Browsing by Author "Rossi, R."

Now showing 1 - 3 of 3

Citation - WoS: 21
Citation - Scopus: 31
Forecasting Intermittent Demand by Hyperbolic-Exponential Smoothing
(Elsevier Science Bv, 2014) Prestwich, S. D.; Tarim, S. A.; Rossi, R.; Hnich, B.
Croston's method is generally viewed as being superior to exponential smoothing when the demand is intermittent, but it has the drawbacks of bias and an inability to deal with obsolescence, where the demand for an item ceases altogether. Several variants have been reported, some of which are unbiased on certain types of demand, but only one recent variant addresses the problem of obsolescence. We describe a new hybrid of Croston's method and Bayesian inference called Hyperbolic-Exponential Smoothing, which is unbiased on non-intermittent and stochastic intermittent demand, decays hyperbolically when obsolescence occurs, and performs well in experiments. (C) 2014 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Hybrid Metaheuristics for Stochastic Constraint Programming
(Springer, 2015) Prestwich, S. D.; Tarim, S. A.; Rossi, R.; Hnich, B.
Stochastic Constraint Programming (SCP) is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. This paper proposes a metaheuristic approach to SCP that can scale up to large problems better than state-of-the-art complete methods, and exploits standard filtering algorithms to handle hard constraints more efficiently. For problems with many scenarios it can be combined with scenario reduction and sampling methods.
Citation - WoS: 7
Citation - Scopus: 10
A Neuroevolutionary Approach To Stochastic Inventory Control in Multi-Echelon Systems
(Taylor & Francis Ltd, 2012) Prestwich, S. D.; Tarim, S. A.; Rossi, R.; Hnich, B.
Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form.