coil j;representing:

punishment generated by delayed delivery of the steel coil i;representing: the steel coil i does not meet punishment generated by the upper limit or the lower limit of the quantity preset by the batch collecting process; />Representing: the stock of the steel coil i does not meet the punishment generated by the preset safety stock or the maximum stock;

constraint conditions:

wherein the decision variable x _kij Indicating whether coil i is being processed immediately before coil j on the kth line; when coil i is processed immediately before coil j, then decision variable x _kij The value is 1, when the steel coil i is not processed immediately before the steel coil j, the decision variable x is determined _kij Is 0; q represents the number of collection batches; s represents the stock quantity;indicating the start time of coil j in process k, < >>The finishing processing time of the steel coil i in the process k is represented;

indicating the end processing time of coil i in immediately preceding step k-1, < >>The processing starting time of the steel coil i in the following procedure k is shown;

the step M3 includes: optimizing the candidate set batch scheduling plan, taking the minimum total penalty as a target, and carrying out cyclic iteration adjustment by utilizing heuristic neighborhood search and heuristic set batch transformation until the total penalty is no longer reduced;

the heuristic batch transformation prevents the result from being trapped in a change by increasing the number of candidate scheduling plans locally optimally;

the step M3.1 includes:

the step M3.2 includes:

2. A method for producing steel, characterized in that the final batch schedule plan with minimum total punishment is obtained by using the batch schedule optimizing method of the multi-production line of the cold rolling area of the steel enterprise according to claim 1.

3. The utility model provides a collection batch scheduling optimizing system of cold rolling district prolific production line of iron and steel enterprise which characterized in that includes:

module M4: selecting the final batch scheduling plan with the minimum optimization total penalty;

the module M1 includes:

Module M1.2: after sequencing according to the sequence of delivery time, finding the position of the first steel coil according to the steel coils with the same batch number, and forwardly adjusting the steel coils arranged at the back according to the preset processing upper limit number of each batch process to be concentrated into one batch for processing;

the module M2 includes:

Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, punishment is generated on equipment processing preference of the steel coil i; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the steel coil i generates jump punishment due to the thickness or width change of the steel coil; />Representing: in the kth production line, when the steel coil i is processed immediately before the steel coil j, the steelPunishment of volume i due to batch switching; />Representing: on the kth production line, when the steel coil i is processed immediately before the steel coil j, the waiting time between the two adjacent processes is larger than or smaller than the punishment of the preset time;

constraint conditions:

the module M3 includes: optimizing the candidate set batch scheduling plan, taking the minimum total penalty as a target, and carrying out cyclic iteration adjustment by utilizing heuristic neighborhood search and heuristic set batch transformation until the total penalty is no longer reduced;

the module M3.1 comprises:

the module M3.2 comprises: