Solving a dynamic cell formation problem with machine cost and alternative process plan by memetic algorithms

  • Authors:

  • Reza Tavakkoli-Moghaddam;Nima Safaei;Masoud Babakhani

  • Affiliations:

  • Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran;Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran;Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

  • Venue:
  • SAGA'05 Proceedings of the Third international conference on StochasticAlgorithms: foundations and applications
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we present a new model of a cell formation problem (CFP) for a multi-period planning horizon where the product mix and demand are different in each period, but they are deterministic. As a consequence, the formed cells in the current period may be not optimal for the next period. This evolution results from reformulation of part families, manufacturing cells, and reconfiguration of the CFP as required. Reconfiguration consists of reforming part families, machine groups, and machine relocations. The objective of the model is to determine the optimal number of cells while minimizing the machine amortization/relocation costs as well as the inter-cell movements in each period. In the proposed model, parts have alternative process plans, operation sequence, and produce as batch. The machine capacity is also limited and machine duplication is allowed. The proposed model for real-world instances cannot be solved optimally within a reasonable amount of computational time. Thus, we propose an efficient memetic algorithm (MA) with a simulated annealing-based local search engine for solving the proposed model. This model is solved optimally by the Lingo software then the optimal solution is compared with the MA implementation.