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Online pharmacies are an important part of the modern healthcare system. They interact with customers through well-designed web interfaces to deliver the healthcare customers need. In addition to well-designed web interfaces, online pharmacies rely on an effective supply chain system to provide medical supplies and services, and especially effective inventory management for supply systems. As green supply chain management (GSCM) becomes increasingly considered by countries, how to develop a sustainable inventory model that takes into account the revenue growth of an online pharmacy while preventing waste and reducing energy costs has become very important. In line with this trend, the study develops a sustainable inventory model that focuses on both economic aspect (profit) and environmental aspect (losses from excessive inventory) within a framework of a single period multi-product inventory model. Specifically, the sustainable inventory model applies the visual-attention-dependent demand (VADD) rate to characterize customer demand in an online trading environment, thereby seeking a profitable marketing strategy and reducing losses due to excessive inventory. Since the complexity of model optimization will drastically increase due to the inclusion of many products in the problem, a Genetic Algorithm (GA) based solution procedure is proposed to increase the feasibility of the proposed model in solving real problems. The sustainable inventory model and the solution procedure are illustrated, compared, and discussed with an online pharmacy example. Additionally, a sensitivity analysis is formulated to study the influence of model parameters on the model solution, the loss of unsold inventory that results in a waste of resources and energy, and the profit of online pharmacies.

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The design of product listing pages is a key component of Website design because it has significant influence on the sales volume on a Website. This study focuses on product placement in designing product listing pages. Product placement concerns how venders of online stores place their products over the product listing pages for maximization of profit. This problem is very similar to the offline shelf management problem. Since product information sources on a Web page are typically communicated through the text and image, visual stimuli such as color, shape, size, and spatial arrangement often have an effect on the visual attention of online shoppers and, in turn, influence their eventual purchase decisions. In view of the above, this study synthesizes the visual attention literature and theory of shelf-space allocation to develop a mathematical programming model with genetic algorithms for finding optimal solutions to the focused issue. The validity of the model is illustrated with example problems.

Purpose Group buying (GB) is a shopping strategy through which customers obtain volume discounts on the products they purchase, whereas retailers obtain quick turnover. In the scenario of GB, the optimal discount strategy is a key issue because it affects the profit of sellers. Previous research has focused on exploring the price discount and order quantity with a fixed selling price of the product assuming that customer demand is uncertain (but follows a known distribution). This study aims to look at the same problem but goes further to examine the case where not only customer demand is certain but also the demand distribution is unknown. Design/methodology/approach In this study, optimal price discount and order quantity of a GB problem cast as a price-setting newsvendor problem were obtained assuming that the distribution of customer demand is unknown. The price–demand relationship is considered in addition form and product form, respectively. The bootstrap sampling technique is used to develop a solution procedure for the problem. To validate the usefulness of the proposed method, a simulated comparison of the proposed model and the existing one was conducted. The effects of sample size, demand form and parameters of the demand form on the performance of the proposed model are presented and discussed. Findings It is revealed from the numerical results that the proposed model is appropriate to the problem at hand, and it becomes more effective as sample size increases. Because the two forms of demand indicate restrictive assumptions about the effect of price on the variance of demand, it is found that the proposed model seems to be more suitable for addition form of demand. Originality/value This study contributes to the growing literature on GB models by developing a bootstrap-based newsvendor model to determine an optimal discount price and order quantity for a fixed-price GB website. This model can assist the sellers in making decisions on optimal discount price and order quantity without knowing the form of customer demand distribution.

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