TY - GEN
T1 - A heuristic approach based on artificial bee colony algorithm for retail shelf space optimization
AU - Ozcan, Tuncay
AU - Esnaf, Sakir
PY - 2011
Y1 - 2011
N2 - Due to high product variety and changing consumer demands, shelf space is one of the most scarce resources in retail management. At this point, the efficient allocation of the limited shelf space carries critical importance for maximizing the financial performance. On the other hand, because of NP-Hard nature of the shelf space allocation problem, heuristic approaches are required to solve real world problems. In this paper, different from existing studies in the literature, a heuristic approach based on artificial bee colony algorithm is presented for shelf space allocation problem by using a model which considers the space and cross elasticity. In order to demonstrate the efficiency of the developed approach, another heuristic approach based on particle swarm optimization is proposed. The performance analysis of these approaches is realized with problem instances including different number of products, shelves and categories. Experimental results show that the developed artificial bee colony algorithm is efficient methodology through near-optimal solutions and reasonable solving time for large sized shelf space allocation problems.
AB - Due to high product variety and changing consumer demands, shelf space is one of the most scarce resources in retail management. At this point, the efficient allocation of the limited shelf space carries critical importance for maximizing the financial performance. On the other hand, because of NP-Hard nature of the shelf space allocation problem, heuristic approaches are required to solve real world problems. In this paper, different from existing studies in the literature, a heuristic approach based on artificial bee colony algorithm is presented for shelf space allocation problem by using a model which considers the space and cross elasticity. In order to demonstrate the efficiency of the developed approach, another heuristic approach based on particle swarm optimization is proposed. The performance analysis of these approaches is realized with problem instances including different number of products, shelves and categories. Experimental results show that the developed artificial bee colony algorithm is efficient methodology through near-optimal solutions and reasonable solving time for large sized shelf space allocation problems.
KW - artificial bee colony algorithm
KW - heuristics
KW - particle swarm optimization
KW - retailing
KW - shelf space allocation
UR - http://www.scopus.com/inward/record.url?scp=80051973122&partnerID=8YFLogxK
U2 - 10.1109/CEC.2011.5949604
DO - 10.1109/CEC.2011.5949604
M3 - Conference contribution
AN - SCOPUS:80051973122
SN - 9781424478347
T3 - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
SP - 95
EP - 101
BT - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
T2 - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
Y2 - 5 June 2011 through 8 June 2011
ER -