A Decision-Making Sequencing Model of a Mixed-Model Assembly Line Considering the Leveling of Material and Assembly Switch Cost

Sequence model is one of the most important decision of production plan and control for mixed-model assembly line. This paper firstly according to customs’ demand and lead time divides the products into four categories: core strategic products I, customer-oriented products II, time-oriented products III, ordinary products IV. And then two principles (customers’ demand and material balance) are proposed for every category of product. By using the principle of MPS, two-objective with minimum variations of part usage rate and minimum assembly switch cost sequencing model is given. Finally, the optimal solution set of every categories in a MPS are solved with genetic algorithm (GA), so that decision-making method is clear offered on mixed-model assembly line sequencing problem when with different preference and in structure characteristics of demand. The results show that two-objectives sequencing model above outperforms good shown as four parameters with work-in-process inventory, rate of material balance, assembly switch cost, lead time, etc.

• Record URL:
  https://doi.org/10.1061/9780784479384.301
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784479384
• Supplemental Notes:
  • © 2015 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Long, Shuling
  • Sun, Baofeng
  • Lu, Zhaoyu
  • Zhang, Shoulong
• Conference:
  • Fifth International Conference on Transportation Engineering
  • Location: Dalian , China
  • Date: 2015-9-26 to 2015-9-27
• Publication Date: 2015-9

Language

• English

Subject/Index Terms

• TRT Terms: Assembly lines; Costs; Decision making; Demand; Genetic algorithms
• Subject Areas: Planning and Forecasting; Transportation (General); I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01579181
• Record Type: Publication
• ISBN: 9780784479384
• Files: TRIS, ASCE
• Created Date: Oct 23 2015 9:35AM