Browsing by Author "Teymourifar, Aydin"
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- An open-source simulation model for solving scheduling problemsPublication . Teymourifar, Aydin; Li, Jie; Li, Dan; Zheng, TaichengIn this study, an open-source simulation model is presented for solving scheduling problems. The model is capable of solving different benchmarks. The methods involved in the simulation are mainly based on generating dispatching rules or using them to solve problems, but there are other heuristics as well. Dispatching rules in an evolutionary process are generated using Gene Expression Programming. For this aim, a coding method, which has not been described in the literature before, is explained. Along with the explanation of the properties of the source code, information about deterministic, dynamic models, buffer states, machine breakdown states, and the methods used to deal with them is presented. Concepts are explained with visual examples. In addition, a subject that has not been investigated in the literature before is analyzed by using the simulation model. This topic is to examine the results of solving machine assignment and operation sequencing sub-problems in flexible job shop scheduling problems with different rules. Moreover, objective functions that the source code can handle are discussed. Unlike many studies in the literature, the codes are presented to the readers as open source. Also, it is open to development and can be easily modified by users to solve other types of problems. Finally, in the study, experimental results are presented on the basis of some benchmarks available in the literature, and the limits of the study and source code are explained.
- Assigning patients to healthcare centers using dispatching rulesPublication . Teymourifar, Aydin; Trindade, Maria A. M.This study proposes a model for the balanced assignment of patients to healthcare centers in a region. In the suggested model, it is supposed that patients want to go to the nearest center, which causes an imbalance in the workloads of resources between centers. This disproportion is undesirable not only for the centers but also for the patients. Thus, balancing assignments is targeted. This goal is expressed in a model with a multi-objective function. Since balancing is one of the main goals of the sectorization concept, we characterize the model based on it. Unlike studies in the literature, we do sectorization employing dispatching rules. This diminishes the problem's complexity and makes it suitable for solving actual, large, and dynamic problems. We simulated the system using the Rockwell Arena software. We consider the effect of different seasons, days, and hours on the system. The dispatching rule used for sectorization is optimized using the OptQuest software. The numerical results demonstrate that by optimizing the dispatching rule, it is possible to enhance the objective function significantly.
- Balanced patient assignment to healthcare centers through dispatching rulesPublication . Teymourifar, Aydin; Trindade, Maria A. M.In the realm of public health management, ensuring a balanced assignment of patients to healthcare centres is a critical concern. This study introduces a novel approach for this purpose, utilizing dispatching rules. Highlighting the need for an easily applicable approach to regulating patient flow efficiently, the study shows the benefit of utilizing dispatching rules in healthcare management. Innovatively, this research departs from traditional approaches by introducing a multi-objective model grounded in the concept of sectorization. This model, unique in the public health literature, leverages dispatching rules to simplify complex, dynamic patient assignment scenarios. Incorporating various factors, the model is simulated, and the optimization of the dispatching rules is carried out. The study’s findings demonstrate that the optimized dispatching rule significantly enhances the model’s efficacy in balancing patient assignments across healthcare centres. This improvement is pivotal in addressing the uneven distribution of healthcare resources. This research makes a substantial contribution to the public health literature by offering a novel and practical solution for balancing patient load among healthcare centres. Its successful application in simulated environments suggests a promising pathway for real-world implementations, potentially leading to more efficient healthcare systems and improved patient care outcomes.
- A comparison among optimization software to solve bi-objective sectorization problemPublication . Teymourifar, AydinIn this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single- and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models; of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems.
- A comparison between metaheuristic-based and solver-based methods to solve routing problem based on sectorizationPublication . Teymourifar, Aydin; Rodrigues, Ana Maria; Ferreira, José SoeiroThis study involves the division of a region into smaller units, based on sectorization, and a route determination for each of them. The routing problem of each unit is defined as a basic traveling salesman problem (TSP). Different implementations of a widely used method to solve TSP are compared. The method is based on a mixed-integer linear programming model, in which after finding an initial solution, a sub-tour elimination process is done iteratively. The implements of the method use some metaheuristics and solvers, which are available in libraries and toolboxes of MATLAB and Python. The implementations are compared in terms of results and solution times. Suggestions to solve large-scale problems are presented, which are derived from the results.
- A comparison between optimization tools to solve sectorization problemPublication . Teymourifar, Aydin; Rodrigues, Ana Maria; Ferreira, José Soeiro; Lopes, CristinaIn sectorization problems, a large district is split into small ones, usually meeting certain criteria. In this study, at first, two single-objective integer programming models for sectorization are presented. Models contain sector centers and customers, which are known beforehand. Sectors are established by assigning a subset of customers to each center, regarding objective functions like equilibrium and compactness. Pulp and Pyomo libraries available in Python are utilised to solve related benchmarks. The problems are then solved using a genetic algorithm available in Pymoo, which is a library in Python that contains evolutionary algorithms. Furthermore, the multi-objective versions of the models are solved with NSGA-II and RNSGA-II from Pymoo. A comparison is made among solution approaches. Between solvers, Gurobi performs better, while in the case of setting proper parameters and operators the evolutionary algorithm in Pymoo is better in terms of solution time, particularly for larger benchmarks.
- A comparison between two approaches to optimize weights of connections in artificial neural networksPublication . Teymourifar, AydinArtificial neural networks (ANNs) have been used for estimation in numerous areas. Raising the accuracy of ANNs is always one of the important challenges, which is generally defined as a non-linear optimization problem. The aim of this optimization is to find better values for the weights of the connections and biases in ANN because they seriously affect the efficiency. This study uses two approaches to do such optimization in an ANN. For this aim, we create a feed-forward backpropagation ANN using the functions of MATLAB’s deep learning toolbox. To improve its accuracy, in the first approach, we use the Levenberg—Marquardt algorithm (LMA) for training, which is available in MATLAB’s deep learning toolbox. In the second approach, we optimize the values of weights and biases of ANN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), available in MATLAB’s global optimization toolbox. Then, we assess the accuracy of estimation for the trained ANNs. In this way, for the first time in the literature, we compare these methods for the optimization of an ANN. The used data sets are also available in MATLAB. Based on the acquired results, in some data sets, training with LMA, and for some others training with PSO cause the best results, however, training with LMA is faster, significantly. Although the used approaches and the obtained conclusions are beneficial for researchers that work in this field, they have some limitations. For instance, since only the functions and data sets from MATLAB are used, it can only serve as an example for researchers.
- Contract mechanisms for value-based technology adoption in healthcare systemsPublication . Teymourifar, AydinAlthough technological innovations are often intended to improve quality and efficiency, they can exacerbate systemic challenges when not aligned with the principles of value-based care. As a result, healthcare systems in many countries face persistent inefficiencies stemming from the overuse, underuse, misuse, and waste associated with the adoption of health technology. This narrative review examines the dual impact of healthcare technology and evaluates how contract mechanisms can serve as strategic tools for promoting cost-effective, outcome-oriented integration. Drawing from healthcare management, and supply chain literature, this paper analyzes various payment and contract models, including performance-based, bundled, cost-sharing, and revenue-sharing agreements, through the lens of stakeholder alignment. It explores how these mechanisms influence provider behavior, patient access, and system sustainability. The study contends that well-designed contract mechanisms can align stakeholder incentives, reduce inefficiencies, and support the delivery of high-value care across diverse healthcare settings. We provide concrete examples to illustrate how various contract mechanisms impact the integration of health technologies in practice.
- A data-driven approach to pricing models for balanced public–private healthcare systemsPublication . Teymourifar, Aydin; Kaya, Onur; Öztürk, GürkanThis study focuses on a real-world healthcare system with coexisting public and private hospitals with distinct characteristics. While public hospitals have lower costs, they also suffer from long waiting times and diminishing patients’ perceived quality of care. Conversely, despite their higher fees, private hospitals offer shorter waiting times, leading to a more favorable perception of quality. A balanced healthcare system could provide societal benefits. Pricing strategies greatly influence a patient’s hospital selection. For instance, reduced fees in private hospitals attract more patients, consequently reducing overcrowding in public facilities and elevating the overall quality of services provided. This study aims to develop pricing models to foster a balanced and socially advantageous healthcare system. This system determines private hospital pricing through contract mechanisms with the government. Thus, we delve into the ramifications of various contract models between the government and private hospitals on social utility. Our findings underscore the communal advantages of contract mechanisms. Furthermore, we generalize the proposed models to apply to similar systems.
- Defining an integrated framework for demand forecasting, due date assignment, scheduling, and performance evaluation in operations managementPublication . Teymourifar, Aydin; Pinto, Maria Francisca de Lima Teixeira; Moreira, José Maria Antunes Bento; Machado, Inês Filipa MedeirosThis study introduces an integrated framework for operations management, encompassing demand forecasting, capacity allocation based on estimated demand, due date assignment for incoming orders according to allocated capacity, and utilizing due dates as scheduling rules and performance evaluation criteria. We provide detailed insights into implementing this innovative approach, leveraging regression models and neural networks. Additionally, we provide a comprehensive review of alternative methods applicable to this framework. Our method offers practical applicability across diverse sectors of operations management, yielding tangible outcomes, which we elucidate in this study.
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