Browsing by Author "Hnich, B."

Now showing 1 - 5 of 5

Citation - Scopus: 5
Extensible Automated Constraint Modelling
(AAAI Press, 2011) Akgün, O.; Miguel, I.; Jefferson, C.; Frisch, A.M.; Hnich, B.
In constraint solving, a critical bottleneck is the formulation of an effective constraint model of a given problem. The CONJURE system described in this paper, a substantial step forward over prototype versions of CONJURE previously reported, makes a valuable contribution to the automation of constraint modelling by automatically producing constraint models from their specifications in the abstract constraint specification language ESSENCE. A set of rules is used to refine an abstract specification into a concrete constraint model. We demonstrate that this set of rules is readily extensible to increase the space of possible constraint models CONJURE can produce. Our empirical results confirm that CONJURE can reproduce successfully the kernels of the constraint models of 32 benchmark problems found in the literature. © 2023 Elsevier B.V., All rights reserved.
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.
Citation - WoS: 3
Citation - Scopus: 3
Template Design Under Demand Uncertainty by Integer Linear Local Search
(Taylor & Francis Ltd, 2006) Prestwich, S. D.; Tarim, S. A.; Hnich, B.
Production planning under uncertain demands leads to optimization problems that are hard both to model and to solve. We describe an integer linear model for a template design problem under uncertainty, and investigate its solution by a general-purpose local search algorithm for integer linear programs. Several such algorithms have previously been proposed as tools for solving large combinatorial optimization problems, and ours is based on a recent Boolean Satisfiability algorithm. In experiments it was slower than other methods on small instances, but rapidly outstripped them as the problem size and number of templates increased. It also found near-optimal solutions to all instances much more quickly. A general-purpose local search algorithm provides a rapid and convenient way of finding high-quality solutions to complex production problems.