Revenue Management with Repeated Customer Interactions

Motivated by online advertising, we model and analyze a revenue management problem where a platform interacts with a set of customers over a number of periods. Unlike traditional network revenue management, which treats the interaction between platform and customers as one-shot, we consider stateful customers who can dynamically change their goodwill toward the platform depending on the quality of their past interactions. Customer goodwill further determines the amount of budget that they allocate to the platform in the future. These dynamics create a trade-off between the platform myopically maximizing short-term revenues, versus maximizing the long-term goodwill of its customers to collect higher future revenues. We identify a set of natural conditions under which myopic policies that ignore the budget dynamics are either optimal or admit parametric guarantees; such simple policies are particularly desirable since they do not require the platform to learn the parameters of each customer dynamic and only rely on data that is readily available to the platform. We also show that, if these conditions do not hold, myopic and finite look-ahead policies can perform arbitrarily poorly in this repeated setting. From an optimization perspective, this is one of a few instances where myopic policies are optimal or have parametric performance guarantees for a dynamic program with nonconvex dynamics. We extend our model to the cases where supply varies over time and where customers may not interact with the platform in every period. This paper was accepted by Chung Piaw Teo, optimization.

Optimal policy for composite sensing with crowdsourcing

The mobile crowdsourcing technology has been widely researched and applied with the wide popularity of smartphones in recent years. In the applications, the smartphone and its user act as a whole, which called as the composite node in this article. Since smartphone is usually under the operation of its user, the user’s participation cannot be excluded out the applications. But there are a few works noticed that humans and their smartphones depend on each other. In this article, we first present the relation between the smartphone and its user as the conditional decision and sensing. Under this relation, the composite node performs the sensing decision of the smartphone which based on its user’s decision. Then, this article studies the performance of the composite sensing process under the scenario which composes of an application server, some objects, and users. In the progress of the composite sensing, users report their sensing results to the server. Then, the server returns rewards to some users to maximize the overall reward. Under this scenario, this article maps the composite sensing process as the partially observable Markov decision process, and designs a composite sensing solution for the process to maximize the overall reward. The solution includes optimal and myopic policies. Besides, we provide necessary theoretical analysis, which ensures the optimality of the optimal algorithm. In the end, we conduct some experiments to evaluate the performance of our two policies in terms of the average quality, the sensing ratio, the success report ratio, and the approximate ratio. In addition, the delay and the progress proportion of optimal policy are analyzed. In all, the experiments show that both policies we provide are obviously superior to the random policy.

MYOPIC POLICIES FOR NON-PREEMPTIVE SCHEDULING OF JOBS WITH DECAYING VALUE

In many scheduling applications, minimizing delays is of high importance. One adverse effect of such delays is that the reward for completion of a job may decay over time. Indeed in healthcare settings, delays in access to care can result in worse outcomes, such as an increase in mortality risk. Motivated by managing hospital operations in disaster scenarios, as well as other applications in perishable inventory control and information services, we consider non-preemptive scheduling of jobs whose internal value decays over time. Because solving for the optimal scheduling policy is computationally intractable, we focus our attention on the performance of three intuitive heuristics: (1) a policy which maximizes the expected immediate reward, (2) a policy which maximizes the expected immediate reward rate, and (3) a policy which prioritizes jobs with imminent deadlines. We provide performance guarantees for all three policies and show that many of these performance bounds are tight. In addition, we provide numerical experiments and simulations to compare how the policies perform in a variety of scenarios. Our theoretical and numerical results allow us to establish rules-of-thumb for applying these heuristics in a variety of situations, including patient scheduling scenarios.

Sequential Decision Making in Computational Sustainability via Adaptive Submodularity

Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. Such problems are generally notoriously difficult. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. Problems exhibiting the adaptive submodularity property can be efficiently and provably near-optimally solved using simple myopic policies. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Secondly, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the US Geological Survey and the US Fish and Wildlife Service.

The Nation Still in Daze, the Need to Race Out of the Incompetent Maze: Sustainable Development (SD) Laws and Nepal

All societies alike have made efforts on socio, economic, political transformations in order to suit their particularized historical, cultural conditions. In this process, that, the development needs a holistic approach over just the 'economic growth' approach, ineludibly, became the pragmatic recourse to agree upon by the human kind. In other words, this norm creation of 'sustainable development' has been expanding in its scope to encompass the determinations of what needs to be done, and what needs to be checked. In the backdrop of a liberal economic system that Nepal attempts to reap benefits from, the backfiring of myopic policies has hit us, as the society fails to create self sustaining legal system in the present scene, where the country is required to juggle with three goals simultaneously, economic development, environmental soundness and human rights for the social uplift. The paper deals with these areas of concern, in order to outline the need for sustainability in Nepal through a more prudent set of laws, and their implementation which can work efficiently in rapidly changed social conditions of this nation, to address and provide better working outcomes to start with, for the future generations.

INVENTORY DECISIONS WITH DECREASING PURCHASING COSTS

Costs of many items drop systematically throughout their life-cycles, due to advances in technology and competition. Motivated by the management of service parts for some high-tech products, this paper studies inventory decisions for such items. In a periodic review setting with stochastic demand, we model the purchasing costs of successive periods as a stochastic and decreasing sequence. Unit selling price of the item is determined as some mark-up of the purchasing cost and, hence, will change over time as well. We consider two specific mark-up models: (1) purchasing cost plus constant-dollar-amount mark-up, and (2) purchasing cost plus constant-percentage mark-up. To maximize the total discounted expected profit, we derive conditions under which myopic policies are optimal for the systems.