In this paper, we consider an online decision problem, where a decision maker has an option to buy a discount plan for his/her regular expenses. The discount plan costs an immediate upfront charge plus a commitment ch...
详细信息
In this paper, we consider an online decision problem, where a decision maker has an option to buy a discount plan for his/her regular expenses. The discount plan costs an immediate upfront charge plus a commitment charge per time slot. Upon expiration, the discount period can be extended if the decision maker continues paying the commitment charge, or be canceled if he or she decides not to pay the commitment charge anymore. We investigate online algorithms for the decision maker to decide when to buy the discount plan and when to cancel it without the knowledge of his/her future expenses, aiming at minimizing the overall cost. The problem is an extension of the classic Bahncard Problem, which is applicable for a wide range of online decision scenarios. We propose a novel deterministic online algorithm which can achieve a closed-form competitive ratio upper bounded by 4. We further propose a randomized online algorithm with a smaller competitive ratio and two variants tailored for average-case inputs and time-varying parameters, respectively. Lastly, we evaluate our algorithms against state-of-the-art online benchmark algorithms in two real-world scenarios.
A retailer provides products to satisfy consumers whose goodwill towards the retailer is positively correlated to the past fulfillment levels of the retailer. Consumer goodwill changes with fulfillment levels and infl...
详细信息
A retailer provides products to satisfy consumers whose goodwill towards the retailer is positively correlated to the past fulfillment levels of the retailer. Consumer goodwill changes with fulfillment levels and influences the demand. The retailer makes a commitment on order quantity to suppliers who offer price discounts. This paper focuses on the decision-making problem faced by the retailer to determine the minimum commitment at the beginning of the planning horizon and ordering policies in the long run. The objective is the retailer's profit maximization. We model the problem as a stochastic dynamic programming model and solve the model with a robust optimization approach considering consumers' goodwill. We find that all goodwill paths monotonically converge to a constant steady-state goodwill characterized by implicit equations. Moreover, if the retailer ignores long-term implications of its ordering policy, it may lose profits. In addition, the steady-state goodwill may decrease even with increased committed minimum quantity. (C) 2018 Elsevier Ltd. All rights reserved.
暂无评论