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Article Citation - WoS: 7Citation - Scopus: 12Genetic Algorithm and Tabu Search Memory With Course Sandwiching (gats_cs) for University Examination Timetabling(Tech Science Press, 2020) Abayomi-Alli, A.; Misra, S.; Fernandez-Sanz, L.; Abayomi-Alli, O.; Edun, A. R.University timetable scheduling is a complicated constraint problem because educational institutions use timetables to maximize and optimize scarce resources, such as tine and space. In this paper, an examination timetable system using Genetic Algorithm and Tabu Search memory with course sandwiching (GAT_CS), was developed fora lame public University. The concept of Genetic Algorithm with Selection and Evaluation was implemented while the memory properties of Tabu Search and course sandwiching replaced Crossover and Mutation. The result showed that GAT_CS had hall allocation accuracies of 96.07% and 99.02%, unallocated score of 3.93% and 0.98% for first and second semesters, respectively. It also automatically sandwiched (scheduled) multiple examinations into single halls with a simulation time in the range of 20-29.5 seconds. The GAT_CS outperformed previous related works on the same timetable dataset. It could, however, be improved to reduce clashes, duplications, multiple examinations and to accommodate more system-defined constraints.Article Citation - WoS: 3Citation - Scopus: 7Optimization-Based Scheduling of Construction Projects With Generalized Precedence Relationships: a Real-Life Case Study(Sharif University of Technology, 2024) Aminbakhsha, S.; Ahmed, A.Concomitant reduction of cost and duration is recognized as one of the main aspects of construction planning. Expedition of project schedule naturally incurs extra costs due to implementation of more productive and/or high-price construction techniques. Meanwhile, a reduction in time is usually plausible only down to a certain limit, below which renders expeditions either technically or nancially unviable. Thus, striking a reasonable balance between project cost and duration remains a desirable yet challenging task for which there has been a myriad of advancements and literature. Despite the many studies associated with this problem-referred to as Time-Cost Trade-off Problem (TCTP) it is observed that only a few exercise TCTPs with the generalized logical relationships. This observation holds despite the fact that generalized precedence relationships are imperative to introduce parallelism and to secure a realistic overlap among the activities. In this regard, a Simulated Annealing-based (SA-based) Genetic Algorithm (GA) as proposed herein, is specically designed to provide the capability of exerting TCTPs with properly overlapped activities. Eciency of this algorithm is tested over a range of problems and its performance is validated over a large-scale real-case construction project. Results of the hybridized GA indicate fast and robust convergence to high-quality solutions. © 2024, Sharif University of Technology. All rights reserved.Conference Object Citation - WoS: 2Citation - Scopus: 3Autonomous Tuning for Constraint Programming Via Artificial Bee Colony Optimization(Springer-verlag Berlin, 2015) Soto, Ricardo; Crawford, Broderick; Mella, Felipe; Flores, Javier; Galleguillos, Cristian; Misra, Sanjay; Paredes, FernandoConstraint Programming allows the resolution of complex problems, mainly combinatorial ones. These problems are defined by a set of variables that are subject to a domain of possible values and a set of constraints. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. Autonomous Search provides the ability to the solver to self-tune its enumeration strategy in order to select the most appropriate one for each part of the search tree. This self-tuning process is commonly supported by an optimizer which attempts to maximize the quality of the search process, that is, to accelerate the resolution. In this work, we present a new optimizer for self-tuning in constraint programming based on artificial bee colonies. We report encouraging results where our autonomous tuning approach clearly improves the performance of the resolution process.
