TY - GEN
T1 - Application of ant colony optimizer (Aco) for effective path planning in a big-box store or retail facility
AU - Okwu, Modestus O.
AU - Lagouge, Tartibu K.
AU - Afenogho, Justice O.
N1 - Publisher Copyright:
© IEOM Society International.
PY - 2020
Y1 - 2020
N2 - Real-life stochastic problems are better addressed by adopting metaheuristic techniques. One of the interesting metaheuristic techniques for defining the shortest path is the ant colony optimization (ACO) algorithm. A considerable number of maps for shortest path have been considered in time past using classical techniques which is appropriate for deterministic variables. For stochastic or nondeterministic decision variables, metaheuristic techniques are much more appropriate. This is possible by mimicking the path navigation and swarm propensities of natural entities to provide real-time quality geographical images representing diverse areas or terrain for easy access to routing and path planning for sustainability and economic benefits in systems. In this research, the solution power of ACO has been demonstrated to predict customers’ behaviour in a popular retail outlet, using the travelling salesman problem (TSP) for stochastic shortest path during the purchase of items in a big-box facility with multiple products and sixteen (16) sections. Data obtained from the facility has been validated. The tour length was subjected to pheromone optimization to obtain a pheromone update of 0.00345 per metre as the maximum and 0.001725 as the best update at varying evaporation rate. In conclusion, out of the selected sections, two major paths in the big-box facility yielded optimal tour length and as such either of the paths can be followed by customers to spend the minimum required time in the facility.
AB - Real-life stochastic problems are better addressed by adopting metaheuristic techniques. One of the interesting metaheuristic techniques for defining the shortest path is the ant colony optimization (ACO) algorithm. A considerable number of maps for shortest path have been considered in time past using classical techniques which is appropriate for deterministic variables. For stochastic or nondeterministic decision variables, metaheuristic techniques are much more appropriate. This is possible by mimicking the path navigation and swarm propensities of natural entities to provide real-time quality geographical images representing diverse areas or terrain for easy access to routing and path planning for sustainability and economic benefits in systems. In this research, the solution power of ACO has been demonstrated to predict customers’ behaviour in a popular retail outlet, using the travelling salesman problem (TSP) for stochastic shortest path during the purchase of items in a big-box facility with multiple products and sixteen (16) sections. Data obtained from the facility has been validated. The tour length was subjected to pheromone optimization to obtain a pheromone update of 0.00345 per metre as the maximum and 0.001725 as the best update at varying evaporation rate. In conclusion, out of the selected sections, two major paths in the big-box facility yielded optimal tour length and as such either of the paths can be followed by customers to spend the minimum required time in the facility.
KW - Ant Colony Optimization (ACO)
KW - Fast-Moving Consumer Goods (FMCG)
KW - Travelling Salesman Problem (TSP)
UR - http://www.scopus.com/inward/record.url?scp=85105557484&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85105557484
SN - 9781792361234
T3 - Proceedings of the International Conference on Industrial Engineering and Operations Management
SP - 788
EP - 799
BT - Proceedings of the 2nd African International Conference on Industrial Engineering and Operations Management, 2020
PB - IEOM Society
T2 - 2nd African International Conference on Industrial Engineering and Operations Management, IEOM 2020
Y2 - 7 December 2020 through 10 December 2020
ER -