Decision Support Mechanism for Cellular Production System – Application of NSGA-II Meta-heuristic and TOPSIS Ranking

Authors

  • A. S. Khan National University of Science and Technology (NUST), Islamabad
  • R. Ullah University of Technology, Nowshera

Abstract

Cellular arrangement is considered beneficial for the distribution of heavy workload, resource utilization
and fast paced production. In such mechanisms, machines, tools and product features are classified into
different cells. Such arrangement impacts the overall performance of system in the form of productivity
and throughput. In current study, serial, parallel and tubular systems have been analyzed with multiple
variants of each production system. The objective is to select variants on the basis of optimal production
time, least cost and higher productivity. Two methods have been used owing to the complex and
combinatorial nature of the problem. Initially, a modified version of Non Sorting Genetic Algorithm
(NSGA-II) has been used to provide Pareto fronts where possible candidates for optimum solution have
been presented. A Multi Attribute Decision Making (MADM) approach of Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS) criteria has been used to select the best compromise
of optimal result. The results show that the objectives of cost, time and productivity are in conflict with
each other and a global solution cannot be attained against the optimal values of all objectives. Also, an
increased productivity can be assured by reducing the total time with an increase in cost.

Author Biographies

A. S. Khan, National University of Science and Technology (NUST), Islamabad

NUST Business School

R. Ullah, University of Technology, Nowshera

Department of Mechanical Technology

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A.S. Khan and R. Ullah / The Nucleus 56, No. 2 (2019) 70-77

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Published

11-11-2019

How to Cite

[1]
A. S. Khan and R. Ullah, “Decision Support Mechanism for Cellular Production System – Application of NSGA-II Meta-heuristic and TOPSIS Ranking”, The Nucleus, vol. 56, no. 2, pp. 70–77, Nov. 2019.

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