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Citation - WoS: 22
Citation - Scopus: 23
An Extended Mixed-Integer Programming Formulation and Dynamic Cut Generation Approach for the Stochastic Lot-Sizing Problem
(informs, 2018) Tunc, Huseyin; Kilic, Onur A.; Tarim, S. Armagan; Rossi, Roberto
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 - WoS: 18
Citation - Scopus: 23
Computing Non-Stationary (S, S) Policies Using Mixed Integer Linear Programming
(Elsevier Science Bv, 2018) Xiang, Mengyuan; Rossi, Roberto; Martin-Barragan, Belen; Tarim, S. Armagan
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. (C) 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 13
Citation - Scopus: 17
An Overview of Revenue Management and Dynamic Pricing Models in Hotel Business
(Edp Sciences S A, 2018) Bandalouski, Andrei M.; Kovalyov, Mikhail Y.; Pesch, Erwin; Tarim, S. Armagan
Basic concepts and brief description of revenue management models and decision tools in the hotel business are presented. An overview of the relevant literature on dynamic pricing, forecasting methods and optimization models is provided. The main ideas of the authors' customized revenue management method for the hotel business are presented.
Citation - WoS: 21
Citation - Scopus: 24
Heuristic Policies for the Stochastic Economic Lot Sizing Problem With Remanufacturing Under Service Level Constraints
(Elsevier Science Bv, 2018) Kilic, Onur A.; Tunc, Huseyin; Tarim, S. Armagan
In this paper, we address the stochastic economic lot sizing problem with remanufacturing under service level constraints. The problem emerges in hybrid production systems where demand can be met via two alternative sources: manufacturing new products and remanufacturing returned products. The deterministic counterpart of this problem has been considered in the literature and it is shown to be NP-Hard. We focus on the case where period demands and returns are stochastic. The optimal solution to this problem is not a deterministic production schedule but a control policy, yet its structure has not yet been characterized. We propose two heuristic policies for the problem that make use of simple decision rules to control manufacturing and remanufacturing operations and present mathematical models thereof. (C) 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 9
Citation - Scopus: 11
Confidence-Based Reasoning in Stochastic Constraint Programming
(Elsevier, 2015) Rossi, Roberto; Hnich, Brahim; Tarim, S. Armagan; Prestvvich, Steven; Prestwich, Steven
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. (C) 2015 Elsevier B.V. All rights reserved.

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