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Distributed workshop scheduling optimization method and system with orders and robot carrying

A technology for workshop scheduling and optimization methods, applied in manufacturing computing systems, design optimization/simulation, instruments, etc., can solve the problems of less application of algorithms and no effective solutions for distributed flow scheduling of orders, etc., to achieve enhanced search capabilities , Minimize the maximum completion time, improve the effect of factory throughput

Active 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

[0005] There are very few studies on the scheduling problem with order and robot constraints in the distributed permutation flow scheduling problem DPFSP, and the application of algorithms to solve this problem is rare: (1) In the intelligent research, the workshop scheduling problem mostly considers the production of a single factory , there is no effective solution for the scheduling of distributed pipeline operations involving orders; (2) In actual production, in the research on the intelligent scheduling of robots in distributed pipeline operations, few literatures involve the issue of allocation order, and also consider Blocking constraints between machines

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  • Distributed workshop scheduling optimization method and system with orders and robot carrying
  • Distributed workshop scheduling optimization method and system with orders and robot carrying
  • Distributed workshop scheduling optimization method and system with orders and robot carrying

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Embodiment 1

[0034] In the technical solutions disclosed in one or more embodiments, such as figure 1 As shown, the distributed workshop scheduling optimization method with orders and robot handling includes the following steps:

[0035] Step 1. Determine the problem description of the distributed flow workshop with order and robot constraints, take minimizing the maximum completion time as the control goal, and establish a problem model according to the problem description;

[0036] Step 2. Use the improved iterative greedy algorithm (IIG) to solve the problem model of distributed flow shop optimization with constraints and robot constraints, and use the simulated annealing (SA) algorithm to jump out of the local optimal solution during the solving process to obtain the optimal solution plan.

[0037] The above steps are described in detail below:

[0038] 1. Problem description of distributed flow shop with order and robot constraints.

[0039] The Distributed Permutation-Flow Schedul...

Embodiment 2

[0126] This embodiment provides a distributed workshop scheduling optimization system with orders and robot handling, including:

[0127] Model building module: configured to determine the problem description of the distributed flow workshop with order and robot constraints, with the control goal of minimizing the maximum completion time, and building a problem model according to the problem description;

[0128] Solving module: configured to use the improved iterative greedy algorithm to solve the problem model of distributed flow shop optimization with constraints and robot constraints, and use the simulated annealing algorithm to jump out of the local optimal solution during the solving process to obtain the optimal solution .

Embodiment 3

[0130]This embodiment provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and executed 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 distributed workshop scheduling optimization method and system with orders and robot handling, solves the problem of a distributed flow shop with order and robot constraints by adopting an improved iterative greedy (IIG) algorithm, and aims at destroying and constructing a strategy for scheduling specific problems with orders and robots. In the proposed algorithm, firstly,three order allocation modes are proposed in initialization, and four types of neighborhood structures are developed in the algorithm. Then embedding a simulated annealing (SA) algorithm into the proposed iterative greedy algorithm to enhance the search capability, and order allocation and robot constraint optimal scheduling is considered in a distributed flow shop at the same time, thereby minimizing the maximum completion time, improving the throughput of a factory and reducing the labor cost.

Description

technical field [0001] The present disclosure relates to the technical field related to artificial intelligence, specifically, to a distributed workshop scheduling optimization method and system with orders and robot handling. 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] In recent years, intelligent manufacturing has brought new innovations to the machinery industry. The representative of intelligent manufacturing in actual production is intelligent factory. Because the manufacturing process characteristics of different products are different, the production planning of products often needs to be oriented to multiple manufacturing processes. Many literatures have studied job processing and distributed stream processing, and developed many effective algorithms. Many people have studied various optimization problems of distributed flo...

Claims

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Application Information

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IPC IPC(8): G06F30/20G06Q10/06G06Q50/04G06F111/04
CPCG06Q10/0631G06Q50/04Y02P90/30
Inventor 李文涵李俊青
Owner SHANDONG NORMAL UNIV