Mobile coupons delivery problem: Postponable online multi-constraint knapsack
作者:
Highlights:
• We study the mobile coupons delivery problem (MCDP) in a push-based location based advertisement.
• MCDP provides a new capability called postponable selection that enables an advertiser to delay the delivery of coupons.
• We propose the postponable online MKP which has significant potential uses in other online decision-making problems.
• The proposed algorithms provide insights into the solution approach for MCDP and the postponable online MKP.
• The proposed algorithm outperforms the FCFS heuristic by 46% on average.
• The postponable selection additionally improves performance by 4% on average.
摘要
We study the mobile coupons delivery problem (MCDP) in a push-based location based advertisement where an advertiser proactively sends mobile coupons to prospective customers on behalf of stores based on the customers' location and preferences. MCDP provides a new capability called postponable selection that enables an advertiser to better capitalize on the plethora of customer information provided by mobile phone users through network service providers. Postponable selection allows a customer to be reconsidered for selection for coupon delivery (i.e. postponed) as more information becomes available. We formulate MCDP as a new problem category referred to as the postponable online multi-constraint knapsack. We propose a single threshold-type algorithm with different design options and conduct extensive computational experiments to discuss the effectiveness of our algorithm as well as the benefit of postponable selection. Our experimental results show that the proposed algorithm outperforms the First-Come-First-Serve heuristic by 46% and postponable selection additionally improves performance by 4% on average.
论文关键词:Mobile coupons delivery problem,Online multi-constraint knapsack problem,Online algorithm,Postponable selection,Computational experiment
论文评审过程:Received 9 March 2018, Revised 8 September 2018, Accepted 9 October 2018, Available online 19 October 2018, Version of Record 17 November 2018.
论文官网地址:https://doi.org/10.1016/j.dss.2018.10.004