Browsing by Author "Sariyildiz, I. Sevil"

Now showing 1 - 12 of 12

Citation - WoS: 15
Citation - Scopus: 22
A discrete event simulation procedure for validating programs of requirements: The case of hospital space planning
(ELSEVIER, 2020) Cemre Cubukcuoglu; Pirouz Nourian; I. Sevil Sariyildiz; M. Fatih Tasgetiren; Nourian, Pirouz; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Cubukcuoglu, Cemre
This paper introduces a Discrete-Event Simulation (DES) tool developed as a parametric CAD program for validating a program of requirements (PoR) for hospital space planning. The DES model simulates the procedures of processing of patients treated by doctors calculating patient throughput and patient waiting times based on the number of doctors patient arrivals and treatment times. In addition the tool is capable of defining space requirements by taking hospital design standards into account. Using this tool what-if scenarios and assumptions on the PoR about space planning can be tested and/or validated. The tool is ultimately meant for reducing patient waiting times and/or increasing patient throughput by checking the match of the layout of a hospital with respect to its procedural operations. This tool is envisaged to grow into a toolkit providing a methodological framework for bringing Operations Research into Architectural Space Planning. The tool is implemented in Python for Grasshopper (GH) a plugin of Rhinoceros CAD software using the SimPy library. (C) 2020 The Authors. Published by Elsevier B.V.
Citation - WoS: 5
Citation - Scopus: 7
A Memetic Algorithm for the Bi-Objective Quadratic Assignment Problem
(ELSEVIER SCIENCE BV, 2019) Cemre Cubukcuoglu; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Liang Gao; Murat Kucukvar; Tasgetiren, M. Fatih; Kucukvar, Murat; Sariyildiz, I. Sevil; Fatih Tasgetiren, M.; Cubukcuoglu, Cemre; Sevil Sariyildiz, I.; Gao, Liang; CH Dagli; GA Suer
Recently multi-objective evolutionary algorithms (MOEAs) have been extensively used to solve multi-objective optimization problems (MOPs) since they have the ability to approximate a set of non-dominated solutions in reasonable CPU times. In this paper we consider the bi-objective quadratic assignment problem (bQAP) which is a variant of the classical QAP which has been extensively investigated to solve several real-life problems. The bQAP can be defined as having many input flows with the same distances between the facilities causing multiple cost functions that must be optimized simultaneously. In this study we propose a memetic algorithm with effective local search and mutation operators to solve the bQAP. Local search is based on swap neighborhood structure whereas the mutation operator is based on ruin and recreate procedure. The experimental results show that our bi-objective memetic algorithm (BOMA) substantially outperforms all the island-based variants of the PASMOQAP algorithm proposed very recently in the literature. (C) 2019 The Authors. Published by Elsevier Ltd.
Citation - WoS: 14
Citation - Scopus: 18
A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements
(MDPI AG, 2016) Cemre Cubukcuoglu; Ioannis Chatzikonstantinou; Mehmet Fatih Tasgetiren; I. Sevil Sariyildiz; Quan-Ke Pan; Chatzikonstantinou, Ioannis; Tasgetiren, Mehmet Fatih; Sariyildiz, I. Sevil; Cubukcuoglu, Cemre; Pan, Quan-Ke
This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla which is a rural touristic region located on the west coast of Turkey near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically we consider three objectives which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces as well as special functions such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation and gives promising results when the Pareto front approximation is examined.
Citation - Scopus: 21
Addressing design preferences via auto-associative connectionist models: Application in sustainable architectural Façade design
(Elsevier B.V., 2017) Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; Chatzikonstantinou, Ioannis; Sariyildiz, I. Sevil
Truly successful designs are characterized by both satisfaction of design goals and the presence of desirable physical features. Experienced design professionals are able to exercise their cognition to satisfy both aspects to a high degree. However complex design tasks represent challenges for human cognition and as such computational decision support systems emerge as a relevant topic. We present a computational decision support framework for treating preferences related to physical design features. The proposed framework is based on auto-associative machine learning models that inductively learn relationships between design features characterizing highly performing designs. The knowledge matter to be learned is derived through multi-objective stochastic optimization. The resulting auto-associative models are excited with a preference vector containing a favorable composition of design features. The models are able to alleviate those relationships that result in shortcomings of performance. The model thus outputs well performing design solution where preferences pertaining to physical features are also satisfied to the extent possible. The paper focuses on the applicability of the proposed approach in architectural design as an exceptional example of complex design discusses methods to evaluate model performance and validates the proposed method through an application focusing on the design of a sustainable façade. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 8
Addressing the High-Rise Form Finding Problem by Evolutionary Computation
(IEEE, 2015) Berk Ekici; Seckin Kutucu; I. Sevil Sariyildiz; M. Fatih Tasgetiren; Ekici, Berk; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Kutucu, Seckin
This paper aims to examine the application of evolutionary algorithms to the form finding problem of high-rise buildings. In the light of mentioned purpose this study concentrates on the conceptual phase of the design process due to the importance of early design decisions. In this respect multi-objective real-parameter constrained optimization is considered as the method of this study in order to solve high-rise design problem. From the point of evolutionary computation we compare two evolutionary algorithms (NSGA-II and DE) focusing on their computational performance and architectural features of the resulting alternatives. Two objective functions are formulated that specifically focus on structural displacement minimization and construction cost per square meter minimization which are clearly conflicting. As a conclusion we discuss in the context of the high-rise design problem the solutions identified by the NSGA-II and DE algorithms.
Citation - WoS: 29
Citation - Scopus: 38
Hospital layout design renovation as a Quadratic Assignment Problem with geodesic distances
(ELSEVIER, 2021) Cemre Cubukcuoglu; Pirouz Nourian; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Shervin Azadi; Nourian, Pirouz; Azadi, Shervin; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Cubukcuoglu, Cemre
Hospital facilities are known as functionally complex buildings. There are usually configurational problems that lead to inefficient transportation processes for patients medical staff and/or logistics of materials. The Quadratic Assignment Problem (QAP) is a well-known problem in the field of Operations Research from the category of the facility's location/allocation problems. However it has rarely been utilized in architectural design practice. This paper presents a formulation of such logistics issues as a QAP for space planning processes aimed at renovation of existing hospitals a heuristic QAP solver developed in a CAD environment and its implementation as a computational design tool designed to be used by architects. The tool is implemented in C# for Grasshopper (GH) a plugin of Rhinoceros CAD software. This tool minimizes the internal transportation processes between interrelated facilities where each facility is assigned to a location in an existing building. In our model the problem of assignment is relaxed in that a single facility may be allowed to be allocated within multiple voxel locations thus alleviating the complexity of the unequal area assignment problem. The QAP formulation takes into account both the flows between facilities and distances between locations. The distance matrix is obtained from the spatial network of the building by using graph traversal techniques. The developed tool also calculates spatial geodesic distances (walkable easiest and/or shortest paths for pedestrians) inside the building. The QAP is solved by a heuristic optimization algorithm called Iterated Local Search. Using one exemplary real test case we demonstrate the potential of this method in the context of hospital layout design/re-design tasks in 3D. Finally we discuss the results and possible further developments concerning a generic computational space planning framework.
Citation - WoS: 13
Citation - Scopus: 16
Multi-objective diagrid facąde optimization using differential evolution
(Institute of Electrical and Electronics Engineers Inc., 2015) Ioannis Chatzikonstantinou; Berk Ekici; I. Sevil Sariyildiz; Basak Kundakci Koyunbaba; Ekici, Berk; Chatzikonstantinou, Ioannis; Sariyildiz, I. Sevil; Koyunbaba, Basak Kundakci
Facądes constitute one of the fundamental systems of contemporary buildings. They serve multiple purposes such as to ensure proper indoor climate to provide sufficient daylight but also to create a desirable architectural image. Integration of these aspects makes facąde design a complex task that requires significant effort in order to achieve well-performing results. It is thus desirable that systematic approaches to facąde design are developed. In this study we consider facąde design as a multiobjective optimization problem integrating diverse design criteria namely indoor daylight distribution structural performance and cost. We evaluate design performance by making use of simulation. Consequently we use Differential Evolution (DE) to search for best-tradeoff solutions. We compare the performance of two DE variants using the Hypervolume metric and also through qualitative inspection. We report facąde designs that demonstrate interesting and often unexpected features concluding that the proposed approach may lead to a novel more integrated design process. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 9
Citation - Scopus: 10
Multi-Objective skylight optimization for a healthcare facility foyer space
(Institute of Electrical and Electronics Engineers Inc., 2017) Muhittin Yufka; Berk Ekici; Cemre Cubukcuoglu; Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; Ekici, Berk; Chatzikonstantinou, Ioannis; Sariyildiz, I. Sevil; Yufka, Muhittin; Cubukcuoglu, Cemre
In this paper the design of a specific case study of a foyer space is concerned in healthcare facility. The design task of a healthcare facility in architectural perspective is one of the most challenging tasks in the architectural design field since it involves different spaces that have unique requirements. Specifically a foyer space has been considered as a gathering area that answers people's needs and expectations. The study shows an application of computational intelligence for a skylight design in foyer space. For this reason objective functions are considered to minimize skylight cost and to maximize the daylight performance of the interior space. Multi-Objective Self-Adaptive Ensemble Differential Evolution Algorithm and Non-Dominated Sorting Genetic Algorithm-II are proposed to tackle this complex problem. According to results jE-DEMO algorithm presents satisfactory solutions as well as NSGA-II. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 38
Citation - Scopus: 52
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: Background methodology setup and machine learning results
(Elsevier Ltd, 2021) Berk Ekici; Tugce Kazanasmaz; Michela Turrin; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Ekici, Berk; Turrin, Michela; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Kazanasmaz, Z. Tugce
Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 28
Citation - Scopus: 35
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 2: Optimisation problems algorithms results and method validation
(Elsevier Ltd, 2021) Berk Ekici; Tugce Kazanasmaz; Michela Turrin; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Ekici, Berk; Turrin, Michela; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Kazanasmaz, Z. Tugce
High-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design, thus the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs) which provide a swift and accurate prediction for the entire building design, hence the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices glazing type and building-shape parameters using the setup and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios respectively using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 12
Citation - Scopus: 19
Optimal Design of new Hospitals: A Computational Workflow for Stacking- Zoning- and Routing
(ELSEVIER, 2022) Cemre Cubukcuoglu; Pirouz Nourian; I. Sevil Sariyildiz; M. Fatih Tasgetiren; Nourian, Pirouz; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Cubukcuoglu, Cemre
The paper proposes a generative design workflow for three major hospital layout planning steps to satisfy multiplex configurational requirements. The initial step is stacking through clustering functional spaces into floor plans for which a spectral method is presented. Subsequently a novel simultaneous process of zoning and routing is proposed as a Mixed-Integer Programming problem-solving task, performed on a quadrilateral mesh whose faces and edges are allocated respectively to the rooms and the corridors. The paper situates the workflow in the context of an Activity-Relations-Chart for a general hospital while demonstrating explaining and justifying the generated optimal floor plans. The conversion of the hospital layout problem to a Mixed-Integer Programming problem enables the use of existing Operations Research solvers allowing for the generation of optimal solutions in a digital design environment. The comprehensive problem formulation for a real-world scenario opens a new avenue for utilization of mathematical programming/optimization in healthcare design.
Citation - WoS: 99
Citation - Scopus: 115
Performative computational architecture using swarm and evolutionary optimisation: A review
(Elsevier Ltd, 2019) Berk Ekici; Cemre Cubukcuoglu; Michela Turrin; I. Sevil Sariyildiz; Ekici, Berk; Turrin, Michela; Sariyildiz, I. Sevil; Cubukcuoglu, Cemre
This study presents a systematic review and summary of performative computational architecture using swarm and evolutionary optimisation. The taxonomy for one hundred types of studies is presented herein that includes different sub-categories of performative computational architecture such as sustainability cost functionality and structure. Specifically energy daylight solar radiation environmental impact thermal comfort life-cycle cost initial and global costs energy use cost space allocation logistics structural assessment and holistic design approaches are investigated by considering their corresponding performance aspects. The main findings including optimisation and all the types of parameters are presented by focussing on different aspects of buildings. In addition usage of form-finding parameters of all reviewed studies and the distributions for each performance objectives are also presented. Moreover usage of swarm and evolutionary optimisation algorithms in reviewed studies is summarised. Trends in publications published years problem scales and building functions are examined. Finally future prospects are highlighted by focussing on different aspects of performative computational architecture in accordance to the evidence collected based on the review process. © 2018 Elsevier B.V. All rights reserved.