Supply chain optimisation with assembly line balancing

Creative Commons License

Paksoy T., Ozceylan E., Gökçen H.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, vol.50, pp.3115-3136, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50
  • Publication Date: 2012
  • Doi Number: 10.1080/00207543.2011.593052
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3115-3136
  • Keywords: supply chain design, assembly line balancing, nonlinear mixed-integer programming, GOAL PROGRAMMING APPROACH, GENETIC ALGORITHM, DESIGN, MODEL, NETWORK, TRANSPORTATION, ALLOCATION, AGGREGATE, RETAILER, SYSTEM
  • Gazi University Affiliated: Yes


Supply chain management operates at three levels, strategic, tactical and operational. While the strategic approach generally pertains to the optimisation of network resources such as designing networks, location and determination of the number of facilities, etc., tactical decisions deal with the mid-term, including production levels at all plants, assembly policy, inventory levels and lot sizes, and operational decisions are related to how to make the tactical decisions happen in the short term, such as production planning and scheduling. This paper mainly discusses and explores how to realise the optimisation of strategic and tactical decisions together in the supply chain. Thus, a supply chain network (SCN) design problem is considered as a strategic decision and the assembly line balancing problem is handled as a tactical decision. The aim of this study is to optimise and design the SCN, including manufacturers, assemblers and customers, that minimises the transportation costs for determined periods while balancing the assembly lines in assemblers, which minimises the total fixed costs of stations, simultaneously. A nonlinear mixed-integer model is developed to minimise the total costs and the number of assembly stations while minimising the total fixed costs. For illustrative purposes, a numerical example is given, the results and the scenarios that are obtained under various conditions are discussed, and a sensitivity analysis is performed based on performance measures of the system, such as total cost, number of stations, cycle times and distribution amounts.