Advance planning models in managing coalition loyalty programs

忠诚奖励计划（lrp）是一种旨在奖励重复购买行为的客户的营销计划。虽然提供这些服务是有成本的，但最终对公司是有利的。它们的作用是吸引新客户，并培育和鼓励与现有客户的长期关系。因此，近年来，lrp在许多行业中越来越受欢迎。在世界范围内继续建立新的机构，现有的机构继续改组。AEROPLANr是加拿大首屈一指的LRP。据报道，它的项目有460万活跃会员，是一家拥有超过75家合作伙伴的合资企业。2012年和2013年，兑换Aeroplan奖励的成本估计为13.82亿美元。Aeroplan是联盟忠诚度计划（CLP）的一个例子，从会员注册规模、积分收集客户来源的多样化、奖励产品的多样化以及运营成本和风险等方面来看，它是最复杂的基于忠诚度的系统。管理这样一个复杂的系统需要先进的解决方案技术。***该研究项目将研究并为管理clp时出现的作战、战术和战略决策提供支持。工作将围绕三个短期和中期目标展开：***1)在各种期权合约设置和赎回需求情景下，为单期或多期规划范围建立奖励订购决策模型。***2)开发多种产品和奖励结构下收入确认的赎回、负债和破损预测模型。***3)在LRP公司寻求进入合作伙伴关系以推广合作伙伴的产品或服务的背景下，建模合作广告和协调决策。****为了实现这些目标，将使用一系列方法。这些方法包括近似、精确（如l形分解方法）、鲁棒优化、马尔可夫链、模拟和基于博弈论的方法。在这些方法中，其他的分解方法，如拉格朗日松弛和丹齐格-沃尔夫，将被应用。研究计划的长期目标包括将这些解决方案嵌入决策支持系统，为LRP决策者提供实用指导，以提高他们的效率和有效性。虽然最初的结果将专门针对管理联盟忠诚度奖励计划，但将开发的方法将提高我们在随机规划、鲁棒优化和大规模优化方面的知识。它们将适用于供应链管理的一般背景，在战术和操作层面评估不同需求假设下的不同合同类型和结构，同时考虑多个合作伙伴关系，而不是单个合作伙伴关系。**

Loyalty reward programs (LRPs) are marketing programs aimed at rewarding customers for repeat buying behavior. While there is a cost in offering them, they are ultimately beneficial to the firm. They serve to attract new customers, and to nurture and encourage long-term relationships with existing customers. Hence, LRPs have grown in popularity in recent years across a spectrum of industries. New ones continue to be established worldwide and existing ones continue to be restructured. AEROPLANr, is Canada's premier LRP. It reports 4.6 million active members in its program and is a joint venture with more than 75 partners. The cost of redeemed Aeroplan rewards is estimated at $1.382 billion for 2012 and 2013. Aeroplan is an example of a coalition loyalty program (CLP), the most complex loyalty-based systems, in terms of the membership enrollment size, diversification of customer sources for the collection of points, diversification of reward offerings, and operating costs and risks. Managing such a complex system requires advanced solutions technologies.***This research program will study and provide support for operational, tactical, and strategic decisions that arise when managing CLPs. The work will be centered around three short and medium-term objectives:***1)  Modelling reward ordering decisions for single or multiple period planning horizons under various option contract settings and redemption demand scenarios.***2) Developing predictive models of redemption, liability and breakage for revenue recognition under multiple product and reward structures.***3) Modelling cooperative advertising and coordination decisions in the context of an LRP firm seeking to enter a partnership to promote the partner's products or services.****In order to achieve these objectives, a range of methodologies will be used. These include approximation, exact (e.g., L-shaped decomposition methods), robust optimization, Markov chain, simulation, and game-theory based approaches. Within some of these methodologies, other decomposition methods such as lagrangean relaxation and Dantzig-Wolfe, will be applied. The research program's long-term objective consists of embedding these solutions within a decision support system to provide practical guidance to LRP decision-makers with the aim of improving their efficiency and effectiveness.***While the initial outcomes will be specifically directed at managing coalition loyalty reward programs, the methodologies that will be developed will advance our knowledge in stochastic programming, robust optimization, and large-scale optimization. They will have applicability in the general context of supply chain management in evaluating, at the tactical and operational levels, different contract types and structures under various demand assumptions, while simultaneously considering multiple partner relationships rather than a single partner relationship.**