ScienceAsia 48S (2022):ID 12-20 |doi:
10.2306/scienceasia1513-1874.2022.S002
Development of a modified biogeography-based
optimisation tool for solving the unequal-sized
machine and multi-row configuration facility layout
design problem
Saisumpan Sooncharoena, Srisatja Vitayasaka, Pupong Pongcharoena,*, Chris Hicksa,b
ABSTRACT: An effective layout can reduce material flow distances and manufacturing lead-times, whilst increasing
productivity, throughput and cost effectiveness. The facilities layout problem (FLP) is a non-deterministic polynomial-time hard problem, which means that the computational time taken to produce solutions increases exponentially with
problem size. Metaheuristics are particularly suitable for solving such problems in reasonable time. Biogeography-based optimisation (BBO) is a well-known nature-inspired computing metaheuristic. Its mechanisms mimic an analogy
with biogeography which relates to the migration, mutation and geographical distribution of biological organisms. This
paper presents a novel BBO optimisation tool that solves the unequal area facilities layout problem to generate multi-row solutions that minimise the total material flow distance. Two novel modifications were made to the conventional
BBO: the use of a Genetic Algorithm crossover operator in the migration process; and a changed method for selecting
candidate solutions. The local search approaches used data on flow intensities and machine adjacencies. Experiments
were conducted using five benchmark datasets obtained from the literature. The statistical analysis of the computational
results indicated that the proposed mBBOs produced statistically better solutions than the conventional BBO and other
metaheuristics for all datasets and converged more rapidly with comparable execution times.
Download PDF
209 Downloads 1226 Views
a |
Centre of Operations Research and Industrial Applications (CORIA), Department of Industrial
Engineering, Faculty of Engineering, Naresuan University, Phitsanulok 65000 Thailand |
b |
Newcastle University Business School, University of Newcastle upon Tyne, NE1 7RU, United Kingdom |
* Corresponding author, E-mail: pupongp@nu.ac.th
Received 30 Oct 2020, Accepted 12 Jul 2021
|