Examinando por Autor "Yuraszeck, Francisco"
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Ítem A competitive constraint programming approach for the group shop scheduling problem(Elsevier B.V., 2023-03) Yuraszeck, Francisco; Mejia, Gonzalo; Canut-De-Bon, DarioIn this paper, we propose a competitive Constraint Programming (CP) approach to solve the Group Shop Scheduling Problem (GSSP) under the makespan minimization criteria. Our contribution is two-fold: we provide a flexible mathematical formulation to solve the GSSP that can be used without change to solve other closed-related scheduling problems such as the Open Shop Scheduling Problem (OSSP), Job Shop Scheduling Problem (JSSP), and Mixed Shop Scheduling Problem (MSSP); and we improve several lower bounds and upper bounds from 130 classical GSSP instances from the literature. We evaluate our approach by comparing the performance with competitive methods mainly based on metaheuristics, where we were able to prove optimality in more than 85% of the instances in competitive running time, with a relative percentage deviation lower than 3% on average. In contrast to metaheuristics approaches, our CP method does not require calibrations of multiple parameters, several replicates for each instance, and complex computational coding to be competitive in both, solution quality and computational running times. © 2023 Elsevier B.V.. All rights reserved.Ítem A Constraint Programming Formulation of the Multi-Mode Resource-Constrained Project Scheduling Problem for the Flexible Job Shop Scheduling Problem(Institute of Electrical and Electronics Engineers Inc., 2023) Yuraszeck, Francisco; Montero, Elizabeth; Canut-De-Bon, Dario; Cuneo, Nicolas; Rojel, MaximilianoIn this work, a constraint programming (CP) formulation of the multi-mode resource-constrained project scheduling problem (MMRCPSP) is proposed for solving the flexible job shop scheduling problem (FJSSP) under the makespan minimization criterion. The resulting CP model allows us to tackle the classical instances of the FJSSP (such as where the operations of a given job follow a linear order). It can also handle FJSSP instances where the precedence relationships between operations are defined by an arbitrary directed acyclic graph (sequencing flexibility). The performance of our approach was tested using 271 classical FJSSP instances and 50 FJSSP instances with sequencing flexibility. We establish the validity of our approach by achieving an average relative percentage deviation of 3.04% and 0.18% when compared to the best-known lower and upper bounds, respectively. Additionally, we were able to contribute to the literature with ten new lower bounds and two new upper bounds. Our CP approach is relatively simple yet competitive and can be quickly applied and adapted by new practitioners in the area. © 2013 IEEE.Ítem A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem(MDPI, 2022-02) Yuraszeck, Francisco; Mejía, Gonzalo; Pereira, Jordi; Vilà, MarionaThis work addresses a particular case of the group shop scheduling problem (GSSP) which will be denoted as the fixed group shop scheduling problem (FGSSP). In a FGSSP, job operations are divided into stages and each stage has a set of machines associated to it which are not shared with the other stages. All jobs go through all the stages in a specific order, where the operations of the job at each stage need to be finished before the job advances to the following stage, but operations within a stage can be performed in any order. This setting is common in companies such as leaf spring manufacturers and other automotive companies. To solve the problem, we propose a novel heuristic procedure that combines a decomposition approach with a constraint programming (CP) solver and a restart mechanism both to avoid local optima and to diversify the search. The performance of our approach was tested on instances derived from other scheduling problems that the FGSSP subsumes, considering both the cases with and without anticipatory sequence-dependent setup times. The results of the proposed algorithm are compared with off-the-shelf CP and mixed integer linear programming (MILP) methods as well as with the lower bounds derived from the study of the problem. The experiments show that the proposed heuristic algorithm outperforms the other methods, specially on large-size instances with improvements of over 10% on average. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Ítem A self-tuning variable neighborhood search algorithm and an effective decoding scheme for open shop scheduling problems with travel/setup times(Elsevier B.V., 2020-09) Mejíaa, Gonzalo; Yuraszeck, FranciscoIn this paper, we study Open Shop Scheduling Problems (OSSPs) that involve (1) travel times between machines and/or (2) sequence-dependent setup times. First, we propose a new decoding scheme on the well-known permutation list representation and study its properties. Second, we describe an effective Variable Neighborhood Search (VNS) algorithm which incorporates the proposed decoding scheme and that uses a self-tuning routine to set its most important parameter. Last, we tested the performance of the algorithm on several sets of instances: the first two sets consisted of classical instances of OSSPs extended with randomly generated both travel times and anticipatory sequence-dependent setup times. The third set of problems were instances of OSSPs with travel times previously presented in the literature. The last set of problems consisted of classical OSSP of the literature and was used mainly to corroborate our results. The solutions of the proposed VNS were compared with the solutions of constraint programming (CP) algorithms, previous solutions and with the optimal solutions where available. The results revealed three important things: First, the decoding strategy was the factor that had the greatest influence on the performance of the VNS algorithm. Second, the proposed self-tuning VNS algorithm was robust and very easy to adapt to a variety of OSSPs. Third, the algorithm exhibited consistent and very competitive performance in terms of computer time and solution quality in all sets of instances. © 2020 Elsevier B.V.