A priority rule-based constructive heuristic and an improvement method for balancing assembly lines with parallel multi-manned workstations


INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, vol.53, no.3, pp.736-756, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 53 Issue: 3
  • Publication Date: 2015
  • Doi Number: 10.1080/00207543.2014.920548
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.736-756
  • Keywords: genetic algorithms, line balancing, parallel multi-manned stations, constructive heuristic, integer programming, MODEL, ALGORITHM
  • Gazi University Affiliated: Yes


Assembly lines of big-size products such as buses, trucks and helicopters are very different from the lines studied in the literature. These products' manufacturing processes have a lot of tasks most of which have long task times. Since traditional assembly line models including only one worker in each station (i.e. simple assembly lines) or at most two workers (two-sided assembly lines) may not be suitable for manufacturing these type of products, they need much larger shop floor for a number of stations and long product flow times. In this study, an assembly line balancing problem (ALBP) with parallel multi-manned stations is considered. Following the problem definition, a mixed integer programming formulation is developed. A detailed study of priority rules for simple ALBPs is also presented, and a new efficient constructive heuristic algorithm based on priority rules is proposed. In order to improve solutions found by the constructive heuristic, a genetic algorithm-based solution procedure is also presented. Benchmark instances in the literature are solved by using the proposed mathematical programming formulation. It has been seen that only some of the small-size instances can be solved optimally by this way. So the efficiency of the proposed heuristic method is verified in small-size instances whose optimal solutions are found. For medium- and big-size instances, heuristics' results and CPU times are demonstrated. A comparative evaluation with a branch and bound algorithm that can be found in the literature is also carried out, and results are presented.