Sequencing for Improving Mixed-Model Assembly Line Supply Using Heuristic Algorithms

被引：0

作者：

Dziki, Karol ^{[1
]}

Krenczyk, Damian ^{[1
]}

机构：

[1] Silesian Tech Univ, Fac Mech Engn, Gliwice, Poland

来源：

16TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2021) | 2022年 / 1401卷

关键词：

Mixed-model; Assembly line; Part feeding; Heuristic algorithms;

D O I：

10.1007/978-3-030-87869-6_74

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work presents a method used to determine sequencing and distribution of parts on mixed-model assembly line with the use of autonomous carts. The aim is to minimise of the number of means of transport, minimizing the idle time of carts and specific restrictions generated by the described intralogistics model. Simulated annealing was used in conjunction with the developed method of constrains prioritization to determine the parts distribution method. The mathematical model, proposed method and the solution procedure are demonstrated using an example set of mixed-model data. Two solutions for the intralogistics system using for example autonomous vehicles have been compared with the result of the simulation experiment.

引用

页码：777 / 788

页数：12

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