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  • S. Fardi Zarnagh, M. Nouri *, S. A. Mousavi Ghasemi
    One of the main concerns of the structural engineers is the necessity of using discrete variables in optimum design of structural systems in which a predefined set of design sections are considered to be used during the optimization process. Most of the recently developed metaheuristic algorithms are capable of dealing with continuous design variables; however, the capability of these algorithms in dealing with a problem of discrete variables is one of the major concerns in optimization field. For this purpose, the applicability of the recently developed Coot optimization algorithm is evaluated in this paper in dealing with optimum design of 4 truss structures with 10, 25, 52 and 72 bars in which the design sections are predefined through discrete variables. Meanwhile, the improved Coot optimization algorithm is also proposed to increase the overall performance of the standard Coot algorithm in which the random movements in the main loop of the algorithm are replaced by Levy flight which denotes on a stochastic process with step length determined by levy distribution. The capability of the standard Coot and the proposed improved Coot optimization algorithms is investigated regarding the considered truss problems while the best and statistical results demonstrate the overall capability of the improved algorithm in dealing with discrete design scheme in truss optimization problems.
    Keywords: Truss Structure, Coot Optimization Algorithm, Levy Flight, Metaheuristic Algorithm, Optimization
  • A. Zaerreza, M. Mohammadi, S. Gholizadeh *
    The design of lined channels is expressed as a constrained optimization problem, where the primary objective is to minimize construction costs. To achieve universal applicability of the results, a dimensionless form of the cost function is employed for the lined channel optimization problem. This ensures that the optimized design can be adapted to any specific scenario. To tackle the optimization problem, an advanced version of the Jaya (AJaya) algorithm is introduced. The performance of the AJaya algorithm is assessed using two benchmark structural optimization design examples. Subsequently, AJaya is applied to the optimum design of the lined channel, using dimensionless design variables and the objective function. The optimization process for lined channels must account for various cross-sectional shapes and an extensive range of design variable combinations. As a solution, artificial neural networks are employed to predict the optimal dimensionless design parameters. The results demonstrate that this methodology is an effective tool for the optimal design of lined channels.
    Keywords: Lined Channel, Optimization, JAYA Algorithm, Metaheuristic, Artificial Neural Network
  • F. Eghbal, M. Ehsanifar *, M. Mirhosseini, H. Mazaheri
    Analyzing financial ratios over consecutive years is beneficial for evaluating the financial performance of construction companies. However, such an analysis can be tedious due to the vast number of the ratios. Therefore, developing an expert system based on artificial intelligence algorithms to identify and predict factors influencing the construction companies' financial performance is essential. To this end, a hybrid model based on Genetic Algorithm (GA) and Adaptive Neuro-Fuzzy Inference System (ANFIS) was introduced in this research to predict the financial performance of construction companies in Iran. This research is applied as descriptive and in terms of methodology well developed; also conducted cross-sectionally. The statistical population included all active construction companies in the construction sector in Tehran. Due to time and resource constraints, a random sampling technique was used. A questionnaire was utilized for data collection and data analysis, factor analysis methods and neuro-fuzzy system combined with GA were employed. The ANFIS combined with GA can evaluate the construction companies' financial performance with the minimum error. The findings ultimately resulted development of a model that forecasts the financial performance of Iranian construction companies, allowing them to concentrate on factors that improve financial performance.
    Keywords: Financial Performance, Iranian Construction Companies, Genetic Algorithm, Adaptive Neuro-Fuzzy Inference System
  • F. Kaveh, M. Karbasian *, O. Boyer, H. Shirouyehzad
    World’s growing population and the frequency of natural disasters as well as managing disasters and continuous improvements in methods and strategies adopted has become an essential global concern. The current paper introduces a two-stage mathematical model designed to minimize operational costs while improving service delivery through incorporating smart city infrastructure. In pre-disaster phase, multiple suppliers, warehouses, and regions are considered, along with such key objectives as allocating suppliers, locating warehouses, managing inventory in addition to identifying regions for smart city development. Post-disaster, the model focuses on routing drone flights for collecting data, distributing relief items, and establishing make-shift relief centers. The second stage comprises both ground and aerial vehicles for logistics and data collection. To handle uncertainty and the model's dual-level nature, a distributionally robust optimization (DRO) approach is exploited. Sensitivity analysis of a numerical example points to the fact that storage costs, demand correlation, and average demand significantly affect total costs. So much so that an increase in these parameters brings about an increase in total operation costs. First-stage decisions yielded 12 efficient solutions for the second-stage model. The obtained results indicate that for reducing shortages in the humanitarian logistics network, there should happen an increase in total costs. The increase should be directed towards using more drones for distributing aid items and gathering information. Given the NP-hard nature of the model, hybrid algorithms were employed, which outperformed exact methods in terms of efficiency. In a real-world case study in Isfahan Province, seven cities (Fereydunshahr, Kashan, Najafabad, Ardestan, Varzaneh, Isfahan, and Shahreza) were identified as smart city infrastructure sites. Five warehouses in Naeen, Kashan, Fereydan, Isfahan, and Shahreza were selected for disaster relief logistics operations. Findings highlight the trade-off between cost and service level, emphasizing the importance of drones in reducing shortages and enhancing disaster response efficiency. Managerial insights highlight the cost-effectiveness of IoT deployment in reducing demand uncertainty and enhancing response efficiency. The proposed framework offers a practical and scalable solution for disaster preparedness and post-disaster management in diverse urban contexts.
    Keywords: Humanitarian Logistics Network, Distributed Robust Optimization, Smart City, Locating Warehouses, Hybrid Algorithm, Managing Inventory, DRO
  • A. M. Golmohammadi *, F. Hajizadeh Ebrahimi, R. Sahraeian, H. Abedsoltan
    The present study aims to optimize a green closed-loop supply chain (GCLSC) network while minimizing carbon emissions and maximizing product shipments. The proposed model incorporates unbalanced factors such as capacity level, input current limit to each distribution center, and facility environmental level. We considered emission control levels for locating distribution centers as well as the reduction in CO2 emissions at all levels of the supply chain. Moreover, all types of expenditures in a closed-loop supply chain including manufacturing, distribution, recovery, assembly, and disassembly in the model are considered. Consideration of these assumptions closes this study to reality and makes this study an innovative one. Moreover, to account for demand uncertainty, a robust optimization method, the Bertsimas and Sim optimization approach, is used. The Epsilon Constraint Method and non-dominated sorting genetic algorithm II (NSGA-II) were employed to solve multi-objective functions with unknown demand, and the genetic algorithm is used to solve large-scale problems. The results indicate that the proposed approach achieves the objectives of reducing costs, minimizing environmental impact. Moreover, the NSGA-II algorithm outperforms other solution methods in terms of the number and diversity of solutions on the Pareto front. Specifically, the Pareto boundary obtained by NSGA-II contains a larger number of solutions compared to the different types of epsilon constraint methods. Additionally, the diversity of solutions on the Pareto front is higher in the NSGA-II algorithm, indicating a more well-spread and diverse set of solutions. These findings highlight the superiority of NSGA-II as a powerful and effective algorithm for multi-objective optimization problems in green closed-loop supply chain networks.
    Keywords: Green Supply Chain Management, Multi-Objective, Genetic Algorithm, Epsilon Method
  • A. Tamanaei, F. Kowsary, S. Sahamifar, F. Samadi *
    This paper presents the numerical multi-objective optimization of staggered tube banks in cross-flow using neural networks and genetic algorithm. The objective is to determine the optimal dimensionless transverse and longitudinal pitches that establish a proper compromise between heat transfer enhancement and pressure drop minimization across a wide range of inlet Reynolds numbers (1,000–50,000). Tube banks simulations are performed for randomly selected pairs of design points to generate data on Nusselt number and friction factor. This dataset is used to train neural networks, which predict heat transfer and pressure drop characteristics as functions of dimensionless pitches. Appropriate objective functions are defined using trained neural networks and integrated into Genetic Algorithm to efficiently identify Pareto-optimal solutions. Results indicate that Reynolds number has a negligible effect on the Pareto front, as the optimal trade-offs between heat transfer and pressure drop remain consistent across different flow regimes. The best point on the Pareto front, defined as the solution with the minimum distance to the utopia point, exhibits dimensionless longitudinal and transverse pitches of approximately 0.90 and 1.30, respectively, regardless of the Reynolds number. Additionally, the study confirms that compact tube banks with dimensionless longitudinal pitches smaller than 1.0, often excluded in experimental and numerical studies, can be successfully simulated and optimized using the proposed framework. The findings provide practical guidelines for designing high-efficiency staggered tube banks and demonstrate a computationally efficient approach to optimize heat exchanger configurations without relying on empirical correlations.
    Keywords: Cross-Flow Staggered Tube Banks, Multi-Objective Optimization, Heat Transfer, Genetic Algorithm, Machine Learning
  • امیرحسین عودی، صالحه علامی، یگانه داودبیگی*
    در صنعت نفت شناخت خواص فیزیکی و شیمیایی به عنوان عنصر کلیدی در توسعه و کنترل این صنعت است. دانسیته از جمله خواص مهم برش های نفتی می باشد که پژوهش حاضر باهدف دستیابی به یک مدل نیمه تجربی ساده برای دانسیته مایع هیدروکربن های سنگین در محدوده فشار و دما های مشخص انجام شد. دانسیته هشت هیدروکربن سنگین بعد از گرد آوری اطلاعات تجربی و خواص بحرانی با استفاده از معادلات حالت (EOS) سوآو - ردلیچ - وانگ (SRK) و پنگ رابینسون (PR) محاسبه و تابع هدف بر اساس الگوریتم بهینه سازی ازدحام ذرات (PSO) کمینه شد. نتایج حاصل از مقادیر مربوط به ضرایب ثابت مدل نیمه تجربی به ترتیب گویای نسبت عکس و مستقیم دانسیته با دما و فشار است. از طرفی مقدار کم تر میانگین نسبی خطا برای مدل نیمه تجربی به دست آمده در مقایسه با معادلات حالت SRK و PR برای هیدروکربن های با تعداد کربن کم تر حاصل شد. همچنین در محدوده دمایی پایین تر این مدل عملکرد و تطابق بهتری با داده های تجربی دارد و به طور کلی رابطه حاصل در عین سادگی دارای دقت و انعطاف بالایی است. نتایج نشان داد که میانگین درصد خطای نسبی حاصل از محاسبه دانسیته مایع 8 هیدروکربن سنگین طبق مدل نیمه تجربی ارائه شده 1/18 درصد می باشد که 17/33 درصد از معادله حالت  SRK کمتر و 7/67 درصد از معادله حالت PR بهتر می باشد.
    کلید واژگان: هیدروکربن سنگین، الگوریتم ازدحام ذرات، خواص فیزیکی و شیمایی، دانسیته مایع، معادلات حالت
    Amirhossein Oudi, Salehe Allami, Yegane Davoodbeygi *
    In the oil industry, knowing the physical and chemical properties is a key element in the development and control of this industry. The present research was conducted with the aim of obtaining a simple semi-empirical correlation for the liquid density of heavy hydrocarbons in certain pressure and temperature range. Density of eight heavy hydrocarbons after collecting experimental data and critical properties using equations of state SRK and PR Calculation and objective function based on optimization algorithm PSO It was minimized. The results obtained from the values related to the constant coefficients of the semi-empirical correlation are indicative of the inverse and direct relationship of density with temperature and pressure, respectively. On the other hand, a lower value of relative average error for the obtained semi-empirical correlation compared to SRK and PR equations of state was obtained for hydrocarbons with less carbon number. Also, in the lower temperature range, this correlation has a better performance and agreement with the experimental data, and in general, the resulting relationship is simple and has high accuracy and flexibility. The results showed that the average percentage of relative error resulting from calculating the liquid density of 8 heavy hydrocarbons according to the presented semi-empirical model is 1.18%, which is 17.33% less than the SRK equation of state and 7.67% better than the PR equation of state.
    Keywords: Heavy Hydrocarbons, PSO Algorithm, Physical, Chemical Properties, Liquid Density, Equation Of State
  • فرشاد طیاری*
    در نوشتار حاضر به بررسی تاثیر الگوریتم های فراابتکاری در طراحی بهینه ی شمع های سازه ی نگهبان ایستگاه شماره ی 3 متروی تبریز پرداخته شده است. برای این منظور، ابتدا ترانشه ی موردنظر در نرم افزار اپن سیس مدل سازی و فرآیند گام به گام خاک برداری آن، مطابق روند اجرایی، شبیه سازی شده است. چهار الگوریتم فراابتکاری متداول، یعنی الگوریتم های مبتنی بر جغرافیای زیستی، ژنتیک، ازدحام ذرات، و زنبورعسل برای طراحی بهینه استفاده شده اند، تا علاوه بر مقایسه ی عملکرد هر یک در حل مسئله ی مذکور، احتمال دستیابی به بهترین پاسخ افزایش یابد. نتایج به دست آمده حاکی از عملکرد بسیار خوب الگوریتم ژنتیک نسبت به سایر الگوریتم های استفاده شده در دستیابی به طرح بهینه بوده است. به منظور بررسی بهتر، توزیع تنش خاک در اطراف سازه ی نگهبان و همچنین تغییرشکل المان های شمع بررسی شده و نتایج نشان داده اند که استفاده از مهار متقابل جهت ایجاد تعادل تغییرشکل های قسمت فوقانی و تحتانی شمع ها و کاهش عمق مدفون المان های شمع ضروری به نظر می رسد.
    کلید واژگان: الگوریتم فراابتکاری، طراحی بهینه، ترانشه ی عمیق، سازه ی نگهبان، شمع های درجا
    Farshad Taiyari *
    The effectiveness of the application of metaheuristic algorithms in the optimal design of retaining structures is investigated in this paper. For this purpose, an ongoing Tabriz metro station project with a deep excavation pit is selected here as a case study. The retaining system of the project consists of secant pile walls supported by a layer of struts. The piles have a circular section consisting of reinforced concrete cores covered by steel sleeves, and the struts are made of steel rectangular hollow sections. A detailed finite element model is developed in the OpenSees platform, including all the construction processes, in order to perform static analyses. Four different metaheuristic algorithms, namely Genetic, Particle swarm optimization, Bee, and Biogeography-based algorithms, are chosen for the optimization problem. The pile external diameter, the steel tube stiffness, the number of longitudinal bars inside the concrete core and their diameters, the center-to-center spaces of the pile elements, the dimensions of structs and their center-to-center spaces, the location of the structs in depth and the buried depth of pile elements are selected as optimization variables. The total cost of the retaining system is considered as an objective function that should be minimized in the design space of the variables. For optimization purposes, an integration of the OpenSees software with the MATLAB platform is done to join the modeling space with the mentioned optimization algorithms. The number of iterations for each run is assumed to be 400, which is also considered a termination criterion. The optimization process is performed 50 times, and the best response is reported here. The results demonstrate an excellent performance of the Genetic algorithm in obtaining the optimum solution with respect to the other three considered algorithms. It exhibits a proper standard deviation and convergence rate in producing the optimum response. It is shown that the soil stress is increased in the depth where struts are installed, while they are reduced near the ground level, where the deflection of piles creates an active situation for the soil. This is true considering the results of all algorithms. Proceeding with the excavation phase increases the soil stress as well as the pile deformation. It can also be obtained that providing a layer of strut seems necessary for reducing pile movements as well as their buried depth.
    Keywords: Metaheuristic Algorithm, Optimal Design, Deep Excavation, Pile Wall Retaining System
  • Faezeh Sadat Mozneb, Kambiz Rahbar*, Parvaneh Asghari, Parand Akhlaghi

    Efficient partitioning of the atomic space among parallel FPGAs is crucial for accelerating molecular simulations. Existing research has primarily focused on uniform partitioning, assuming a homogeneous distribution of atoms. However, in scenarios with non-uniform atomic distributions, these approaches may lead to suboptimal performance. This study investigates the impact of non-uniform atom distributions on molecular simulation performance across parallel FPGAs. We propose a novel space partitioning scheme that optimizes the distribution of atomic space among FPGAs, taking into account the spatial heterogeneity of atoms. Our evaluation demonstrates that the proposed scheme consistently outperforms uniform partitioning in terms of simulation speed across various spatial dimensions and atom counts, particularly in scenarios with non-uniform atom distributions.

    Keywords: Molecular Simulation, Acceleration, Parallelization, Greedy Algorithm
  • Amit Kumar Rajput *, Jagdeep Singh Lather

    This paper presents a power management and control strategy for a residential DC microgrid (DCMG) incorporating photovoltaic (PV) systems, fuel cells (FCs), and a hybrid energy storage system (HESS). The fluctuations in the DC bus voltage, arising from intermittent PV generation and variable load conditions, are mitigated by the HESS, which comprises both batteries and supercapacitors (SCs). This control strategy adopts, batteries to handle slow-frequency power surges, whereas SCs are employed to manage rapid frequency fluctuations effectively. The proposed controllers are optimized using an evolution-based Genetic Algorithm (GA), eliminating the need for extensive mathematical modeling of the system. Comparative analysis between the GA-tuned and conventionally tuned controllers is conducted based on performance metrics, including overshoot, undershoot, and settling time. The simulation results indicate that the proposed controller performs satisfactorily, achieving a maximum overshoot of 3.08%, a maximum undershoot of 2.95%, and a settling time of 44.5 ms. To further assess the efficacy and robustness of the controllers, they are subjected to disturbances in sensor readings and variations in system parameters within a range of ±25 % of their nominal values. Additionally, to validate the practical applicability of the proposed system, the simulation results are corroborated using a real-time FPGA-based simulator (OP 5700). 

    Keywords: Photovoltaic, Fuel Cell, Battery, Supercapacitor, Energy Management, Genetic Algorithm
  • Abdolsalam Ghaderi *, Azade Modarres, Zahra Hosseinzadeh Bandbon
    This study addresses an integrated problem of hierarchical facility location and network design, which involves multiple decisions about the opening of facilities and network links at various levels. We introduce a novel multi-period model that integrates these problems, taking into account budgetary constraints and addressing the specific challenge of optimizing hierarchical upgrades for urban centers and transportation network links within each time period. The aim is to determine the optimal upgrade levels for urban centers and transportation network links in each time period, subject to a predefined budget. The proposed model is formulated as a mixed-integer linear programming problem. To solve the developed model, we employ a heuristic algorithm that combines simulated annealing with different neighborhood structures and fix-and-optimize strategies. The efficiency of the proposed algorithm is demonstrated through various instances, showing superior performance compared to the CPLEX solver, especially for larger problem instances. Furthermore, we illustrate the practical utility of this model in real-world decision-making processes, underscoring its efficacy. By addressing these factors, the proposed model provides valuable insights for organizational managers and planners.
    Keywords: Facility Location, Network Design, Hierarchical, Fix, Optimize Algorithm, Simulated Annealing
  • Reza Aalikhani, Mohammad Fathian *, Mohammad Reza Rasouli
    Cloud Manufacturing (CMfg) enables flexible and customized manufacturing services through dynamic service composition. However, achieving optimal service composition remains challenging due to the need to meet complex Quality of Service (QoS) requirements, including cost, time, quality, and resource workload balance. Notably, previous studies on service composition models have rarely considered workload balancing as part of their QoS criteria, which is critical for maintaining efficient and sustainable resource use. This study addresses this gap by presenting an advanced service composition model that integrates workload balance as an essential QoS metric alongside traditional factors like composite service quality, time, and cost. To further support optimization, the Simulated Annealing (SA) and Tabu Search (TS) algorithms are enhanced with a novel shaking mechanism designed to expand the search space and mitigate premature convergence risks common in metaheuristics. Experimental evaluations conducted on an OR-Library dataset confirm that the enhanced SA algorithm achieves up to a 25% improvement in the fitness function and a 7% reduction in computational time, while the improved TS algorithm achieves a 2% reduction in the fitness function and a 21% decrease in computational time. These findings highlight the model's potential to enhance CMfg service composition efficiency, offering substantial performance benefits over traditional methods. The core contributions of this study include the development of a workload-integrated service composition model and enhancements to SA and TS algorithms for effective problem-solving within this framework.
    Keywords: Cloud Manufacturing, Optimal Service Composition, Metaheuristic Algorithm, Improved SA, Improved TS
  • Hazhir Dousti, Mehdi Ahmadi Jirdehi *
    As energy demand surges due to technological advancements and population growth, optimizing energy supply networks becomes critical. This study presents a novel approach to intelligent energy management in microgrids that incorporates renewable resources and electric vehicle (EV) charging stations. The primary innovation lies in the simultaneous application of the Firefly algorithm and Monte Carlo method to enhance optimization speed and reduce operational costs, a strategy not previously explored in the literature. Despite existing research on microgrid management, significant gaps remain, particularly regarding the integration of EV charging infrastructure without active vehicle participation and the use of fuel cells as energy storage solutions. This paper addresses these gaps by proposing a framework that allows for future consumer integration while minimizing risks associated with operational uncertainties. Key findings indicate that utilizing the Firefly algorithm significantly outperforms traditional Particle Swarm Optimization (PSO) methods in identifying optimal solutions for energy management. The results demonstrate a marked reduction in operational costs over a 24-hour period while ensuring reliability in energy supply. Furthermore, the study establishes a robust foundation for transforming passive distribution systems into active ones, aligning with smart grid concepts.
    Keywords: Energy Management, Microgrid, Renewable Resources, Firefly Algorithm, Electric Vehicle Charging Station
  • علی محقر*، قاسم مختاری، محمدحسن ملکی، سید جمال الدین حسینی
    هدف

    تعیین اندازه انباشته برای تعیین میزان تولید مجموعه ای از قطعات و محصولات تولیدی و برش مواد خام برای تعیین الگوی برش مواد خام و تعداد استفاده از هر الگو برش استفاده می شود.

    روش شناسی پژوهش

     در این پژوهش یک مدل ریاضی یکپارچه برای تعیین اندازه انباشته و برش مواد خام با هزینه تاخیر در تحویل سفارشات، اضافه کاری و خرید مواد اولیه با هدف کمینه سازی هزینه های تولید معرفی شده است. در مدل پیشنهادی تعیین اندازه انباشته به صورت چند سطحی و چند دوره ای و برش مواد خام به صورت یک بعدی در نظر گرفته شده است. با توجه به NP-hard بودن مساله و عدم کارایی روش های دقیق در ابعاد بزرگ، یک روش ترکیبی مبتنی بر الگوریتم ژنتیک و حل دقیق با نرم افزار گمز ارایه شده است. در روش پیشنهادی، مساله موردنظر به دو قسمت تجزیه شده که در آن الگوهای برش مواد خام با الگوریتم ژنتیک مشخص می شود، سپس مساله با استفاده از نرم افزار گمز حل می شود. جهت بررسی عملکرد روش پیشنهادی چندین مساله نمونه معرفی و با روش حل پیشنهادی، حل شده است و نتایج آن با حل مساله با الگوریتم ژنتیک مقایسه شده است.

    یافته ها

    نتایج مساله حاکی از کارایی روش حل پیشنهادی است. میزان انحراف روش حل پیشنهادی از جواب دقیق برابر با %1.5 است و میزان بهبود روش پیشنهادی در ابعاد بزرگ نسبت به الگوریتم ژنتیک که روش دقیق قادر به حل آن نیست، برابر با %16.8 است. با افزایش %1 در هزینه های خرید میزان افزایش هزینه های مساله و هزینه های نگهداری به صورت میانگین %0.6 و %0.43 و با افزایش %1 در هزینه خرید، هزینه های مساله و نگهداری به صورت میانگین %0.73 و %0.23 افزایش می یابد.

    اصالت/ارزش افزوده علمی

     مساله تعیین اندازه انباشته و مساله برش مواد خام دو موضوع مهم در صنعت تولید هستند که در فرآیند تولید بسیاری از صنایع ازجمله صنعت مبلمان، کاغذ، آلومینیوم، صنایع فلزی و... کاربرد دارند.

    کلید واژگان: تعیین اندازه انباشته، برش یک بعدی مواد خام، جریمه دیرکرد، هزینه اضافه کاری، الگوریتم ژنتیک
    Ali Mohaghar *, Ghasem Mokhtari, Mohammadhasan Maleki, Seyed Jamaleddin Hosseini
    Purpose

    The problem of determining the lot sizing and cutting stock raw materials are two important issues in the production industry, which are used in the production process of many industries, including furniture, paper, aluminum, metal industries, etc. this research aims to determine the accumulated size in a multi-level and multi-period way and to cut the raw materials in a one-dimensional way.

    Methodology

    In this research, an integrated mathematical model has been introduced to determine the size of stockpiling and cutting of raw materials with the cost of delay in delivery of orders, overtime, and purchase of raw materials with the aim of minimizing production costs. Due to the NP-hard of the problem and the inefficiency of exact methods in large dimensions, a combined method based on a genetic algorithm and a precise solution with GAMS software is presented. In the proposed method, the desired problem is divided into two parts, in which a genetic algorithm determines the cutting patterns of raw materials, and then the problem is solved using GAMS software. In order to check the performance of the proposed method, several sample problems have been introduced and solved with the proposed solution method, and the results have been compared with problem-solving using the genetic algorithm.

    Findings

    The results of the problem indicate the effectiveness of the proposed solution method. The deviation of the proposed solution method from the exact solution is equal to 1.5%, and the improvement rate of the proposed method in large dimensions compared to the genetic algorithm, which the exact method is not able to solve, is equal to 16.8%. With an increase of 1 percent in purchase costs, the amount of rise in problem costs and maintenance costs is on average 0.6% and 0.43%, and with an increase of 1% in purchase cost, problem and maintenance costs are on average 0.73% and 0.23% increase.

    Originality/Value

     Determining the lot size is used to determine the amount of production of a set of parts and products, and cutting of raw materials is used to determine the cutting pattern of raw materials and the number of uses of each cutting pattern.

    Keywords: Determination Of Accumulated Size, One-Dimensional Cutting Of Raw Materials, Lateness Penalty, Overtime Cost, Genetic Algorithm
  • WAN ISMAIL IBRAHIM*, Nasiruddin Sadan, Noorlina Ramli, Mohd Riduwan Ghazali Riduwan Ghazali, Ilham Fuad

    Hydrokinetic energy harnessing has emerged as a promising renewable energy that utilizes the kinetic energy of moving water to generate electricity. Nevertheless, the variation and fluctuation of water velocity and turbulence flow in a river is a challenging issue, especially in designing a control system that can harness the maximum output power with high efficiency. Besides, the conventional Hill-climbing Search (HCS) MPPT algorithm has weaknesses, such as slow tracking time and producing high steady-state oscillation, which reduces efficiency. In this paper, the Variable-Step Hill Climbing Search (VS-HCS) MPPT algorithm is proposed to solve the limitation of the conventional HCS MPPT. The model of hydrokinetic energy harnessing is developed using MATLAB/Simulink. The system consists of a water turbine, permanent magnet synchronous generator (PMSG), passive rectifier, and DC-DC boost converter. The results show that the power output achieves a 28 % increase over the system without MPPT and exhibits the lowest energy losses with a loss percentage of 0.9 %.

    Keywords: Hill-Climbing Search, Hydrokinetic Energy Harnessing, MPPT Algorithm
  • شیما شیروانی، محمدرضا شهرکی*، فرانک حسین زاده سلجوقی

    مسائل مکان یابی هاب، یکی از مهم ترین شاخه های حمل ونقل هستند، که استفاده ی زیادی از آن ها در حوزه های راهبردی می شود. در شبکه ی توزیع، استفاده از هاب ها باعث کاهش هزینه های انتقال جریان در شبکه می شود. در پژوهش حاضر، یک مدل دوهدفه برای مسئله ی مکان یابی هاب سلسله مراتبی ارائه شده است. در پژوهش حاضر، با توجه به اهمیت مشکلات زیست محیطی در دنیای واقعی و نگرانی از افزایش آلودگی های مخرب زیست محیطی، علاوه بر بهبود و کاهش هزینه ها به مسائل زیست محیطی نیز پرداخته شده است. مدل ارائه شده به بررسی حمل ونقل چندحالته و ایجاد چند نوع سیستم حمل ونقل در یک هاب می پردازد. در ادامه، با استفاده از نرم افزار GAMS مسائل با ابعاد کوچک و نیز در ابعاد بزرگ با استفاده از الگوریتم های فراابتکاری ژنتیک، پارتویی قوی، و گرگ خاکستری حل و نتایج با یکدیگر مقایسه شده اند. با توجه به نتایج به دست آمده مشاهده شده است که الگوریتم های ارائه شده، کارایی مناسبی دارند.

    کلید واژگان: مسئله ی مکان یابی، هاب سلسله مراتبی، پایداری، زنجیره ی تامین، الگوریتم فراابتکاری
    Shima Shirvani, Mohammadreza Shahraki *, Faranak Hosseinzadeh Saljooghi

    Identifying the optimal location for facilities is a key strategic objective for companies striving to enhance their competitiveness. Managers carefully select facility locations to ensure they effectively meet demand and align with organizational goals. Given the impracticality of establishing direct communication between all points in a network, utilizing hub points within networks can result in significant cost savings. Hub location problems are one of the new and remarkable topics in industrial engineering and one of the most important branches of transportation which is widely used in strategic areas such as transportation systems, postal systems, and communication networks. The use of hubs in the distribution network reduces the costs of current transmission in the network and thus increases system efficiency. In summary, hubs are used in different places of the supply chain such as transferring from point to point, sorting, and switching. The problem of location-allocation of hub is one important problem that is common in many transportation systems. One of the important branches of hub area is hierarchical hub that has been considered by many researchers. In this research, a two-objective model for the hierarchical hub location problem is presented. Given the importance of real-world environmental problems and concerns about increasing destructive environmental pollution, in this study, in addition to reviewing and trying to improve and reduce costs, environmental problems and their improvement have been studied. The proposed model also examines multi-mode transport and creates several types of transport systems in one hub. In the following, smaller problems are solved by GAMS software and large-scale problems are solved by genetic, strong Pareto and gray wolf metaheuristic algorithms and the results are compared. The results of solving problems with different dimensions show the good performance of the proposed algorithm, so that by using this method in an acceptable time, a suitable quality answer can be obtained.

    Keywords: Location, Hierarchical Hub, Sustainability, Supply Chain, Metaheuristic Algorithm
  • حسن ناصح، حدیثه کریمایی*، محمد لسانی فدافن
    در این مقاله، طراحی بهینه چندموضوعی پیکربندی یک کپسول زیستی بازگشتی انجام می شود. در این فرآیند، کلیه موضوعات طراحی مرتبط با پیکربندی و اهداف سازه ای نظیر کمینه سازی تغییرشکل سازه، بیشینه سازی فرکانس طبیعی اول سازه و بیشینه سازی ضریب بار کمانشی سازه، مدنظر گرفته می شوند؛ بنابراین، موضوعات طراحی شامل هندسه، آیرودینامیک، مسیر، گرمایش و سازه انتخاب می شوند. در این راستا، با توجه به فضای طراحی تعریف شده توسط حدود مجاز متغیرهای هندسی کپسول زیستی، مدل های جایگزین با استفاده از روش ترکیبی کریگینگ-سطح پاسخ (RSM) استخراج می شود. پس از مدل سازی موضوعات طراحی و تهیه مدل های جایگزین، نقطه طراحی بهینه به کمک الگوریتم ژنتیک (GA) شناسایی می شود. برای حل این مسئله از چارچوب چندموضوعی همه در یک مرحله (AAO) استفاده می شود. رویکردهای بهینه سازی مسئله شامل کمینه سازی جرم، بیشینه سازی پارامتر پسای فاز برگشت (CDA)، بیشینه سازی راندمان حجمی، کمینه سازی ضریب بالستیک، بیشینه سازی پایداری استاتیک طولی، کمینه سازی تغییرشکل سازه ای، بیشینه سازی حجم داخلی، بیشینه سازی فرکانس طبیعی اول سازه و بیشینه سازی ضریب بار کمانشی سازه می باشند. نتایج نشان داد که استفاده از روش مدل جایگزین ترکیبی، دقت مدل های جایگزین در فرآیند بهینه سازی را به میزان قابل توجهی (دقت به بیش از 90 درصد می رسد) بهبود می بخشد. در پایان، نتایج پیکربندی های مختلف حاصل از روش حاضر با نتایج تست پروازی پیکربندی کپسول زیستی بومی مقایسه شد که مطابقت مطلوبی در کلیت پارامترهای مسیر پروازی کپسول ها را نشان داد.
    کلید واژگان: مدل جایگزین ترکیبی کریجینگ-سطح پاسخ، کپسول زیستی بازگشتی، طراحی بهینه چندموضوعی، الگوریتم ژنتیک
    Hassan Naseh, Hadiseh Karimaei *, Mohammad Lesani Fadafan
    In this paper, the multi-disciplinary design optimization of a re-entry bio-capsule configuration is performed. In this process, all design disciplines related to the configuration and structural objectives, such as minimizing the structure's deformation, maximizing the structure's first natural frequency, and maximizing the buckling load multiplier of the structure, are considered. Therefore, the design disciplines selected include geometry, aerodynamics, trajectory, heating, and structure. In this regard, considering the design space defined by the allowable limits of the geometric variables of the bio-capsule, surrogate models are extracted using the combinatorial Kriging-Response Surface Method (RSM). After modeling the design disciplines and preparing the surrogate models, the optimal design point is identified using the Genetic Algorithm (GA). To solve this problem, the multi-objective All-At-Once (AAO) framework is used. The optimization approaches of the problem include mass minimization, CDA parameter maximization, volumetric efficiency maximization, ballistic coefficient minimization, static longitudinal stability maximization, structural deformation minimization, internal volume maximization, first natural frequency maximization, and buckling load multiplier maximization. The results showed that using the combinatorial surrogate model method significantly improves the accuracy of the surrogate models in the optimization process (accuracy reaches more than 90%). Finally, the different configurations obtained by the present method were compared with the flight test results of the native bio-capsule configuration, which showed a favorable match in all flight path parameters of the capsules.
    Keywords: Combinatorial Kriging-RSM, Re-Entry Capsule, MDO, Genetic Algorithm
  • عباس مهدوی*
    خانواده توزیع های دوگانه لگ-تی به عنوان مدلی جدید برای مدل سازی داده های نامتقارن و کراندار در بازه (0،1) پیشنهاد شده است. برخی از خصوصیات و ویژگی های احتمالی آن و توسیع آن به مدل های آمیخته مورد بحث قرار گرفته اند. بر اساس نوعی نمایش تصادفی این توزیع ها، یک الگوریتم امید بیشینه سازی برای برآورد پارامترهای مدل پیشنهاد می شود. با توجه به کاربرد مدل های آمیخته در بخش بندی داده ها و همچنین با در نظر گرفتن مقادیر عددی پیکسل های تصویر در بازه (0،1)، آزمایشی را بر روی یک مجموعه تصویر طبیعی انجام می دهیم. نتایج به دست آمده، کارایی و سودمندی روش پیشنهادی را در مقایسه با توزیع های کراندار و غیر کراندار مرسوم نشان می دهد.
    کلید واژگان: الگوریتم امید بیشینه سازی، توزیع تی، توزیع واحد، مدل های آمیخته
    Abbas Mahdavi *
    This study introduces a novel unit double log-t distribution (DLT) specifically designed for values constrained between 0 and 1. We explore various characterizations and probabilistic properties of the DLT distributions, along with an extension to finite mixtures of these distributions. Utilizing a stochastic representation, we develop a practical expectation-maximization (EM) algorithm to compute the maximum likelihood estimates of the model parameters. Considering the application of mixture models in data clustering and taking into account the numerical values of image pixels within the (0,1) interval, we conduct an experiment on a natural image dataset. The results obtained demonstrate the effectiveness and utility of the proposed method in comparison to conventional bounded and unbounded distributions.
    Keywords: EM Algorithm, Mixture Model, T Student Distribution, Unit Distribution
  • در این مقاله، سه الگوریتم فراابتکاری شناخته شده شامل الگوریتم بازار بورس، الگوریتم تکامل پیچ درهم و الگوریتم زنبور ملکه به منظور ارائه سه الگوریتم تکاملی ترکیبی جدید با نام های EMA-QB، EMA-SCE و EMA-SCE-QB مورد بررسی قرار گرفته اند. به منظور تحلیل و ارزیابی کارایی و اثربخشی این الگوریتم های ترکیبی، عملکرد آن ها با الگوریتم های EMA، SCEو QB در حل 12 تابع محک با تعداد متغیرهای 10، 20، 30 و 50 مقایسه شده است. نتایج نشان می دهد که ترکیب الگوریتم ها منجر به بهبود عملکرد در جستجوی نقطه بهینه از نظر دقت و زمان شده است، به گونه ای که این بهبود با افزایش تعداد متغیرها ملموس تر می شود. در نهایت، مجموع زمان اجرای الگوریتم ها، کمینه مقدار توابع هدف، و تعداد تکرارهای لازم برای بهینه سازی تمامی توابع مورد بررسی، در قالب چهار نمودار برای هر تعداد متغیر به تصویر کشیده شده اند که نشان دهنده موفقیت الگوریتم های ترکیبی پیشنهادی است.

    کلید واژگان: الگوریتم ترکیبی، الگوریتم بازار بورس، الگوریتم زنبور ملکه، تکامل مختلط تصادفی
    Mina Salim*, Sima Hamedifar, Ali Asghar Lotfi

    In this paper, three popular algorithms, including the Exchange Market Algorithm (EMA), the Shuffled Complex Evolution (SCE) algorithm, and the Queen Bee (QB) algorithm, are considered to propose three new hybrid evolutionary algorithms named EMA-QB, EMA-SCE, and EMA-SCE-QB. Then, to analyze and validate the effectiveness and efficiency of these new algorithms, we compared their performance with the performance of EMA, SCE, and QB algorithms on 12 benchmark functions with 10, 20, 30, and 50 variables. It is deduced that hybridization has presented a better performance in optimum seeking from both time and accuracy points of view, which become more distinctive as the number of variables grows. Finally, the sum of run times, minimum value of cost functions, and the number of iterations obtained from the procedure of optimization of all functions using the considered algorithms are illustrated in four graphs for each number of variables, which prove the success of the proposed hybrid algorithms.

    Keywords: Hybrid Algorithm, Exchange Market Algorithm, Queen Bee Algorithm, Shuffled Complex Evolution
  • حسگرهای اینترنت اشیا و شبکه های حسگر بی سیم برای ارائه اطلاعات، مانند اطلاعات جاده، نیاز به مکان یابی دقیق دارند. نصب مکان یاب بر روی تمامی گره ها و سنسورها بسیار پرهزینه است و به همین دلیل مکان یابی غیرمستقیم انجام می شود. یکی از روش های مکان یابی کم هزینه، الگوریتم DVHop است. به دلیل سادگی الگوریتم DVHop، زمان اجرای آن زیاد نیست. به همین دلیل مصرف انرژی زیادی را مصرف نمی کند و یک الگوریتم کم هزینه محسوب می شود. یکی از چالش های روش DVHop خطای قابل توجه آن در مکان یابی است. برای حل این مشکل، در مقاله حاضر، یک سیستم مکان یاب هوشمند با استفاده از الگوریتم بهبود یافته DVHop و الگوریتم بهینه سازی وال برای تخمین موقعیت اشیا و حسگرها ارائه شده است. روش پیشنهادی دارای سه مرحله اصلی برای مکان یابی هوشمند است. آزمایش ها نشان داد که روش پیشنهادی نسبت به الگوریتم های PSO، WOA، GWO و HHO خطای مکان یابی کمتری دارد و به دلیل انحراف استاندارد کمتر در بومی سازی خطا، از پایداری بالایی برخوردار است. در مقایسه با DVHop، PSODVHop، GSODVHop و DEIDVHop، روش پیشنهادی خطاها را به ترتیب 1.73، 1.60، 1.28 و 1.13 برابر کاهش می دهد.

    کلید واژگان: اینترنت اشیاء، الگوریتم Dvhop، مکان یابی، الگوریتم بهینه سازی وال
    Nazbanoo Farzaneh*, Enciye Farmanbar

    Sensors of the Internet of Things and wireless sensor networks need accurate localization to provide information, such as road information. Installing a locator on all the nodes and sensors is very expensive, and, for this reason, indirect localization is done. One of the low-cost localization methods is DVHop algorithm. Due to the simplicity of DVHop algorithm, its execution time is not long; for this reason, it does not impose much energy consumption, and it is considered a low-cost algorithm. One of the challenges of the DVHop method is its significant error in localization. To solve this problem, in the present paper, a smart locator system is presented using the DVHop algorithm and improved Whale optimization algorithm to estimate the location of objects and sensors. The proposed method has three main steps for smart localization. Experiments showed that the proposed method has less localization error than PSO, WOA, GWO, and HHO algorithms and has high stability due to less standard deviation in localizing the error. Compared to the DVHop, PSODVHop, GSODVHop, and DEIDVHop, the proposed method reduces errors by 1.73, 1.60, 1.28, and 1.13 times, respectively.

    Keywords: Internet Of Things, Dvhop Algorithm, Localization, Whale Optimization Algorithm
نکته
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