Modeling Shop Mix Problems as Pareto Optimization Considering Consumer Preference
This study models a shop mix problem in a large-scale shopping center, aiming at realizing Pareto optimization of consumer preference. Our study defines a consumer preference order to respective shops as a two-level hierarchy obtained by computation from the “repeat rate” in reference data from actual POS. The combinatorial problem that preference order should be Pareto-improved is modeled and solved with a genetic algorithm. Results show that positively preferred shops do not coincide with the shops with a high average repeat rate. Results show that our method using a repeat rate is a good indicator for tenant replacement planning.
KeywordsGenetic algorithm Operation research Optimization Service engineering
- 1.Ishigaki T, Takenaka T, Motomura Y (2010) Category mining by heterogeneous data fusion using PdLSI model in a retail service. In: IEEE international conference on data mining, pp 857–862Google Scholar
- 3.Takenaka T et al (2013) Modeling customer behaviors in a shopping mall; tenant variety and customer types. In: Proceedings of 1st international conference on serviceology (ICServ2013) (to appear)Google Scholar
- 4.Holland J (1975) Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann ArborGoogle Scholar