A stochastic resource allocation model, based on the principles of Markov decision processes (MDPs), is proposed in this paper. In particular, a general-purpose framework developed, which takes into account requests for both instant and future needs. The considered can handle two types reservations (i.e., specified unspecified time interval reservation requests), implement an overbooking business strategy to further increase revenues. resulting dynamic pricing problems be regarded as...

In this paper, resource allocation problems are formulated via a set of parallel birth–death processes (BDP). This way, we can model the fact that resources be allocated to customers at different prices, and hold them as long they like. More specifically, discretisation approach is applied discrete-time BDPs, which then integrated into one Markov decision process. The stochastic dynamics resulting system also investigated. As result, revenue management becomes decision-making problem, where...

Big data and the curse of dimensionality are common vocabularies that researchers in different communities have recently been dealing with, e.g. dynamic programming (DP) automatic control system society. A novel unweighted sampled based least square projection approach is proposed this study to address issue large state space DP optimisation problem. The method, particular, takes into account both contraction mapping monotonicity properties algorithm for value function approximation....

Partial differential equation parameter estimation is a mathematical and computational process used to estimate the unknown parameters in partial model from observational data. This paper employs greedy sampling approach based on Discrete Empirical Interpolation Method identify most informative samples dataset associated with its parameters. Greedy are train physics-informed neural network architecture which maps nonlinear relation between spatio-temporal data measured values. To prove...

In this paper, a novel model for price management systems in resource allocation problems is proposed. Stochastic customer requests allocations and releases are modelled as constrained parallel Birth–Death Processes (BDP). We address both instant (i.e. the requires to be allocated immediately) advance books future use) reservation requests, latter with bounded unbounded time interval options. Algorithms based on Dynamic Programming (DP) principles proposed calculation of suitable profiles....

This letter presents an Approximate Dynamic Programming (ADP) least-squares based approach for solving optimal stopping problems with a large state space. By extending some previous work in the area of problems, it provides framework their formulation and resolution. The proposed method uses combined on/off policy exploration mechanism, where states are generated by means transition probability distributions different from ones dictated underlying Markov decision processes. contraction...

In this paper, we analyse the convergence properties of Dynamic Programming Value Iteration algorithm by exploiting stability theory discrete-time switched affine systems. More specifically, formulating as a system, Lyapunov-based optimal policy selection strategy is designed to guarantee practical stabilisation resulting system towards an invariant set attraction containing given target value function. The switching control algorithm, referred can be regarded analysis tool and adopted...

The problem of managing the price for resource allocation arises in several applications, such as purchasing plane tickets, reserving a parking slot, booking hotel room or renting SW/HW resources on cloud. In this paper, we model management with parallel Birth-Death stochastic Processes (BDPs) to account fact that same can be possibly purchased by customers at different prices. addition, hold purchase necessary extent. maximization revenue both finite and infinite time horizon cases is...