Lookup NU author(s): Dr Pupong Pongcharoen,
Professor Christian Hicks
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
An effective layout can reduce material flow distance and manufacturing lead-times, whilst increasing throughput and cost effectiveness. The facilities layout problem (FLP) is a non-deterministic polynomial-time hard problem, which means that the computational time required to produce solutions increases exponentially with problem size. Biogeography-Based Optimisation (BBO) is a recent metaheuristic, which is based on an analogy with biogeography, the geographical distribution of biological organisms. It has been applied to engineering optimisation problems and the travelling salesman problem. The BBO utilises migration and mutation operations, which are based on the probabilistically sharing of fitness value information between candidate solutions. The performance of the BBO method can be improved by modifying these operations. This paper presents a new BBO tool that solves the unequal area facilities layout problem to generate solutions that minimise the total material flow distance. Non-identical machines are placed in multi-row configurations. Two novel modifications were made to the conventional BBO: the use of a crossover operator in the migration process; and a changed method for selecting candidate solutions. The local search approaches were also improved to take into account flow intensities and machine adjacencies. Experiments were conducted using five benchmark datasets obtained from the literature. The results demonstrated that all of the modifications produced statistically better solutions than the conventional BBO for all of the datasets and converged more quickly with comparable execution times. The best modified BBO generally outperformed other common used algorithms for almost all datasets.
Author(s): Vitayasak S, Pongcharoen P, Hicks C
Publication type: Article
Publication status: Published
Journal: International Journal of Production Economics
Print publication date: 01/08/2017
Online publication date: 24/05/2016
Acceptance date: 14/03/2016
ISSN (print): 0925-5273
Notes: This paper was submitted to a Special Issue Associated with the International Working Seminar on Production Economics.
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