Browsing by Author "Rossi, Roberto"

Now showing 1 - 4 of 4

Citation Count: Tunc, Huseyin; Kilic, Onur A.; Tarim, S. Armagan; et al. "An Extended Mixed-Integer Programming Formulation and Dynamic Cut Generation Approach for the Stochastic Lot-Sizing Problem", Informs Journal On Computing, Vol. 30, No. 3, pp. 492-506, (2018)
An Extended Mixed-Integer Programming Formulation and Dynamic Cut Generation Approach for the Stochastic Lot-Sizing Problem
(Inform, 2018) Tunç, Hüseyin; Kılıç, Onur A.; Tarım, S. Armağan; Rossi, Roberto; 6641
We present an extended mixed-integer programming formulation of the stochastic lot-sizing problem for the static-dynamic uncertainty strategy. The proposed formulation is significantly more time efficient as compared to existing formulations in the literature and it can handle variants of the stochastic lot-sizing problem characterized by penalty costs and service level constraints, as well as backorders and lost sales. Also, besides being capable of working with a predefined piecewise linear approximation of the cost function-as is the case in earlier formulations-it has the functionality of finding an optimal cost solution with an arbitrary level of precision by means of a novel dynamic cut generation approach.
Citation Count: Xiang, M., Rossi, R., Martin-Barragan, B., Tarım, S.A. (2018). Computing non-stationary (s, S) policies using mixed integer linear programming. European Journal of Operational Research, 271(2), 490-500. http://dx.doi.org/10.1016/j.ejor.2018.05.030
Computing non-stationary (s, S) policies using mixed integer linear programming
(Elsevier Science Bv, 2018) Xiang, Mengyuan; Rossi, Roberto; Martin-Barragan, Belen; Tarım, S. Armağan; 6641
This paper addresses the single-item single-stocking location non-stationary stochastic lot sizing problem under the (s, S) control policy. We first present a mixed integer non-linear programming (MINLP) formulation for determining near-optimal (s, S) policy parameters. To tackle larger instances, we then combine the previously introduced MINLP model and a binary search approach. These models can be reformulated as mixed integer linear programming (MILP) models which can be easily implemented and solved by using off-the-shelf optimization software. Computational experiments demonstrate that optimality gaps of these models are less than 0.3% of the optimal policy cost and computational times are reasonable.
Citation Count: Rossi, R., Hnich, B., Tarım, S.A., Prestvvich, S. (2015). Confidence-based reasoning in stochastic constraint programming. Artificial Intelligence, 228, 129-152. http://dx.doi.org/10.1016/j.artint.2015.07.004
Confidence-based reasoning in stochastic constraint programming
(Elsevier Science BV, 2015) Rossi, Roberto; Hnich, Brahim; Tarım, S. Armağan; Prestvvich, Steven; 6641
In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the original problem being analysed; by solving this reduced problem, with a given confidence probability, we obtain assignments that satisfy the chance constraints in the original model within prescribed error tolerance thresholds. To achieve this, we blend concepts from stochastic constraint programming and statistics. We discuss both exact and approximate variants of our method. The framework we introduce can be immediately employed in concert with existing approaches for solving stochastic constraint programs. A thorough computational study on a number of stochastic combinatorial optimisation problems demonstrates the effectiveness of our approach.
Citation Count: Prestwich, Steven D.; Rossi, Roberto; Tarim, S. Armagan, "Randomness as a constraint" Principles And Practice Of Constraint Programming, Cp 2015, Vol.9255, pp.351-366, (2015).
Randomness as a constraint
(Springer-Verlag Berlin, 2015) Prestwich, Steven D.; Rossi, Roberto; Tarım, S. Armağan
Some optimisation problems require a random-looking solution with no apparent patterns, for reasons of fairness, anonymity, undetectability or unpredictability. Randomised search is not a good general approach because problem constraints and objective functions may lead to solutions that are far from random. We propose a constraint-based approach to finding pseudo-random solutions, inspired by the Kolmogorov complexity definition of randomness and by data compression methods. Our "entropy constraints" can be implemented in constraint programming systems using well-known global constraints. We apply them to a problem from experimental psychology and to a factory inspection problem.