Hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method

A hybrid genetic algorithm and production planning technology, applied in the direction of comprehensive factory control, instrumentation, and comprehensive factory control, can solve the problems of inefficient MES production scheduling method, enhance local search ability, avoid falling into premature convergence, and maintain population diversity sexual effect

Inactive Publication Date: 2016-06-01
WUHAN KAIMU INFORMATION TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the embodiment of the present invention is to provide a method for MES production planning and schedul

Method used

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  • Hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method
  • Hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method
  • Hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method

Examples

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Example Embodiment

[0058] Embodiment one

[0059] like figure 1 Shown is a MES production planning and scheduling method based on a hybrid genetic algorithm provided by the present invention, there are one or more orders in the MES production planning and scheduling, and there are one or more execution resources in the workshop, the Methods include:

[0060] In step 201, according to the generation of order priority (OS initialization based on order priority, abbreviated as: OSIOP) method and / or random generation (Random OS initialization, abbreviated as: ROSI) method, generate a preset number of initial production scheduling schemes that meet the constraints between tasks Workshop task sequences for relational and genetic algorithm coding rules;

[0061] In step 202, according to the task optimal start and end time allocation (resource selection according to appropriate planned time, abbreviated as: RSAPT), according to resource load balancing principle allocation (resource selection for work...

Example Embodiment

[0072] Embodiment two

[0073] The embodiment of the present invention further elaborates on the technical features involved in step 201 in the first embodiment, and gives specific examples of its implementation, wherein, according to the generation method of the order priority described in step 201, the generated Inter-task constraint relations and workshop task sequences of genetic algorithm encoding rules, such as figure 2 shown, including:

[0074] Step 301: arrange the orders from high to low priority, and arrange the front and back positions of orders with the same priority in random order;

[0075] Step 302: Orders are sequentially selected from the sorted order sequence. If it is an order that is forward scheduled, go to step 303; if it is an order that is reverse scheduled, go to step 304.

[0076] Step 303, putting its subordinate workshop tasks into the workshop task sequence sequentially from the first task to the last task according to the succession relationsh...

Example Embodiment

[0080] Embodiment three

[0081] The embodiment of the present invention further elaborates on the technical features involved in step 201 in the first embodiment, and gives a specific example of its implementation, wherein, according to the random generation method described in step 201, a Constraint relationships and genetic algorithm coding rules for workshop task sequences, such as Figure 4 As shown, the specific implementation is:

[0082] Step 401: put the workshop tasks included in the order into the workshop task sequence in any order;

[0083] Step 402: Generate an optional shop floor task set Oa, select the shop floor tasks without previous tasks from the forward scheduled orders, and select the shop floor tasks without subsequent tasks from the unstarted reverse scheduled orders, and then select these Workshop tasks are put into Oc;

[0084] Step 403: Randomly select a workshop task Oc from Oa to be placed in the next position of the workshop task sequence, and th...

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Abstract

The invention is applicable to the technical field of workshop production planning management, and provides a hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method. According to an order priority generation mode and/or random generation mode, a workshop task sequence which meets constraint relations between tasks and a genetic algorithm coding rule is generated for a preset number of initial scheduling schemes; according to the best task starting and ending time and according to one or more distribution combination modes in resource load balancing principle distribution and random distribution, execution resources are set for each workshop task in the preset number of initial scheduling schemes; the preset number of initial scheduling schemes are converted into a series of chromosomes through a coding process to serve as an initial population for the hybrid genetic algorithm; and the initial population is introduced to the hybrid genetic algorithm, and a scheduling result after optimization is calculated according to a preset optimization target. High efficiency of the MES production planning and scheduling results in the prior art is improved.

Description

technical field [0001] The invention belongs to the technical field of workshop production planning management, in particular to a MES production planning and scheduling method based on a hybrid genetic algorithm. Background technique [0002] Manufacturing Execution Systems (Manufacturing Execution Systems, abbreviated as: MES) focuses on the characteristics of the execution and management of workshop manufacturing plans, and determines that the formulation and scheduling of detailed production plans is one of the most important core functions of MES. Through this process, MES receives the coarser overall production planning target of the upper-level production planning system, and generates specific to the smallest processing unit based on the process information in MES, workshop resources, and the specific conditions and constraints of existing work execution. Each detailed production operation plan, and the quality of the produced production plan will directly affect the...

Claims

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Application Information

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IPC IPC(8): G05B19/418
CPCY02P90/02G05B19/41865G05B2219/32091
Inventor 周力杨亚菲余章勇
Owner WUHAN KAIMU INFORMATION TECH
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