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Joint optimization of inventory control and product placement on e-commerce websites using genetic algorithms

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Abstract

Prior research had shown that the design of product listing pages has significant influence on the sales volume on an e-commerce website. This study focused on product placement in designing product listing pages; that is, how venders of online stores place their products over the product listing pages for maximization of profit. When trying to increase the sales volume through a better website design, it is imperative to keep a close watch on inventory replenishment so as to reduce business cost. Therefore, this study proposed a visual-attention-dependent demand inventory model for determining the optimal product placement and inventory replenishment decisions that jointly maximize the total profit under the arrangement constraints. This model assumes that visual stimuli such as image size and location have a significant effect on product demand. The substitution effect between products on demand was also examined. Then a genetic algorithms-based search method was employed to solve the model. Finally, the validity of the proposed model was illustrated with example problems.

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Acknowledgments

The authors are grateful to Associate Editor and five anonymous reviewers for their valuable comments, which have greatly helped to improve the paper.

Author information

Authors and Affiliations

  1. Department of Distribution Management, National Taichung University of Science and Technology, 129, Sec. 3, Sanmin Rd., Taichung, Taiwan

    Yan-Kwang Chen & Chien-Hua Yeh

  2. Department of Hotel and M.I.C.E Management, Overseas Chinese University, 100, Chiao Kwang Rd., Taichung, Taiwan

    Fei-Rung Chiu

  3. Department of Health Services Administration, Chung-Shan Medical University, 110, Sec. 1, Jian-Koa N. Rd., Taichung, Taiwan

    Hung-Chang Liao

Authors
  1. Yan-Kwang Chen
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Corresponding author

Correspondence to Yan-Kwang Chen.

Appendix: optimal operative condition in gas

Appendix: optimal operative condition in gas

The quality of the solution generated by GAs usually depends on the setting of their control parameters: population size (PS), crossover probability (CP), mutation rate (MR), and number of generations (GN). Therefore, setting the control parameters of genetic algorithms so as to obtain good results is necessary for the users. In order to find the optimal setting of control parameters in GAs, an orthogonal array experiment was developed because it can provide a set of well balanced (minimum) experiments. In the orthogonal array experiment, three levels of each control parameter were planned as shown in Table 7.

Table 7 Parameters and levels in GA
Full size table

As L9 (34) is the smallest orthogonal array experiment that will accommodate the problem of 4 control parameters and 3 levels, this experiment for the optimal setting of control parameters in GAs was conducted. In L9 (34) orthogonal array experiment, there are totally nine assays (different level combinations of the four control parameters). For each assay, three replicates of optimal profit values from the GAs, denoted by y 1, y 2, and y 3, were recorded in Table II. The characteristic of the objective value (i.e., total profit) is larger-the-better; hence, the appropriate Taguchi’s signal-to-noise (SN) ratio for evaluating the experiment results is

$$ {\text{SN}} = - 10 \times \log \left( {\frac{1}{3}\mathop \sum \limits_{i = 1}^{3} \frac{1}{{y_{i}^{2} }}} \right) $$
(8)

Taguchi’s SN ratio is a larger-the-better index that can serve as an objective function for optimization of control parameter settings. More details about Taguchi’s SN ratio can be found in [54]. Table 8 lists Taguchi’s SN ratios for all nine assays. Since each assay is the combination of different parameter levels, it is essential to segregate the individual SN ratio of control parameters. This can be done by summing up the obtained SN ratios for the corresponding level settings (Table 9). As an example, for parameter PS at level 1 the average of SN ratios is 86.57090191, which is calculated by (86.62542568 + 86.60401424 + 86.48326581)/3. According to the individual SN ratio of control parameters, the optimal combination of the four parameters in the GAs are PS = 100, CP = 0.10, MR = 0.05, and GN = 40,000.

Table 8 Experiment layout of L9 (34) orthogonal array and results
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Table 9 Average of SN ratios for each parameter at different levels in GAs
Full size table

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Chen, YK., Chiu, FR., Liao, HC. et al. Joint optimization of inventory control and product placement on e-commerce websites using genetic algorithms. Electron Commer Res 16, 479–502 (2016). https://doi.org/10.1007/s10660-016-9216-9

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