Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (4): 289-297.doi: 10.23940/ijpe.22.04.p7.289297

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Modified Cat Swarm Optimization for Optimal Assembly Sequence Planning Problems

Chiranjibi Champatiraya,*, Sonali Samalb, M. V. A. Raju Bahubalendrunic, R. N. Mahapatraa, Debasisha Mishrad, and B. K. Balabantarayb   

  1. aDepartment of Mechanical Engineering, National Institute of Technology Meghalaya, 793003, India;
    bDepartment of Computer Science & engineering, National Institute of Technology Meghalaya, 793003, India;
    cDepartment of Mechanical Engineering, National Institute of Technology Puducherry, 609609, India;
    dDepartment of Strategic Management, Indian Institute of Management Shillong, 609609, India
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address:

Abstract: Product manufacturing industries are looking for efficient assembly planners to instantly generate an optimal feasible assembly sequence for multiple product variants. The products with a sizeable part count account for colossal search space, and applying feasibility constraints leads to NP-hard problems. The current study seeks to identify and customize an optimization algorithm for the assembly sequence planning (ASP) problem in order to maximize computational efficiency. Nature has the optimal constructal patterns for several phenomena; three nature-inspired algorithms have been identified, namely Cat Swarm Optimization (CSO), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Further, they are tailored and enhanced for ASP with a non-linear weight factor and t-distribution. It is found that weighted CSO with t-distribution (TWCSO) is efficient in solving ASP problems with a large number of products with less computational time, and the rate of convergence is better compared with that of weighted ACO with t-distribution (TWACO) and weighted PSO with t-distribution (TWPSO).

Key words: assembly sequence planning (ASP), TWCSO, TWACO, TWPSO