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Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem

  • Mukund Nilakantan Janardhanan
  • Zixiang Li
  • Peter Nielsen
Methodologies and Application
  • 37 Downloads

Abstract

Worker assignment is a relatively new problem in assembly lines that typically is encountered in situations in which the workforce is heterogeneous. The optimal assignment of a heterogeneous workforce is known as the assembly line worker assignment and balancing problem (ALWABP). This problem is different from the well-known simple assembly line balancing problem concerning the task execution times, and it varies according to the assigned worker. Minimal work has been reported in worker assignment in two-sided assembly lines. This research studies worker assignment and line balancing in two-sided assembly lines with an objective of minimizing the cycle time (TALWABP). A mixed-integer programming model is developed, and CPLEX solver is used to solve the small-size problems. An improved migrating birds optimization algorithm is employed to deal with the large-size problems due to the NP-hard nature of the problem. The proposed algorithm utilizes a restart mechanism to avoid being trapped in the local optima. The solutions obtained using the proposed algorithms are compared with well-known metaheuristic algorithms such as artificial bee colony and simulated annealing. Comparative study and statistical analysis indicate that the proposed algorithm can achieve the optimal solutions for small-size problems, and it shows superior performance over benchmark algorithms for large-size problems.

Keywords

Assembly line balancing Two-sided assembly line Worker assignment Migrating birds optimization Metaheuristics 

Notes

Acknowledgements

This research is partially supported by National Science Foundation of China under grant 61803287 and China Postdoctoral Science Foundation under grant 2018M642928.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EngineeringUniversity of LeicesterLeicesterUK
  2. 2.Key Laboratory of Metallurgical Equipment and Control TechnologyWuhan University of Science and TechnologyWuhanChina
  3. 3.Hubei Key Laboratory of Mechanical Transmission and Manufacturing EngineeringWuhan University of Science and TechnologyWuhanChina
  4. 4.Department of Materials and ProductionAalborg UniversityAalborgDenmark

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