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    A branch-and-cut approach for the distributed no-wait flowshop scheduling problem
    (Pergamon-Elsevier Science Ltd, 2022) Avci, Mustafa; Avci, Mualla Gonca; Hamzadayi, Alper
    The distributed no-wait flowshop scheduling problem (DNWFSP) is an extension of the permutation flowshop scheduling problem with multiple factories and no-wait constraints. The DNWFSP consists of two decisions, namely, assigning jobs to the factories and sequencing the set of jobs assigned to the same factory. The no -wait constraints require that jobs have to be processed without any interruption between operations. Since the introduction of the DNWFSP, a number of metaheuristic approaches were developed to solve it. However, there exists no exact solution approach for the DNWFSP to the best of our knowledge. In this regard, a branch -and-cut (BC) algorithm is proposed to solve the DNWFSP. The proposed BC is integrated with a heuristic algorithm to obtain good upper bounds. Moreover, a set of symmetry breaking constraints are employed in the models to strengthen the formulations. The performance of BC is evaluated on a set of benchmark problem instances available in the related literature. The proposed BC is numerically compared with mixed-integer programming formulations of the DNWFSP which are solved by a commercial solver. The results obtained from the computational experiments reveal the effectiveness of the proposed approach. The proposed BC is able to solve all small-size instances, as well as, 206 out of 660 large-size instances to optimality. Besides, it is worth to mention that the average percentage gap for the large-size instances with two factories is only 0.43%.
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    Öğe
    The wildfire suppression problem with multiple types of resources
    (Elsevier, 2024) Avci, Mualla Gonca; Avcı, Mustafa; Battarra, Maria; Erdogan, Gunes
    The frequency and impact of wildfires have considerably increased in the past decade, due to the extreme weather conditions as well as the increased population density. The aim of this study is to introduce, model, and solve a wildfire suppression problem that involves multiple types of fire suppression resources and their operational characteristics. Two integer programming (IP) formulations, a basic IP and its reformulation with combinatorial Benders' cuts, are presented. The performances of the proposed formulations are evaluated on a set of randomly generated instances. The results indicate that the proposed formulations are able to obtain high quality upper and lower bounds. Extensive numerical experiments are performed to analyse the effects of several operational constraints on the computational performance of the models. A case study arising in Yata & gbreve;an district of Mu & gbreve;la province of T & uuml;rkiye is presented.