CN115309111A - Resource-limited distributed hybrid flow shop scheduling method and system - Google Patents
Resource-limited distributed hybrid flow shop scheduling method and system Download PDFInfo
- Publication number
- CN115309111A CN115309111A CN202111522573.7A CN202111522573A CN115309111A CN 115309111 A CN115309111 A CN 115309111A CN 202111522573 A CN202111522573 A CN 202111522573A CN 115309111 A CN115309111 A CN 115309111A
- Authority
- CN
- China
- Prior art keywords
- resource
- machine
- flow shop
- workpiece
- processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims description 39
- 238000012545 processing Methods 0.000 claims abstract description 50
- 238000005265 energy consumption Methods 0.000 claims abstract description 35
- 238000004519 manufacturing process Methods 0.000 claims abstract description 18
- 238000013178 mathematical model Methods 0.000 claims abstract description 7
- 238000010586 diagram Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 238000009826 distribution Methods 0.000 claims description 4
- 230000003111 delayed effect Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 abstract description 5
- 101001062338 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) Dihydrofolate synthetase Proteins 0.000 abstract description 3
- 101000892866 Schizosaccharomyces pombe (strain 972 / ATCC 24843) Probable dihydrofolate synthetase Proteins 0.000 abstract description 3
- 238000003754 machining Methods 0.000 description 7
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 230000001934 delay Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000009776 industrial production Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000009749 continuous casting Methods 0.000 description 1
- AEUTYOVWOVBAKS-UWVGGRQHSA-N ethambutol Natural products CC[C@@H](CO)NCCN[C@@H](CC)CO AEUTYOVWOVBAKS-UWVGGRQHSA-N 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009628 steelmaking Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32339—Object oriented modeling, design, analysis, implementation, simulation language
Landscapes
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The distributed hybrid flow shop scheduling problem (DHFSP) has been extensively studied in recent years due to the needs of practical industrial applications. However, resource constraints in actual production are rarely mentioned in DHFS. The distributed hybrid flow shop scheduling problem (RCDHFSP) with resource limitation constraint is researched by combining the characteristics of parallel machines and factory scheduling, and the aim of minimizing energy consumption and maximizing completion time is achieved. In this problem, each job needs to preempt limited resources in order to be processed at each plant, and some jobs delay processing when resources are insufficient. Therefore, to solve this NP-hard problem, a mixed integer linear programming model is proposed. CPLEX is used to obtain the most suitable solution due to the high complexity of RCDHFS in the model. The results of CPLEX calculation show that the proposed mathematical model has obvious feasibility on RCDHFS.
Description
Technical Field
The invention belongs to the field of production scheduling, and particularly relates to a resource-limited distributed hybrid flow shop scheduling method and system.
Technical Field
The distributed hybrid flow shop scheduling problem (DHFSP) is an extension of the traditional hybrid flow shop scheduling problem (HFSP). As HFSP has the characteristics of multiple processes and multiple stages and can meet the actual production requirements of the manufacturing industry, rubnRuiz et al have conducted intensive research on HFSP [1]. Furthermore, with the development of global economy, production has shifted from a single mode to multiple plants to improve efficiency. Thus, ying and Lin et al have studied DHFSP [2]. However, in actual industrial production, various dynamic situations, such as worker restriction and resource limitation, occur. Under the current market competition environment, it is necessary to research the resource-limited scheduling problem of the distributed hybrid flow shop. The problem of minimizing the completion time and energy consumption of the circulating fluidized bed is of great predictability and practical significance.
In recent years, distributed has become one of the main aspects of manufacturing research. Weng et al consider distributed computing based on dynamic routing policies to minimize just-in-time (JIT) [3]. Then, k. -c.ying et al first solved DHFSP with multiprocessor tasks and extended the field of distributed scheduling problem [4]. Hao et al further investigated DHFSP and proposed an algorithm to improve efficiency [5]. Cai et al [6] considered DHFSP with sequence dependent set times and published a new strategy to match this problem [7]. Shao et al continue to discuss DHFSP [8] in view of scheduling among multiple production centers. Cai et al studied fuzzy distributed scheduling [9] since complex uncertainty problems in production are often ignored. Based on fuzzy distributed scheduling with setup time (setup time), the fuzzy DHSP multi-objective approach proposed by Zheng et al [10 ]. Today, distribution still has significant research value.
All enterprises around the world face resource limitations, and work in many production modes requires resources, such as automated guided vehicles, machine operators [11 ]]. Workshop scheduling based on tasks per unit time, sural H et al propose renewable resource limitation [12]. In order to make economical use of the resources,leu et al studied a resource-constrained hybrid production flow shop scheduling system [13]. From a public redevelopment project perspective, T.C.E.Cheng et al have a workshop scheduling problem [14]Wherein resource constraints are taken into account by searching for feasible re-development sequences. However, EWA Figielska takes precedence into account when solving the resource limitation problem [15]. W. -C.Yeh et al studied resource consumption constraints in uniform parallel machine scheduling [16]. The tool resource constraints are constrained by a.c.
al.[17]And (5) realizing. Luis et al [18 ]]And solving the resource problem in the scheduling of the parallel machine by adopting a model and a meta-heuristic method. Based on steel-making schedules, li et al put forward resource constraints and continuous casting constraints to satisfy production [19 ]]. Further, X-r. Tao et al combine the hybrid flow shop scheduling problem with the resource constraint problem [20]And so on.
Energy consumption has attracted more attention in the large industry due to the increase in global carbon emissions [21]. Dai et al consider the impact of energy consumption on the environment in flexible flow shop scheduling (FFS) [22]. Zhang et al aim to provide a manufacturing schedule to minimize the carbon footprint and electricity costs [23]. Both y.he et al and Jiang et al propose scheduling strategies [24] - [25] for improving energy efficiency. The two-stage random variable flow shop scheduling problem is addressed by a.fazli Khalaf and Wang in terms of total electricity purchase cost [26 ]. However, there is little mention in DHFS of the resources in actual production.
A resource-limited distributed hybrid flow shop scheduling method and system (RCDHFSP) are researched by combining the characteristics of parallel machines and factory scheduling, and the aim of minimizing energy consumption and maximizing completion time is achieved. In this problem, each job needs to preempt limited resources in order to be processed at each plant, and some jobs delay processing when resources are insufficient. Therefore, to solve this NP-hard problem, a mixed integer linear programming model is proposed. Since the body of the RCDHFS is complex in the model, CPLEX is used to obtain the most suitable solution. The CPLEX calculation result shows that the proposed mathematical model has obvious feasibility on RCDHFS
Disclosure of Invention
The invention aims to solve the problems in the prior art, provides a resource-limited distributed hybrid flow shop scheduling method and system, fully proves the feasibility of the problem through a model, and further elaborates the description of the resource-limited hybrid flow shop scheduling problem. The invention is realized by the following technical scheme:
a resource-limited distributed hybrid flow shop scheduling method and system includes:
s1, researching the scheduling problem of the distributed mixed flow shop, and simultaneously considering the requirement that the workpiece processing is limited by resources;
s2: determining an optimized target and constraint conditions;
s3: establishing a corresponding mathematical model;
s4: the correctness of the proposed mathematical model for solving the problem of the scheduling of the distributed hybrid flow shop with resource limitation constraint;
in the scheduling problem of the S1 resource-limited distributed hybrid flow shop, n workpieces are distributed in F identical factories to be processed, and each workpiece needs to be processed in S processing stages. Each factory has m machines, each machine can only process the work piece working procedure of the corresponding stage, and at least one stage has a plurality of parallel machines for processing. With the fixed and known processing time for each job on a machine, each machine has energy consumption when processing different workpieces, and waiting energy consumption occurs when the machine waits for a workpiece to be processed, and the unit energy consumption and the unit waiting energy consumption of the workpiece on the machine are also predefined. If there are no machines free, the next workpiece needs to wait and an infinite amount of buffer space can be reserved before the machine completes the processing of the current workpiece. Each workpiece must be assigned to only one factory and all stages of processing must be completed at the assigned factory. Therefore, the factory must not be replaced after each process. Furthermore, each workpiece and machine is feasible at time zero, and no preemption is allowed during processing. Furthermore, the machine can only process one workpiece at a time, and the workpiece can only be processed by one machine at a stage. Resource constrained means that each plant has h reusable resources. The total resource type and the number of resources are defined in advance. All workpiece processing requires preemption of the required resources before processing on the machine and the required resources can be described in advance. Once resources are scarce, the job needs to be delayed. When in use, all resources are not allowed to be occupied, and the release is immediately carried out after the processing is finished.
The objective function in S2 is:
min C max
C max a continuous variable representing the maximum completion time of the workpiece.
The S3 is established by:
the resource constrained distributed hybrid flow shop model is as follows.
The parameters and symbols used in the distributed hybrid flow shop scheduling optimization method model based on the resource constraint are described as follows:
the constraints used in the model are as follows:
the S4 is realized by the following steps:
to verify the correctness of the proposed mixed integer linear programming model to further illustrate the resource constrained hybrid flow shop scheduling optimization problem, we obtained the results of twenty small-scale algorithms using the IBM ILOG CPLEX 12.6 exact solution software, and all of the algorithms were related to actual production. We set the CPU limit time to 1. Each example was run for 1.5 hours with a maximum number of passes of 3. The generated data are tested, the correctness of the model is illustrated through a Gantt chart, the data distribution and the data concentration condition of the model can be seen through a scatter diagram, and the method has strong practicability in an actual production system.
Drawings
FIG. 1: machine-processed gantt chart for resource constrained problem description
FIG. 2: gantt chart of resource occupation situation
FIG. 3: example energy efficiency scatter plot
FIG. 4: gantt graph for solving problem of resource-constrained hybrid flow shop by CPLEX
FIG. 5 is a schematic view of: CPLEX solution example result box diagram
FIG. 6: energy efficiency box diagram of example
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
in view of the need for practical industrial applications, the distributed hybrid flow shop scheduling problem (DHFSP) has been extensively studied in recent years. However, there is little mention in DHFS of resource constraints in actual production. The distributed hybrid flow shop scheduling problem (RCDHFSP) with resource limitation constraint is researched by combining the characteristics of parallel machines and factory scheduling, and the aim of minimizing energy consumption and maximum completion time is fulfilled. In this problem, each job needs to preempt limited resources to be processed at each plant, and some jobs delay processing when resources are insufficient. Therefore, to solve this NP-hard problem, a mixed integer linear programming model is proposed. CPLEX is used to obtain the most suitable solution due to the high complexity of RCDHFS in the model. The results of CPLEX calculation show that the proposed mathematical model has obvious feasibility on RCDHFS.
1. Resource-constrained distributed hybrid flow shop scheduling method and system description
There are n workpieces distributed for processing in F identical factories, each workpiece requiring s processing stages. Each factory has m machines, each machine can only process the work piece working procedure of the corresponding stage, and at least one stage has a plurality of parallel machines for processing. In the case where the processing time for each job on the machine is fixed and known, each machine has energy consumption when processing different workpieces, and there is a wait for energy consumption to occur when the machine waits for the workpieces to be processed, and the unit energy consumption and the unit wait energy consumption of the workpieces on the machine are also predefined. If there are no machines free, the next workpiece needs to wait and an infinite amount of buffer space can be reserved before the machine completes the processing of the current workpiece. Each workpiece must be assigned to only one factory and all stages of processing must be completed at the assigned factory. Therefore, the factory must not be replaced after each process. Furthermore, each workpiece and machine is feasible at time zero, and no preemption is allowed during processing. Furthermore, the machine can only process one workpiece at a time, and the workpiece can only be processed by one machine at a stage. Resource constrained means that each plant has h reusable resources. The total resource type and the number of resources are defined in advance. All workpiece processing requires preemption of the required resources before processing on the machine and the required resources can be described in advance. Once resources are scarce, the job needs to be delayed. When in use, the method is not allowed to occupy all resources, and is released immediately after the processing is finished.
1.1 resource-constrained distributed hybrid flow shop scheduling method and system modeling
The parameters and symbols used in the distributed hybrid flow shop scheduling optimization method model based on the resource constraint are described as follows:
the constraints used in the model are as follows:
the objective function is to minimize the completion time and the total energy consumption. Constraint (1) indicates how to calculate the time for the workpiece to finish machining. Constraining (2) workpieces located forward in the sequence of positions to be machined earlier than workpieces located aft in the sequence of positions. Constraint (3) indicates that the workpiece must complete the machining of the pre-stage before machining the current stage. Constraints (4) - (5) indicate that the machine dependent time must be associated with workpiece j of priority pr on machine i. The constraint (6) represents the relation between the workpiece start machining time and the machine start time. Constraints (7) define that the operation of each stage of the workpiece can only select one machine. Constraints (8) indicate that only one workpiece can be machined per machine position. Constraints (9) - (10) indicate that all stages of machining of one workpiece can be performed only in one factory. Constraints (11) indicate that at least one workpiece is to be processed per factory. Constraints (12) - (14) indicate that the amount of resources required for a process cannot exceed the total amount of such resources. Constraints (15) - (17) illustrate the relationship between resource usage time and workpiece processing time, and the correspondence between machines and resources. Constraints (18) account for total energy consumption including total standby energy consumption and total process energy consumption. Constraints (19) define the values of the decision variables.
2A resource-constrained distributed hybrid flow shop scheduling method and system further illustrate the problem description by way of example
We further illustrate this by way of an example. Five jobs are distributed in two plants, each with three identical machines, two parallel machines processing in stage 1 and one machine processing in stage 2. Table 1 summarizes each job having a different processing time, and table 2 defines the energy consumption per unit time for the different machines. The last column in table 2 is the standby energy consumption of the machine. Table 3 shows the resource consumption for processing each job. In Table 3, Y indicates that the job requires this type of resource for processing, and N indicates the opposite. The total resource type is set to 5, and the total number of resources of each type is 1.
Fig. 1 shows a solution to the machine gantt chart described above, where J3, J4 are assigned to plant 1 and J1, J2, J5 are assigned to plant 2. We assume that the optimal arrangement of operations in plant 1 is: j3, J4, the best arrangement of operations in plant 2 is: j5, J2, J1. The information in the middle of a job indicates the number of jobs and the stage of the job. It is clear that in plant 1, J3 completing processing at M1 stage 1 can go to stage 2 before J4 processing on M3 because the machine was idle at time 4 due to no resource conflict and complicated stage 1 before J4. We can see that the jobs in plant 1 can be processed in the conventional distributed hybrid flow shop scheduling order. However, jobs in plant 2 cause processing delays due to lack of resources, e.g., J2 until time 6 because J5 uses resource R2 before J2 processing, R5 uses before J2 processing, and J1 uses before time 16. Subsequently, J2 preempts the resource before J1, and J2 starts processing before J1. All workpieces need to be processed in a phased sequence. Thus, the makespan for this example is 20.
In this example, energy consumption is in joules and time is in seconds. The plant 1 comprises two jobs, since there is no wait in the process, i.e. no wait energy consumption. The machining energy of J3 is 4 × 24 × 3=20, and the machining energy of the workpiece 4 is 1 × 75 × 9=52; plant 2 needs to handle three jobs: the standby time of machine 1 is 6, the processing time is 3, and the energy consumption of M1 is 6 × 0.7+3 × 4= 16.2. The standby time of M2 is 14; the time for processing J5 and J1 is 2, and the energy consumption of M2 is 14 × 0.8+2 × 2=19.2. M3 has standby time 6, and the processing time J5, J2 and J1 are respectively 4, 6 and 2; the energy consumption of M3 is 6 × 0.9+4 × 3+6 × 4+2 × 3=47.4. Therefore, the total energy consumption is 20+52+16.2+19.2+47.4=154.8.
Fig. 2 depicts each type of resource used at a point in time. In plant 1, J3 occupies R1, R2, R3 resources until time 8, and J4 and J5 can be processed simultaneously since there is no conflict with J4 using R4 and R5. However, in plant 2, J2 gets R2 and R5 until J5 releases resources at time 6, which may explain why J2 delays processing due to insufficient resources. J1 starts the process at time 16 in the same manner.
TABLE 1 processing time
TABLE 2 energy consumption
TABLE 3 resource consumption
3. Results and analysis of the experiments
To verify the correctness of the proposed mixed integer linear programming model and further illustrate the RCDHFSP, an IBM ILOG CPLEX 12.6 precision solver was used to obtain twenty small-scale example results. All examples are based on actual industrial production. We set the CPU limit time to 1. The maximum number of threads in the IBM ILOG CPLEX 12.6 is 3 for 1.5 hours per example.
Table 4 is the CPLEX solver results for 20 examples. The first column represents the instance number. The second column gives the scale of each example (where 5-4-2-2 indicates that the example achieved five jobs, four machines and two stages per plant, two plants). The third column shows the maximum completion time and the fourth column shows the total energy, both representing each example.
Table 4.CPLEX example solving results
FIG. 3 shows the energy efficiency, energy efficiency eta, of each example i Can be calculated as follows.
η i ＝TE i /C i (20)
In formula (20) C i And TE i Respectively representing the minimum completion time and the minimum total energy consumption, eta, of each of the examples i i And the energy efficiency target value corresponding to the energy consumption in unit time of each example is shown.
Fig. 4 shows a machined gantt chart of the solution of the calculation example 15 (8-4-2-3), which shows 8 jobs and 4 machines at two stages in two factories, requiring 5 resources. FIG. 5 is a box diagram of the maximum completion time and total energy consumption profiles for all algorithms. Fig. 6 shows the energy efficiency value distribution box diagram calculated according to the formula (20) for all the calculation results.
Claims (5)
1. A resource-limited distributed hybrid flow shop scheduling method and system are characterized in that: the method comprises the following steps:
s1, researching the scheduling problem of the distributed mixed flow shop, and simultaneously considering the requirement that the workpiece processing is limited by resources;
s2: determining an optimized target and constraint conditions;
s3: establishing a corresponding mathematical model;
s4: the proposed mathematical model is correct for solving the problem of distributed hybrid flow shop scheduling with resource-constrained constraints.
2. The resource-constrained distributed hybrid flow shop scheduling method and system according to claim 1, wherein:
in the scheduling problem of the distributed mixed flow shop with the resource limited in the S1, n workpieces are distributed in F identical factories to be processed, each workpiece needs to be processed in S processing stages, each factory is provided with m machines, each machine can only process the workpiece procedures of the corresponding stage, and at least one stage is provided with a plurality of parallel machines for processing. Under the condition that the processing time of each operation on the machine is fixed and known, when different workpieces are processed, each machine has energy consumption, when the machine waits for the workpieces to be processed, waiting energy consumption is generated, unit energy consumption and unit waiting energy consumption of the workpieces on the machine are also predefined, and if no machine is idle, the next workpiece needs to wait and can reserve a buffer space with infinite capacity before the machine finishes processing the current workpiece; each workpiece must be assigned to only one factory and all stages of processing must be completed at the assigned factory. Therefore, the factory must not be replaced after each process; furthermore, each workpiece and machine is feasible at time zero, not allowing preemption while processing; furthermore, the machine can only process one workpiece at a time, and the workpiece can only be processed by one machine at a stage. Resource limitation means that each plant has h reusable resources and the total resource type and the resource quantity are defined in advance; all workpieces need to preempt required resources before being processed on a machine, the required resources can be described in advance, once the resources are insufficient, the operation needs to be delayed, all the resources are not allowed to be occupied during use, and the resources are released immediately after processing is finished.
3. The method and system for resource-constrained distributed hybrid flow shop scheduling as claimed in claim 2, wherein: the objective function in S2 is:
min(C max +TE) (1)
C max a continuous variable representing the maximum completion time of the workpiece,
TE represents the total energy consumption for all workpieces to complete the process.
4. The resource-constrained distributed hybrid flow shop scheduling method and system according to claim 3, wherein: the S3 is established by:
the resource-constrained distributed hybrid flow shop model is as follows:
the parameters and symbols used in the distributed hybrid flow shop scheduling optimization method model based on the resource constraint are described as follows:
the constraints used in the model are as follows:
5. the method and system for resource-constrained distributed hybrid flow shop scheduling as claimed in claim 6, wherein: the S4 is realized by the following steps:
in order to verify the correctness of the proposed mixed integer linear programming model and further explain the scheduling optimization problem of the mixed flow shop with resource limitation constraint, the IBM ILOG CPLEX 12.6 precise solution software is used for obtaining the results of twenty small-scale operators, all the operators are related to actual production, the CPU limitation time is set to be 1, each operator runs for 1.5 hours, the maximum number of threads is 3, the generated data is tested, the correctness of the model is illustrated through a Gantt chart, the data distribution and the data concentration condition of the proposed model can be seen through a scatter diagram, and the mixed integer linear programming model has strong practicability in an actual production system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111522573.7A CN115309111A (en) | 2021-12-13 | 2021-12-13 | Resource-limited distributed hybrid flow shop scheduling method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111522573.7A CN115309111A (en) | 2021-12-13 | 2021-12-13 | Resource-limited distributed hybrid flow shop scheduling method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115309111A true CN115309111A (en) | 2022-11-08 |
Family
ID=83854109
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111522573.7A Pending CN115309111A (en) | 2021-12-13 | 2021-12-13 | Resource-limited distributed hybrid flow shop scheduling method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115309111A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116795054A (en) * | 2023-06-19 | 2023-09-22 | 上海交通大学 | Intermediate product scheduling method in discrete manufacturing mode |
| CN117371769A (en) * | 2023-12-08 | 2024-01-09 | 聊城大学 | Scheduling acceleration evaluation method for distributed blocking flow shop |
-
2021
- 2021-12-13 CN CN202111522573.7A patent/CN115309111A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116795054A (en) * | 2023-06-19 | 2023-09-22 | 上海交通大学 | Intermediate product scheduling method in discrete manufacturing mode |
| CN116795054B (en) * | 2023-06-19 | 2024-03-19 | 上海交通大学 | Intermediate product scheduling method in discrete manufacturing mode |
| CN117371769A (en) * | 2023-12-08 | 2024-01-09 | 聊城大学 | Scheduling acceleration evaluation method for distributed blocking flow shop |
| CN117371769B (en) * | 2023-12-08 | 2024-03-12 | 聊城大学 | Scheduling acceleration evaluation method for distributed blocking flow shop |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Deja et al. | Feature-based generation of machining process plans for optimised parts manufacture | |
| CN115309111A (en) | Resource-limited distributed hybrid flow shop scheduling method and system | |
| Kumar et al. | Integration of process planning and scheduling in a job shop environment | |
| Ivanov et al. | Technology for complex parts machining in multiproduct manufacturing | |
| Gargeya et al. | Scheduling research in multiple resource constrained job shops: a review and critique | |
| Usher et al. | Dynamic process planning—the static phase | |
| Giusti et al. | KAPLAN: a knowledge-based approach to process planning of rotational parts | |
| CN110597210A (en) | Flexible job shop scheduling energy-saving optimization method considering equipment pre-maintenance | |
| Kongchuenjai et al. | An integer programming approach for process planning for mixed-model parts manufacturing on a CNC machining center | |
| Ivanov et al. | Increasing productivity of connecting rods machining | |
| Wang et al. | ASP: an adaptive setup planning approach for dynamic machine assignments | |
| Liu et al. | Optimization of line configuration and balancing for flexible machining lines | |
| Ji et al. | Big data analytics based optimisation for enriched process planning: a methodology | |
| Shimada et al. | Optimization of Cutting Tool Allocation to Enhance Workload Balance and Total Completion Time in Parallel-Type FMS | |
| Bhale et al. | Grouping and sequencing of machining operations for high volume transfer lines | |
| CN116736819B (en) | FJSP-AGV modeling method based on mixed integer programming model | |
| Sinriech et al. | Sequencing, scheduling and tooling single-stage multifunctional machines in a small batch environment | |
| CN116736820B (en) | Modeling method considering integrated scheduling of processing, transportation and assembly | |
| Agarwal et al. | Flowshop Scheduling Problem for 10-Jobs, 10-Machines By Heuristics Models Using Makespan Criterion | |
| Battaïa et al. | Heuristics for Batch Machining at Reconfigurable Rotary Transfer Machines | |
| Yonemoto et al. | Reactive scheduling based on adaptive manipulator operations in a job shop configuration with two machines | |
| Ståhl | The development of NEXT STEP beyond Lean Production-The link between technology and economics with focus on sustainable developments | |
| Denkena et al. | Adaptive process chain optimisation of manufacturing systems | |
| DR | Optimization In A Casing Manufacturing Industry Using Simulation Study | |
| Al Aqel et al. | A New Priority-Sort Based Optimization Algorithm for Integrated Process Planning and Scheduling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |