A simulated annealing for the cell formation problem with ratio level data

Abstract

In this paper, the cell formation problem is considered with ratio level data with an objective of minimising the cell load variation. The attempt has been made to propose a simulated annealing (SA) based on the perturbation scheme as random insertion perturbation scheme (RIPS). The ratio level data is distinguished by utilising the workload information gathered from process times, production quantity of parts and also from the capacity of the machines. A modified grouping efficiency (MGE) is used to measure the performance of the system. From the results it is observed that the simulated annealing produces the solution does not differ significantly from the optimal solutions for the benchmark problems. The algorithms which we have chosen the benchmark problems are K-means, modified ART1 and genetic algorithm taken from the literature.

Calibration of GA Parameters for Layout Design Optimization Problems Using Design of Experiments

“…Dmytryshyn et al [27] suggested a novel modeling approach for solving the cell-formation problem. Kamalakannan et al [28] developed a simulated annealing algorithm for solving CFP with ratio level data. Shashikumar et al [29] determined the solution for CFP using a heuristics approach.…”

Solving a Cubic Cell Formation Problem with Quality Index Using a Hybrid Meta-Heuristic Approach

“…In cell formation literature, besides exact methods, many meta-heuristic techniques have been used. In some studies, Particle Swarm Optimization (PSO) [13,14], Bacteria Foraging Algorithm (BFA) [15], Genetic Algorithm (GA) [11,16], Simulated Annealing (SA) [17,18] techniques were used. In addition, some hybrid [19] and stochastic approaches [20,21] are also presented to solve the cell formation problem.…”