MIP model and elitist strategy hybrid GA–SA algorithm for layout design

Abstract

It is most important for any manufacturing industry to have an efficient layout for their production environment to participate in global competition. One of the prime objectives of such an organisation is to decide an optimal arrangement of their facilities (machines or departments) in a two-dimensional planar region satisfying desired objectives, which is termed facility layout problem. To overcome the drawbacks of traditional layout design methodology, it is attempted to solve three important layout design problems such as inter-cell layout design, determination of optimum location for input/output stations and flow path layout design of material handling system simultaneously in an integrated manner. The quality of the final layout is evaluated by minimizing the total material handling cost, where the perimeter distance metric is used for the distance measurement. Sequence-pair, an elegant representation technique is used for layout encoding. The translation from sequence-pair to layout is efficiently done by longest common subsequence computation methodology. Due to the non-polynomial hard nature of the problem considered, an elitist strategy based hybrid genetic algorithm that uses simulated annealing as B I. Jerin Leno local search mechanism (ESHGA) is developed and tested with test problem instances available in the literature. The results indicate that proposed integrated methodology with developed mixed integer programming based mathematical model along with ESHGA could generate realistic layouts compared to reported result.

Hopca

“…Lacksonen (2018) [311] used a MILP model to find the block diagram layouts with varying department areas using a branch and bound algorithm. Leno et al (2018) [312] solved an unequal area facility layout problem with a MIP formulation by using an elitist strategy hybrid GA-SA algorithm. Recently, Wan et al (2020) [313] dealt with a multi-row facility layout problem by using an improved multi-objective greedy randomised adaptive search procedure.…”