E-commerce companies have been implementing several sales strategies toincrease orders from online buyers. One of the most profitable strategies used to enticebuyers to purchase more items is a volume discount on particular goods. Generally, an individualonline buyer has limited bargaining power and always makes orders individually.From the perspective of buyers, they look for an efficient way to receive a lower price fortheir products without buying a large volume. Moreover, buyers are often heterogeneousin terms of preferences and willingness-to-pay. In such a situation, wc propose a newapproach for forming buying groups while taking consideration of buyers’ heterogeneouspreferences. The approach, which is based on genetic algorithms with roulette-wheel selection,searches for an optimized group of buyers by aggregating a number of buyer-selecteditems to obtain the highest utility received from the sellers. The paper compares the performanceof the algorithm using roulette-wheel selection with generational replacement.Additionally, the web-based application of the proposed approach is developed in order toillustrate how the proposed algorithm works in the real world. The experimental results ofowr empirical case study show that the algorithm optimally searches fo~r the best solution.
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