TITLE:
Simulation Model Using Meta Heuristic Algorithms for Achieving Optimal Arrangement of Storage Bins in a Sawmill Yard
AUTHORS:
Asif Rahman, Siril Yella, Mark Dougherty
KEYWORDS:
Simulation, Genetic Algorithm, Simulated Annealing, Planning and Arrangement, Decision Making, Storage Bins, Log Stackers and Sawmill Yard
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.6 No.2,
May
22,
2014
ABSTRACT:
Bin planning
(arrangements) is a key factor in the timber industry. Improper planning of the
storage bins may lead to inefficient transportation of resources, which
threaten the overall efficiency and thereby limit the profit margins of
sawmills. To address this challenge, a simulation model has been developed.
However, as numerous alternatives are available for arranging bins, simulating
all possibilities will take an enormous amount of time and it is
computationally infeasible. A discrete-event simulation model incorporating
meta-heuristic algorithms has therefore been investigated in this study.
Preliminary investigations indicate that the results achieved by GA based simulation
model are promising and better than the other meta-heuristic algorithm.
Further, a sensitivity analysis has been done on the GA based optimal
arrangement which contributes to gaining insights and knowledge about the real
system that ultimately leads to improved and enhanced efficiency in sawmill
yards. It is expected that the results achieved in the work will support timber
industries in making optimal decisions with respect to arrangement of storage
bins in a sawmill yard.