Flexible workshop scheduling optimization method and system with robot transportation

A technology of workshop scheduling and optimization methods, applied in manufacturing computing systems, instruments, data processing applications, etc., can solve problems such as low applicability, slow convergence speed, time-consuming, etc., to minimize completion time, improve convergence, and improve production. The effect of efficiency

Pending Publication Date: 2020-07-10
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The inventor found that, on the one hand, most studies on FJSP assume that a workpiece is processed directly on another machine tool after it is completed on one machine tool, which obviously does not correspond to reality
On the other hand, the existing methods have the following difficulties in solving the FJSP problem: the artificial immune algorithm (AIA) has the disadvantage of slow convergence when solving the scheduling problem; the hybrid tabu search algorithm (HTSA) has the disadvantage of solving the scheduling problem It is easy to fall

Method used

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  • Flexible workshop scheduling optimization method and system with robot transportation
  • Flexible workshop scheduling optimization method and system with robot transportation
  • Flexible workshop scheduling optimization method and system with robot transportation

Examples

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Example Embodiment

[0035] Example 1

[0036] Among the technical solutions disclosed in one or more embodiments, such as figure 1 As shown, a flexible workshop scheduling optimization method with robot transportation includes the following steps:

[0037] Step 1. Obtain production configuration information, where the production configuration information includes the workpieces to be produced, the number of machine tools, and the production operations that each machine tool can perform;

[0038] Step 2. Determine the constraint conditions of the distributed flow shop with transmission and switching time, take minimizing the maximum completion time as the control goal, and establish the problem model according to the constraint conditions;

[0039] Step 3. According to the production configuration information, the imperial competition algorithm is used to solve the problem model, and the simulated annealing algorithm is used to obtain the optimal solution for the selection of equipment for each operation a...

Example Embodiment

[0117] Example 2

[0118] This embodiment provides a flexible workshop scheduling optimization system with robot transportation, including:

[0119] Obtaining module: configured to obtain production configuration information, the production configuration information including the workpieces to be produced, the number of machine tools, and the production operations that each machine tool can perform;

[0120] Problem model establishment module: It is configured to determine the constraint conditions of the distributed flow shop with transmission and switching time, with the control goal of minimizing the maximum completion time, and the problem model is established according to the constraint conditions;

[0121] Solving module: It is configured to solve the problem model by using the imperial competition algorithm according to the production configuration information, and using the simulated annealing algorithm to obtain the optimal solution for the selection of equipment for each oper...

Example Embodiment

[0122] Example 3

[0123] This embodiment provides an electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are executed by the processor, the steps described in the method in Embodiment 1 are completed.

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Abstract

The invention provides a flexible workshop scheduling optimization method and system with robot transportation. The transportation time and the switching time are considered; a problem model established by corresponding constraints is proposed; an empire competition algorithm is adopted to solve the problem, and a simulated annealing (S A) algorithm is adopted in the solving process to jump out ofa local optimal solution, so that the convergence of the algorithm is improved, a better optimal solution is obtained and serves as an optimal solution for equipment selection of each operation and an operation sequence on each piece of equipment, the completion time is minimized, and the production efficiency is improved.

Description

technical field [0001] The present disclosure relates to the technical field related to intelligent production scheduling, and specifically relates to a method and system for optimizing flexible workshop scheduling with robot transportation. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Scheduling problems have received extensive attention in recent years. The classic scheduling problems include Flow Job Scheduling Problem (FSP), Job Shop Scheduling Problem (JSP) and Flexible Job Shop Scheduling Problem (FJSP). The flexible job shop problem is one of the hardest problems in this field, scheduling a set of jobs through a set of machine tools in order to minimize the creation of performance criteria. Each workpiece consists of a series of consecutive operations, each of which requires only one machine tool, which is continuously availa...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/04
CPCG06Q10/0631G06Q50/04Y02P90/30
Inventor 于辉李俊青
Owner SHANDONG NORMAL UNIV
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