Developing a new solution methodology from scratch to solve similar real-life problems requires a significant effort. Instead, it would be better to consider a problem-class and then develop a generic approach that can be particularized to individual cases. In this thesis, we consider a class of online resource-allocation problems that have to factor-in user’s time preference and propose a generic reusable decision support system for multiple domains. Time preference in behavioural economics plays a major role in understanding a customer’s behavioural pattern over time. With the advent of internet based services (e.g., cloud computing, in-app advertising, ride sharing applications and others), customers interact with on-demand markets of service providers for meeting their resource requirements. Every time a customer requests a service, the service provider tries to satisfy the customer immediately. Otherwise, the customers choose to leave the service provider and requests other service providers to get their job done. One way to handle this problem is to offer the service to the customer in a succeeding time-period, rather than the exact one that the customer has requested. This, however, requires expert judgement to understand customer’s willingness to wait until certain time-periods (say t). In this work, we address the supplier-platform revenue maximization problem by incorporating customer time preferences. Proposing a generic approach that can accommodate a wide variety of digital platform characteristics, however, is challenging because the domains in which these digital platforms reside have different deadline expectations for decision-making. Our approach addresses this problem space in a generic manner that will accommodate this variation in time expectations across domains, thus providing a generalized approach to this problem space.