Optimization & Operations Research
Javad Gerami; Alireza Davoodi
Abstract
Two-stage network Data Envelopment Analysis (DEA) models under variable returns to scale (VRS) suffer from a well-known pitfall: efficiency score decomposition and frontier projection can be mutually inconsistent, undermining both the theoretical foundations and practical interpretability of the results. ...
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Two-stage network Data Envelopment Analysis (DEA) models under variable returns to scale (VRS) suffer from a well-known pitfall: efficiency score decomposition and frontier projection can be mutually inconsistent, undermining both the theoretical foundations and practical interpretability of the results. A further limitation is the universal assumption of strong disposability for all inputs and outputs, which is unrealistic when variables are structurally or statistically interdependent—as is common in healthcare settings. This paper addresses both issues simultaneously by developing a novel two-stage network DEA model under hybrid disposability (HD) technology, which allows selective strong or weak disposability for subsets of closely related inputs, intermediate measures, and outputs. We formally derive the efficiency decomposition and frontier projection under HD technology, establish theoretical consistency between the envelopment and multiplier forms, and prove that the proposed model yields Pareto-efficient targets. The model captures synergistic scale effects across stages and preserves structural dependencies between them, thereby providing a more realistic representation of multi-stage production systems. The practical relevance and advantages of the proposed framework are demonstrated through an empirical case study involving 32 Iranian healthcare centers operating under a two-stage network structure with interdependent variables.
Optimization & Operations Research
Ramin Kazemi; Akram Kohansal
Abstract
We consider the estimation of model parameters and prediction of unobserved records based on record statistics for the Basic Gompertz distribution (BGD) with parameter $\lambda$ using frequentist and Bayesian analysis. In frequentist analysis, we give the moment generating function of the $m$th record, ...
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We consider the estimation of model parameters and prediction of unobserved records based on record statistics for the Basic Gompertz distribution (BGD) with parameter $\lambda$ using frequentist and Bayesian analysis. In frequentist analysis, we give the moment generating function of the $m$th record, the maximum likelihood estimation (MLE) of $\lambda$, the moment-based estimate (MBE) of $\lambda$, the confidence interval for $\lambda$, and the prediction of future records. Exactly, we show that $$\hat{\lambda}_{\text{MBE}}=\frac{m(m+1)}{2\sum_{i=1}^{m}(e^{Y_i}-1)}, \hat{\lambda}_{\text{MLE}}=\frac{m}{e^{y_m}-1},$$ where $m\geq 1 $ and $Y_m=\max(\min)\{ X_1,\ldots,X_m \}$. In Bayesian analysis, we obtain the Bayesian sample-based estimation and prediction. Exactly, we show that under the squared error loss (SEL) function, $$\hat{\lambda}_{BS}=\frac{m+a}{e^{y_m}+b-1}$$ and under the LINEX loss function, $$\hat{\lambda}_{BL}=-\frac{m+a}{c}\ln\bigg( \frac{e^{y_m}+b-1}{e^{y_m}+(b+c)-1}\bigg).$$ Based on Monte Carlo simulations, the performances of the different methods of estimation and prediction are compared via MSEs and Biases. Finally, a real dataset has been analyzed for illustrative purposes.
Optimization & Operations Research
Nabil Sellami; Romaissa Mellal; Basim A. Hassan
Abstract
This paper introduces two hybrid nonlinear conjugate gradient algorithms, NRB1 and NRB2, for solving unconstrained optimization problems. Both methods are constructed as a convex combination of the AlBayati–AlAssady (BA) and Conjugate Descent methods (CD). The first variant, denoted NRB1, employs ...
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This paper introduces two hybrid nonlinear conjugate gradient algorithms, NRB1 and NRB2, for solving unconstrained optimization problems. Both methods are constructed as a convex combination of the AlBayati–AlAssady (BA) and Conjugate Descent methods (CD). The first variant, denoted NRB1, employs an adaptive combination parameter designed to align its search direction more closely with Newton’s method, improving curvature approximation and acceleration of convergence. The second variant, NRB2, independently satisfies the conjugacy condition without relying on line search mechanisms, enhancing numerical stability. Both methods guarantee sufficient descent and global convergence properties under the strong Wolfe line search criteria. Extensive numerical experiments, using the performance profile of Dolan and Moré, demonstrate that NRB1 and NRB2 consistently outperform some classical conjugate gradient methods, including BA, CD, and Dai–Yuan, particularly for large-scale problems. Furthermore, NRB1 is applied to image restoration under salt-and-pepper noise, achieving competitive or superior peak signal-to-noise ratios (PSNR) compared to the Fletcher–Reeves (FR) algorithm with significantly fewer iterations, especially at high noise intensities.
Optimization & Operations Research
Harmandeep Kaur; Sukhpreet Kaur Sidhu
Abstract
Multi-criteria decision-making (MCDM) often involves situations characterized by uncertainty, ambiguity, and vagueness. To address such complexities, MCDM techniques play a crucial role. This paper presents a comparative analysis of two widely used methods—Technique for Order Preference by ...
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Multi-criteria decision-making (MCDM) often involves situations characterized by uncertainty, ambiguity, and vagueness. To address such complexities, MCDM techniques play a crucial role. This paper presents a comparative analysis of two widely used methods—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)—within a hesitant fuzzy environment. Hesitant fuzzy sets allow decision-makers to express hesitation by assigning multiple possible membership values to an element rather than a single value. In this framework, the TOPSIS ranks alternatives based on their closeness to the positive and negative ideal solutions, while the VIKOR identifies a compromise solution by balancing individual and collective regret measures. The effectiveness of the comparison is demonstrated through illustrative numerical examples. Moreover, some real life applications of these methods are discussed.
Optimization & Operations Research
Sina Nemati; Jafar Fathali; Abolfazl Poureidi
Abstract
Classical inverse location models aim to modify problem parameters such that pre-specified facility locations become optimal with respect to a given objective. This paper addresses a fundamentally different variant: the inverse balanced facility location problem in the Euclidean plane, in ...
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Classical inverse location models aim to modify problem parameters such that pre-specified facility locations become optimal with respect to a given objective. This paper addresses a fundamentally different variant: the inverse balanced facility location problem in the Euclidean plane, in which parameters are adjusted so as to achieve an equitable distribution of client demand between two given facilities. Specifically, given a set of n weighted points in the plane and two predetermined facility locations, the objective is to minimally modify either the weights or the coordinates of the client points such that the absolute difference in total demand assigned to each facility-referred to as the unbalancing number-is minimized. For the weight-modification case, we establish that the planar problem is structurally equivalent to its network counterpart and is therefore solvable in O(n Log n) time under any Lp norm, via an existing linear programming formulation. For the coordinate-modification case under the Euclidean norm, we exploit the isometric property of orthogonal rotations to prove that thetwo-dimensional problem reduces, without loss of generality, to a one-dimensional problem along the perpendicular bisector of the segment joining the two facilities. Leveraging this reduction, we design three novel greedy algorithms-IFLP1, IFLP2, and IFLP3-that prioritize minimization of the unbalancing number, minimization of the total transfer cost, and a hybrid criterion balancing both objectives, respectively. Under uniform weights and identical modification costs, all three algorithms are proven to yield optimal solutions and operate within O(n2) time complexity. Extensive computational experiments on standard benchmark datasets and randomly generated instances demonstrate that IFLP1 achieves the lowest CPU time and smallest unbalancing number, while IFLP3 yields superior performance in termsof total transfer cost and is recommended for practical applications
Optimization & Operations Research
Ali Shokri; Roman Rafig Maharramov; Mutallim Mirzaahmed Mutallimov; Elshan Giyas Hashimov; lkin Aladdin Maharramov
Abstract
In this paper, we address the problem of covering a given bounded domain in the plane using simple geometric figures. The proposed approach is based on a discretization of the domain, which leads to a corresponding discrete optimization problem. To solve this problem, we introduce a novel iterative algorithm ...
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In this paper, we address the problem of covering a given bounded domain in the plane using simple geometric figures. The proposed approach is based on a discretization of the domain, which leads to a corresponding discrete optimization problem. To solve this problem, we introduce a novel iterative algorithm that minimizes a given objective function by generating successive neighboring nodal points. As the covering elements, circular sectors with centers located outside the domain are considered. The objective is to determine the locations of the sector centers and their radii in such a way that the entire domain is completely covered, while the ratio of the total area of the covering sectors to the area of the domain is minimized. Finally, the algorithm is demonstrated on a representative example, and the resulting coverings are illustrated.
Optimization & Operations Research
Niousha Zeidyahyaee; Sajjad Shokouhyar; Alireza Motameni
Abstract
This study develops a mathematically informed optimization framework for decision-making in reverse supply chain management, with an application to Apple’s MacBook product line. The proposed framework integrates Failure Mode and Effects Analysis (FMEA) with deep learning, based sentiment analysis ...
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This study develops a mathematically informed optimization framework for decision-making in reverse supply chain management, with an application to Apple’s MacBook product line. The proposed framework integrates Failure Mode and Effects Analysis (FMEA) with deep learning, based sentiment analysis in a multi-stage structure designed to quantify risk factors and predict consumer-driven outcomes. The dataset consists of 91 days of Twitter user feedback on Apple notebooks, processed using supervised learning algorithms to extract sentiment scores and thematic indicators of product performance. The analysis identifies “power and battery” and “storage” as the most critical components contributing to user dissatisfaction and elevated risk severity. These data-driven insights are incorporated into an optimization model that supports decisions on product recycling, refurbishment, and reuse. The hybrid framework enhances decision stability and accuracy compared with conventional reverse logistics models, while improving operational efficiency and environmental performance. The results demonstrate the model’s suitability as a scalable, machine-learning-supported optimization tool for reverse supply chain systems.
Optimization & Operations Research
Alireza Ezzati; Mahdi Mollazadeh; Sadegh Moodi; Morteza Araghi; Hossein Mahdizadeh
Abstract
Homogeneous second-order Aw-Rascle-type models have demonstrated greater effectiveness than their non-homogeneous counterparts in traffic flow modeling. This study addresses the numerical solution of hyperbolic conservation laws governing these models by coupling the second-order HLLE Riemann solver, ...
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Homogeneous second-order Aw-Rascle-type models have demonstrated greater effectiveness than their non-homogeneous counterparts in traffic flow modeling. This study addresses the numerical solution of hyperbolic conservation laws governing these models by coupling the second-order HLLE Riemann solver, a Godunov-type finite volume approach, with the wave propagation algorithm. A novel wave-speed selection strategy is proposed by comparing characteristic velocities with Roe speeds, yielding solutions with guaranteed positive density and speed. The proposed IWP-HLLE method is applied to simulate shock, rarefaction, and contact discontinuity waves under homogeneous long-road conditions, eliminating the influence of external source terms and ensuring the homogeneity of the governing hyperbolic equations. Its performance is benchmarked against the MacCormack scheme supplemented by two standard stabilization techniques, namely artificial viscosity (AV) and central differencing (CD). Spatiotemporal distributions and density profiles are examined across four representative traffic scenarios: free flow, congested traffic flow, queue dissolution, and congested flow with non-equilibrium velocity and uniform density. The results demonstrate that the IWP-HLLE approach substantially suppresses numerical oscillations compared to both AV and CD methods while maintaining stability across all test cases.
Optimization & Operations Research
Mohammad Mahyar Amiri Chimeh; Babak Javadi
Abstract
Efficient layout design in healthcare facilities is critical for operational effectiveness and patient care. This study addresses the healthcare facility layout problem using a multi-objective optimization approach. We propose a novel methodology based on graph theory, specifically planar adjacency graphs, ...
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Efficient layout design in healthcare facilities is critical for operational effectiveness and patient care. This study addresses the healthcare facility layout problem using a multi-objective optimization approach. We propose a novel methodology based on graph theory, specifically planar adjacency graphs, to generate and evaluate department layouts. Nodes in the graph represent departments, while weighted edges represent the desired closeness based on patient flow and functional relationships. We introduce five strategies based on different weightings of these objectives and evaluate them using a real-world hospital case study. Our results show that a hybrid strategy, prioritizing patient flow while incorporating departmental relationships, yields the optimal layout. This approach provides a systematic and data-driven framework for healthcare planners to create efficient layouts that enhance workflow, reduce travel distances, and improve overall service quality.
Optimization & Operations Research
Tahereh Azizpour; Majid Yarahmadi
Abstract
In this paper, we introduce a new continuous quantum evolutionary optimization algorithm designed for optimizing nonlinear convex functions, non-convex functions, and efficiency evaluation problems using quantum computing principles. Traditional quantum evolutionary algorithms have primarily been ...
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In this paper, we introduce a new continuous quantum evolutionary optimization algorithm designed for optimizing nonlinear convex functions, non-convex functions, and efficiency evaluation problems using quantum computing principles. Traditional quantum evolutionary algorithms have primarily been implemented for discrete and binary decision variables. The proposed method has been designed as a novel continuous quantum evolutionary optimization algorithm tailored to problems with continuous decision variables. To assess the algorithm’s performance, several numerical experiments are conducted, and the simulated results are compared with the Grey Wolf Optimizer and Magnet Fish Optimization search algorithm. The simulation results indicate that the proposed algorithm can approximate the optimal solution more accurately than the two compared algorithms.
Optimization & Operations Research
Farzad Rahpeymaii; Majid Rostami
Abstract
The conjugate gradient ({CG}) method is one of the simplest and most widely used approaches for unconstrained optimization, and our focus is on two-dimensional problems with numerous practical applications. We devise three hybrid {CG} methods in which the hybrid parameter is constructed from the Barzilai–Borwein ...
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The conjugate gradient ({CG}) method is one of the simplest and most widely used approaches for unconstrained optimization, and our focus is on two-dimensional problems with numerous practical applications. We devise three hybrid {CG} methods in which the hybrid parameter is constructed from the Barzilai–Borwein process, and in these hybrids, the weaknesses of each constituent method are mitigated by the strengths of the others. The conjugate gradient parameter is formed as a linear combination of two well-known CG parameters, blended by a scalar, enabling our new methods to solve the targeted problems efficiently. Under mild assumptions, we establish the descent property of the generated directions and prove the global convergence of the hybrid schemes. Numerical experiments on ten practical examples indicate that the proposed hybrid {CG} methods outperform standard {CG} methods for two-dimensional unconstrained optimization.
Optimization & Operations Research
Maedeh Shahabi; Freydoon Rahbarnia
Abstract
In irregular coloring, each vertex is labeled with a unique color code, a tuple consisting of its assigned color and the number of neighbors in each color class. This work proposes a local search algorithm as a metaheuristic approach to the irregular face coloring problem in planar graphs, ...
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In irregular coloring, each vertex is labeled with a unique color code, a tuple consisting of its assigned color and the number of neighbors in each color class. This work proposes a local search algorithm as a metaheuristic approach to the irregular face coloring problem in planar graphs, with a particular focus on fullerene molecular structures. Additionally, a linear programming model is utilized to validate the performance of the proposed algorithm. The methodology demonstrates efficient solutions for irregular coloring in fullerene graphs, bridging combinatorial optimization with practical applications in chemistry and materials science.
Optimization & Operations Research
Zahra Mohammadhashemi; Khatere Ghorbani-Moghadam; Safora Allahy; Sepehr Ghazinoory
Abstract
This study employs a two-stage analytical framework to assess efficiency, comprising a standard SBM evaluation and a novel weighted SBM model. Unlike conventional SBM-DEA applications, the proposed weighted model uses an enhanced slack-based mechanism that prioritizes strategic inputs (R&D investment, ...
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This study employs a two-stage analytical framework to assess efficiency, comprising a standard SBM evaluation and a novel weighted SBM model. Unlike conventional SBM-DEA applications, the proposed weighted model uses an enhanced slack-based mechanism that prioritizes strategic inputs (R&D investment, number of employees, and funding) and clearly distinguishes input redundancies (e.g., excessive R&D expenditure or staffing) from output deficiencies (e.g., weak revenue performance). This separation yields more precise and targeted diagnostic insights. Additionally, the model incorporates sector-specific efficiency differentiation, supported by ANOVA, enabling assessment of cross-firm inefficiencies and their statistical significance in terms of systemic versus sector-specific phenomena. The methodology is applied to a distinctive panel of 146 technology-based firms (TBFs) in Iranian science and technology parks from 2021–2023, a context rarely explored with DEA in emerging markets. The study combines quantitative DEA results from both models with qualitative follow-up analyses of factors such as marketing strategies, private investment initiatives, and certification achievements, producing a robust mixed-methods approach and actionable policy recommendations. A comparative analysis reveals that fully efficient firms comprise 2.7\% under the unweighted model and 3.4\% under the weighted model, indicating that weighting yields a small, non-significant change in overall efficiency. About 97.3\% of firms display efficiency gaps due to input redundancies or output shortfalls. Sectoral tests show no statistically significant inter-sector differences, pointing to systemic inefficiencies across industries. Qualitative insights identify firm-level success factors—effective marketing, certification, and investment strategies—that align with the detected inefficiency patterns. Collectively, these findings offer measurable strategies for improvement, such as reducing redundant investment and enhancing revenue-generation mechanisms, to inform evidence-based policy aimed at the commercialization and growth of TBFs in emerging markets.
Optimization & Operations Research
Mohamed Kouadria; Halim Zeghdoudi; Mohammed El-Arbi Khalfallah
Abstract
This study proposes the New Two-Parameter Weibull–Lindley Distribution (NTPWLD), a flexible lifetime model generated through a transformation of a one-parameter baseline survival function. Owing to its general structure, the NTPWLD accommodates diverse hazard rate shapes, including increasing, ...
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This study proposes the New Two-Parameter Weibull–Lindley Distribution (NTPWLD), a flexible lifetime model generated through a transformation of a one-parameter baseline survival function. Owing to its general structure, the NTPWLD accommodates diverse hazard rate shapes, including increasing, decreasing, and bathtub forms, and captures both light- and heavy-tailed behaviors relevant to survival analysis, engineering reliability, and biomedical applications. The work provides a full mathematical treatment of the distribution, deriving closed-form expressions for its density, distribution, survival, hazard, and quantile functions, along with ordinary and incomplete moments, the moment generating function, mean deviations, and Rényi entropy. Several reliability measures, such as mean residual life and stress–strength reliability, are also obtained. Parameter estimation is examined under various inferential approaches, with particular focus on maximum likelihood estimation. A Monte Carlo simulation study shows that the maximum likelihood estimator performs well across settings, displaying low bias, stability, and consistency. To incorporate uncertainty in lifetime data, fuzzy reliability measures are constructed using Zadeh’s extension principle and α-cut techniques. Applications to two real datasets demonstrate that the NTPWLD provides superior goodness-of-fit compared with several competing models based on AIC, BIC, AICC, and −2 log L, highlighting its practical value in both precise and fuzzy reliability environments.
Optimization & Operations Research
Jafar Pourmahmoud; Ahad Abbasi; Alireza Ghaffari-Hadigheh
Abstract
Data Envelopment Analysis (DEA) is a well-established methodology for assessing the efficiency of decision-making units. In complex systems comprising multiple interconnected subsections, Network DEA provides a structured framework for efficiency evaluation. However, ...
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Data Envelopment Analysis (DEA) is a well-established methodology for assessing the efficiency of decision-making units. In complex systems comprising multiple interconnected subsections, Network DEA provides a structured framework for efficiency evaluation. However, traditional DEA models rely on the assumption of deterministic data, which inadequately reflects the inherent uncertainty present in real-world scenarios. Traditional uncertainty-handling methods, such as fuzzy logic, stochastic models, and interval-based techniques, often fail when there is limited historical data and when expert opinions significantly influence the dataset. To address these limitations, this study introduces an uncertain network DEA model based on Liu’s uncertainty theory, facilitating a more accurate assessment of efficiency under conditions of data imprecision. The proposed model is designed for three interconnected subsections and is further extended into a generalized multi-stage framework, allowing it to adapt to increasingly complex systems. Its effectiveness and practical applicability are demonstrated through two numerical case studies in the banking industry, highlighting its capacity to support decision-making under uncertainty. The findings emphasize the model's potential to enhance efficiency evaluation methods, particularly in environments characterized by limited and uncertain data.
Optimization & Operations Research
Mahdi Dehghani Darmian
Abstract
This paper analyzes systems of linear first-order ordinary differential equations (ODEs) with parametric coefficients, a class of problems that arises in control theory, optimization, and applied mathematics. We introduce the notion of a comprehensive solution ...
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This paper analyzes systems of linear first-order ordinary differential equations (ODEs) with parametric coefficients, a class of problems that arises in control theory, optimization, and applied mathematics. We introduce the notion of a comprehensive solution system for such parametric ODEs, constructed using Gröbner systems from computer algebra. Our approach partitions the parameter space into finitely many cells and associates an explicit solution with each cell. Furthermore, we present an algorithm that computes a comprehensive solution system for any given parametric system. To address the computational challenges inherent in Gröbner systems, we adopt the GES algorithm, a parametric variant of Gaussian elimination, which eliminates the need for Gröbner bases. This method builds upon the LDS algorithm proposed in 2017. Both algorithms have been implemented in Maple, and we illustrate the structural framework of the main algorithm with a straightforward example. The results highlight the practicality and effectiveness of the proposed methods for solving parametric linear first-order ODE systems.
Optimization & Operations Research
Michael Oluwaseun Ayansiji; Friday Zinzendoff Okwonu
Abstract
Hyperparameter optimization (HPO) is essential for maximizing the performance of deep learning models. Traditional approaches, such as grid search and Bayesian Optimization (BO), are widely used but can be computationally expensive. We present Interpolation-Based Optimization (IBO), a ...
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Hyperparameter optimization (HPO) is essential for maximizing the performance of deep learning models. Traditional approaches, such as grid search and Bayesian Optimization (BO), are widely used but can be computationally expensive. We present Interpolation-Based Optimization (IBO), a novel framework that employs piecewise polynomial interpolation to estimate optimal hyperparameters from sparse evaluations efficiently. IBO achieves substantial computational savings by constructing deterministic interpolants with linear per-iteration complexity of O(n.d^3), in contrast to the cubic O(n^3) cost associated with BO. Empirical studies on the MNIST dataset show that IBO attains 98.0% accuracy with a 39% reduction in runtime (12 iterations vs. 18) and no statistically significant difference from BO, p = 0.12. In higher-dimensional, lower-cost settings, such as ResNet-18 on CIFAR-10, performance degrades, highlighting a trade-off between dimensionality and efficiency. More generally, IBO is well-suited for resource-constrained settings due to its simplicity, determinism, and computational efficiency. Future work will explore hybrid methods to address scalability problems and extend IBO to more complex modeling architectures, such as transformers.
Optimization & Operations Research
Gassan A.M.O. Farah; Abdulaziz Mukhtar; Kailash C. Patidar
Abstract
Malaria continues to represent a significant public health concern in Sudan, with cases rising over 40% from 2015 to 2020. This research investigates how climate change affects malaria transmission patterns using a mathematical model in an ordinary differential equation framework. ...
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Malaria continues to represent a significant public health concern in Sudan, with cases rising over 40% from 2015 to 2020. This research investigates how climate change affects malaria transmission patterns using a mathematical model in an ordinary differential equation framework. The analysis involves calculating the basic reproduction number and evaluating the system's qualitative properties to gain insights into disease dynamics. Additionally, a sensitivity analysis is conducted to evaluate how climatic conditions, e.g., rainfall and temperature, influence key model parameters. Statistical approaches are utilized to estimate parameters and calibrate the model using empirical data from Sudan, ensuring consistency between the model and observed trends. Numerical simulations demonstrate the growing influence of climate variability on the spatial distribution of malaria vectors and the transmission progression over time. The study establishes a strong association between climatic changes and the exacerbation of malaria prevalence in Sudan. These findings emphasize the urgent need for climate-adaptive strategies, including improved vector control, strengthened surveillance systems, and climate-resilient public health interventions, to address the increased risks posed by changing environmental conditions. The research provides valuable insights to inform evidence-based policies aimed at reducing malaria transmission in Sudan and other regions that are experiencing similar challenges due to climate change.
Optimization & Operations Research
Narjes Amiri; Hadi Nasseri; Davood Darvishi Salokolaei
Abstract
This paper explores a specific category of optimization management models tailored for wireless communication systems. To enhance the efficiency of managing these systems, we introduce a fuzzy relation multi-objective programming approach. We define the concept of a feasible ...
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This paper explores a specific category of optimization management models tailored for wireless communication systems. To enhance the efficiency of managing these systems, we introduce a fuzzy relation multi-objective programming approach. We define the concept of a feasible index set and present a novel algorithm, termed the feasible index set algorithm, which is designed to determine the optimal lexicographic solution to the problem, demonstrating polynomial computational complexity. Previous studies have indicated that the emission base stations within wireless communication systems can be effectively modeled using a series of fuzzy relation inequalities through max-product composition. This topic is also addressed in our paper. Wireless communication is widely employed across various sectors, encompassing mobile communication and data transmission. In this framework, information is transmitted via electromagnetic waves generated by fixed emission base stations.
Optimization & Operations Research
Narjes Sabeghi
Abstract
A critical aspect of successful project management is ensuring that execution aligns with the baseline schedule. However, traditional project control methods often struggle to effectively address the uncertainties and deviations that can arise during project execution, leading ...
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A critical aspect of successful project management is ensuring that execution aligns with the baseline schedule. However, traditional project control methods often struggle to effectively address the uncertainties and deviations that can arise during project execution, leading to delays and inefficiencies. To tackle these challenges, this paper introduces a novel heuristic approach based on the Tabu Search (TS) algorithm for identifying discrete control points throughout the project life cycle. These control points enable proactive monitoring, timely deviation detection, and corrective actions, significantly minimizing project delays. Unlike traditional scheduling techniques, which can be rigid and reactive, our proposed method dynamically adjusts control points to enhance project oversight. Experimental results on benchmark instances from the Kolisch library demonstrate that our approach significantly reduces project delays, with up to 20% improvements compared to initial schedules in certain scenarios. These findings underscore the effectiveness of the TS algorithm in enhancing project control strategies, highlighting its potential applicability in real-world project management scenarios.
Optimization & Operations Research
Narges Hosseinzadeh; Elyas Shivanian; Saeid Abbasbandy
Abstract
This study employs the radial basis function-generated finite difference (RBF-FD) method to address high-dimensional elliptic differential equations under Dirichlet boundary conditions. The method utilizes polyharmonic spline functions (PHSs) combined with polynomials for approximation. ...
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This study employs the radial basis function-generated finite difference (RBF-FD) method to address high-dimensional elliptic differential equations under Dirichlet boundary conditions. The method utilizes polyharmonic spline functions (PHSs) combined with polynomials for approximation. A notable benefit of this approach is that PHSs do not require a shape parameter, simplifying implementation and enhancing numerical stability. The proposed method offers several advantages, including high accuracy, rapid computation, and adaptability to complex geometries and irregular node arrangements. It is particularly effective for high-dimensional problems, providing a mesh-free alternative that scales efficiently with increased complexity. Beyond scientific computing, the method is also applied to financial option pricing, where integro-differential equations are transformed into a series of second-order elliptic partial differential equations (PDEs). Numerical experiments demonstrate that the proposed algorithm significantly outperforms existing RBF-based approaches in both accuracy and efficiency. These strengths make it a robust tool for solving a wide range of PDEs in both regular and irregular domains.
Optimization & Operations Research
Maryam Yaghoubi; Fatemeh Dadmand
Abstract
Natural disasters, such as earthquakes, result in significant financial and human losses. Rescue operations play a crucial role in managing such crises. However, the lack of precise information and the damage or destruction of urban transportation routes ...
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Natural disasters, such as earthquakes, result in significant financial and human losses. Rescue operations play a crucial role in managing such crises. However, the lack of precise information and the damage or destruction of urban transportation routes following earthquakes introduces uncertainty into these operations. This study presents a multi-objective humanitarian logistics model that utilizes a mixed-integer nonlinear programming (MINLP) approach. The model considers the reliability of transportation routes after an earthquake, the standard response time for allocating personnel and relief equipment, and the coverage maximization. This model incorporates various uncertainties, including the reliability of the transportation network. Real data from the city of Gonabad, Iran, was used to evaluate the proposed model. The results and sensitivity analysis demonstrated that the model exhibits desirable performance.
Optimization & Operations Research
Ahmad Sharif
Abstract
In this study, we explore soliton solutions for the conformable time-fractional Boussinesq equation utilizing the three-wave method. To validate the precision of our findings, we discuss specific special cases by adjusting certain potential parameters and also present the ...
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In this study, we explore soliton solutions for the conformable time-fractional Boussinesq equation utilizing the three-wave method. To validate the precision of our findings, we discuss specific special cases by adjusting certain potential parameters and also present the graphical representations of our results. The results achieved in this research align closely with those from previous studies, demonstrating enhanced accuracy and simplicity. Given the extensive applications of this equation in particle physics, understanding its dynamics is crucial. Consequently, employing methods that encompass a broad spectrum of solutions is imperative. The versatility of this method in yielding diverse solutions is evident in the results we have obtained. The solutions derived in this paper are novel and offer greater precision compared to previous works.
Optimization & Operations Research
Majid Anjidani
Abstract
Designing dynamically stable controllers for a robot with 2r legs is challenging due to its complex hybrid dynamics (r>1). This paper proposes a technique to decompose the robot into r biped robots, where the influence of other robot parts on each biped can be modeled as external forces. This approach ...
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Designing dynamically stable controllers for a robot with 2r legs is challenging due to its complex hybrid dynamics (r>1). This paper proposes a technique to decompose the robot into r biped robots, where the influence of other robot parts on each biped can be modeled as external forces. This approach allows existing research on biped control to be applied to the quadruped robot. Time-invariant controllers, which typically ensure walking stability for planar point-footed bipeds, are selected for this purpose. For clarity, we focus on a planar point-footed quadruped for decomposition. We extend a recent reinforcement learning method to optimize these controller parameters for walking on slopes or under specific forces, while accounting for significant modeling errors in the quadruped. Simulation results demonstrate that our method achieves stable walking with the desired features and effectively compensates for modeling errors.
Optimization & Operations Research
Hadi Adib; Akbar Mirzapour Babajan; Beitollah Akbari Moghaddam; Roozbeh Balounejad Nouri
Abstract
This paper explores the resilience optimization of Iran's banking sector in the face of exchange rate shocks---critical macroeconomic disturbances with extensive consequences. We develop a multi-sector macro-dynamic stochastic general equilibrium model encompassing essential economic components, ...
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This paper explores the resilience optimization of Iran's banking sector in the face of exchange rate shocks---critical macroeconomic disturbances with extensive consequences. We develop a multi-sector macro-dynamic stochastic general equilibrium model encompassing essential economic components, including firms, government, central bank, and the banking sector. This framework facilitates the simulation of the macroeconomic environment and allows for a thorough analysis of the banking sector's adaptive responses to exchange rate fluctuations. Our findings reveal optimization strategies that effectively mitigate the adverse effects of these shocks while maintaining equilibrium in the broader economy. Specifically, we discover that while an initial positive exchange rate shock can enhance banking sector performance, it ultimately triggers inflationary pressures that threaten profitability and operational stability in the medium to long term.
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