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Static Scheduling Optimization System for Discrete Manufacturing Workshop Based on Teaching and Learning Algorithm

A discrete manufacturing workshop, static scheduling technology, applied in control/regulation systems, general control systems, program control, etc., can solve problems such as unreasonable parts production arrangements, long machine tool standby time, etc., to achieve fast convergence speed and optimization. Strong ability and good effect

Inactive Publication Date: 2017-05-31
JIANGNAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

In the current production process of discrete manufacturing workshops, there are problems such as too long standby time of machine tools and unreasonable parts production arrangements. Therefore, static scheduling arrangements before workshop production are very necessary. Reasonable static scheduling arrangements are conducive to improving the energy efficiency of the manufacturing process, which is in line with the needs of enterprises. core interests of

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  • Static Scheduling Optimization System for Discrete Manufacturing Workshop Based on Teaching and Learning Algorithm
  • Static Scheduling Optimization System for Discrete Manufacturing Workshop Based on Teaching and Learning Algorithm
  • Static Scheduling Optimization System for Discrete Manufacturing Workshop Based on Teaching and Learning Algorithm

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Embodiment example

[0104] Step 1: Build a data server and communicate with the workshop machine tool system to obtain the processing time (including standby time) of a single process of a single workpiece processed by the machine tool dead time cutting time ) and processing energy consumption details (including standby energy consumption No-load energy consumption Cutting energy consumption ), where i represents the machine tool, j represents the workpiece, k represents the specific process, d represents standby, o represents no-load, and q represents cutting, so the time taken by machine tool i to process the kth process of workpiece j and energy consumption In the data server, the acquired data is analyzed and processed, and the energy consumption information is integrated:

[0105] The time for the machine tool to process workpiece j:

[0106] The energy consumption of the machine tool to process workpiece j:

[0107] Among them, t xy Indicates the time taken by the yth pr...

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Abstract

The invention discloses a teaching-and-learning-algorithm-based static scheduling optimization system for a discrete manufacturing shop. The system comprises a data server, an operation server, and a terminal display screen. A scheduling optimization client is embedded into the operation server and is used for carrying out optimization-algorithm-based reasonable scheduling arrangement on a processing task on the day by analyzing the processing task and consulting various detailed information of the data server, wherein the specific optimization algorithm employs a teaching and learning algorithm. A generation scheduling scheme is decoded; de-compilation processing is carried out according to dual-layer definition on a student during coding; a processing tool and a processing sequence of a workpiece are expressed successively. And then the optimized scheduling scheme is displayed on a terminal display screen, so that the production staff and the management staff in the shop can obtain current specific processing task arrangement information in real time. According to the invention, with the teaching and learning algorithm, the system has advantages of fast convergence speed and high optimization searching capability and adapts to the complex situation in practical production well.

Description

technical field [0001] The invention relates to the technical field of discrete manufacturing workshop scheduling, in particular to a static scheduling optimization system for discrete manufacturing workshops based on teaching and learning algorithms. Background technique [0002] In the 21st century, the competition in the manufacturing industry is becoming increasingly fierce. Enterprises are also facing threats and challenges while gaining great room for development. The factors that determine the core competitiveness of an enterprise are reflected in the products provided by the enterprise. High-quality products, low prices, and short delivery times are required, and they can continuously adapt to the changing market and individual customer needs, which puts forward higher requirements for the production plan and balanced production of the enterprise's manufacturing workshop. In the current production process of discrete manufacturing workshops, there are problems such ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/418
CPCG05B19/41865G05B2219/32252Y02P90/02
Inventor 王艳徐军辉纪志成陈彦
Owner JIANGNAN UNIV
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