An efficient heuristic bundle search algorithm for buyers in electronic markets
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In the age of electronic commerce, with low-cost information access, it has been recognized that a bundle search in a combinatorial trade is very valuable for buyers. Optimal travel package search is one of the most prominent examples of bundle search, allowing buyers to exploit various discounts through business partnerships among sellers. There are many varieties of bundle search problem. In general, these problems are varieties of the NP-hard Knapsack problem. Developing heuristic algorithms to find close optimal results is a promising approach to solving those problems with polynomial or even linear computational complexity. In this paper, we address a bundle search problem for buyers in electronic markets. There exist multiple sellers in a market. Sellers offer widely varying prices for identical items and various discount ratios for different purchasing costs. We propose a heuristic bundle search algorithm, Maximal Gain Bundle Search (MGBS) algorithm, to solve this problem. The time complexity of MGBS algorithm is O(NM) in the worst case. The experiments show that the execution time of the MGBS algorithm is not sensitive to an increase of the number of bundle goods and sellers. The simulation results also show that the MGBS algorithm can produce a nearly optimal bundle purchase for buyers.