A Branch and Bound Algorithm for the List Selection Problem in Direct Mail Advertising
提出一种分支定界算法，用于优化直邮广告中的媒体选择问题，即从多个邮寄列表中选出最佳组合以最大化受众覆盖，并通过实际数据验证了算法的有效性。
This paper describes a branch and bound approach for optimizing a media selection problem, namely, to choose the best set of mailing lists to maximize audience reach. Prompted by a national retailer's interest in more effective and efficient direct mail catalogue distribution, the algorithm exploits current heuristic approaches which improve computational efficiency. A numerical example and computational experience using actual data are discussed, along with extensions to other practical situations.