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جستجوی مقالات مرتبط با کلیدواژه

linear programming

در نشریات گروه ریاضی
تکرار جستجوی کلیدواژه linear programming در نشریات گروه علوم پایه
  • Narjes Amiri *, Hadi Nasseri, Davood Darvishi Salokolaei
    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.
    Keywords: Fuzzy Relation Inequality‎, ‎Linear Programming‎, ‎Max-Product‎, ‎Wireless Communication
  • Zohreh Akbari *
    This paper addresses the inverse optimization problem for linear programming, focusing on determining a cost vector that ensures a pre-specified solution is optimal. Two approaches are presented: (i) using the Karush-Kuhn-Tucker (KKT) conditions, and (ii) a geometric perspective leveraging first-order necessary conditions. The latter method results in a convex quadratic programming problem, solved efficiently using the gradient projection method. Numerical experiments, including a complex resource allocation problem, validate the proposed approach. This study extends the theory and application of inverse optimization across logistics, resource management, and supply chain optimization.
    Keywords: Inverse Optimization, Linear Programming, Gradient Projection Method
  • Shahram Saeidi, Sahar Khoshfetrat

    The transportation industry of any country represents the economic situation and the level of industrial development of that country, so this industry should be considered as one of the most essential factors in any society's economic, cultural, and social development. Exact planning and scheduling in airline transportation is inevitable. The crew scheduling problem is defined as creating a set of tasks to provide daily transportation services by creating a set of trips and assigning crews to them at minimum cost. So far, many studies have been carried out in this field, and researchers have presented several methods and algorithms to solve this problem. This research assumes that the air fleet consists of different types of planes, which are classified into different types based on their capacity and operating cost. The time it takes for a plane to travel back and forth depends on the type of plane and the length of the round trip. However, there may be no flights due to the proximity of the distance or low passenger demand. The main goal of this research is to determine the best flight schedule for the country's airlines using the linear planning method to minimize the total cost of transportation and passenger movement. Due to the non-linearity of the proposed model, a meta-heuristic method has also been developed based on the particle swarm optimization approach and simulated in MATLAB on sample problems in small, medium, and large dimensions. The calculation results indicate the efficiency and stability of the proposed method.

    Keywords: Crew Scheduling Problem, Airline Routing, Linear Programming, Particle Swarm Optimization
  • Sulima Ahmed Mohammed Zubair *, Najla Elzein Abukaswi Osman, Wiem Abedelmonem Salah Ben Khalifa, Amal Hassan Mohammed Yassin
    This paper investigates Neutrosophic Linear Programming (NLP) and focuses on one of the most suitable approaches to solve it, which is called the Simplex-based model. This type of method, inspired by the classic Simplex algorithm, is in search of an optimal basic neutrosophic feasible solution, and several attractive models of it have been proposed in recent years. However, due to neutrosophic logic considers three dimensions of a problem, using a direct generalization of the simplex algorithm (which has been done in existing methods), the computational volume is greatly increased even for the small problems, and as a result, the use of these models in real-world issues will be questioned. To solve this gap, we consider NLP and propose an effective, simple model that can significantly reduce computational tasks and address these deficits in the mentioned models. Some numerical experiments with the comparison results are provided to explain the efficiency and superiority of the proposed approach.
    Keywords: Linear Programming, Neutrosophic Linear Programming, Simplex Method, Triangular Neutrosophic Numbers, Single Valued Triangular Neutrosophic Numbers
  • Mona Barat *, Ghasem Tohidi

    Data envelopment analysis (DEA) provides performance evaluation for a set of homogeneous decision making units (DMUs) in the sense that all DMUs evaluated with the same criteria setting. In some settings, however, the assumption of having a common input and output bundle may not hold. Such can occur in universities, for example, since they may have different departments, or in hospitals where have different wards. This motivates to the issue of how to fairly evaluate efficiency when inputs and outputs configurations are different. This paper proposes a three-process methodology that aims at evaluating of a set of DMUs when the requirement of homogeneity among inputs and outputs is relaxed. In the first step, based on the duality theory a multiplier directional distance function (DDF) model is developed to determine an appropriate split of inputs and output. In step 2, the efficiency of a DMU is evaluated in terms of each scaled down inputs and outputs. Finally, the overall efficiency score of a DMU is viewed as a weighted combination of a set of product lines efficiencies. To demonstrate the validity and practicability of the proposed method, we apply it to evaluate the performance of a hypothetical data set. The results show that the methodology has the ability to discriminate performance for data with nonhomogeneous inputs and outputs.

    Keywords: Data Envelopment Analysis, Non-Homogeneous Inputs, Outputs, Efficiency Evaluation, Combined-Oriented DEA Models, Linear Programming
  • Shahram Saeidi*, Sahar Khoshfetrat

    A mobile cellular network consists of several base stations as antennas. Each antenna operates under a coverage radius and can serve users within its range. Based on the number of stations in the city and the overlapping of their service areas, a user may simultaneously be in the radio coverage radius of several antennas. However, he/she will be able to receive service from only one antenna. The number of users for whom each antenna can provide service is limited, and increasing the workload of the base station can lead to disturbances in the network performance. In this research, a linear programming model was presented to balance the workload among base stations in cellular mobile networks, and it was implemented in Lingo software on 12 real data sets in Tabriz city. The simulation results show that the proposed model can achieve the global optimal solution with the objective function equal to zero in eight examples. In the other examples, the local optimum solution with a minimal objective function value is obtained, and the workload is balanced throughout the network base stations.

    Keywords: Linear Programming, Load Balancing, Cellular Mobile Networks, Lingo
  • Mohammad Gholami Baladezaei *, Elahe Sarfi, Monire Hosein Alliani
    In recent years, use of fuzzy linear regression has expanded significantly in economics, accounting and financial mathematics. In this type of regression, for data analysis, there is no need to meet the prerequisites that are required in normal linear regression. In addition, it is necessary to solve a linear programming problem to find coefficients of this type of regression. Since in examining the status of stability and the cash and accrual components of companies’ profits, the calculation of accruals is based on forecasts and estimates and is measured by less reliability, so implied greater stability of profit due to its cash component. Among the cases that have an accrual basis, the non-objectivity of the amount, especially the cash amount of the earnings, is very important for the future profitability. In this study, focusing on profit cash components and the stability, the profitability of the company and its efficiency compared to the accrual basis are investigated using fuzzy linear regression. In the issue of technical analysis and to check profits sustainability, a sample of four selected industries, vehicle manufacturing, pharmaceuticals, basic metals, and ceramic tiles during the years 2011-2016, has been selected in the Tehran Stock Exchange market. In this study, sign constraints are added to the set of constraints in the main linear programming problem so obtained solutions can be interpreted and justified. The results confirmed the hypothesis “cash components of profit have more stability of profit than accrual components”, but the hypothesis “more stability of profit due to cash components of profit has a greater impact on the amount of cash and the rights Stockholders” and also the hypothesis” the profit expectations implicit in the stock price fully reflect the stability of the profit related to the cash components of the profit”, were not confirmed.
    Keywords: Fuzzy Linear Regression, Profit Stability, Stock Returns, Linear Programming
  • Samira Valipour*, Amirhossein Nafei

    The diet problem, as a critical challenge in the health science and the food industry, involves optimizing the combination of foods to meet nutritional requirements at minimal cost. This research presents a novel and flexible linear programming model for the diet problem, integrating neutrosophic triplets to manage the inherent uncertainties and indeterminacies in nutritional data. Neutrosophic logic extends fuzzy logic by introducing an indeterminacy component, allowing for a more nuanced representation of the variability in the food nutritional contents and costs. In our study, we examine eight types of food and four essential nutrients, representing each food’s cost and nutritional content as neutrosophic triplets. These triplets encapsulate the degrees of truth, indeterminacy, and falsity inherent in the data. By converting the neutrosophic triplets into crisp values using a specific score function, we enable the application of traditional linear programming techniques. Our model aims to minimize the cost while ensuring that the diet meets all specified nutritional constraints. The practical implications of the neutrosophic model are demonstrated through a comprehensive case study, highlighting its effectiveness in diet planning and its applications within the food industry. The results underscore the model’s ability to handle data uncertainties robustly, providing a reliable and adaptable solution to the diet problem. This approach not only enhances the precision of dietary planning but also supports improved decision-making processes within the food industry, ultimately contributing to better health outcomes and more efficient resource utilization.

    Keywords: Diet Problem, Food Mixture, Linear Programming, Uncertainty, Neutrosophic Triplets
  • IRRIGATION WATER RESOURCE PLANNING OPTIMIZATION MODEL: THE CASE OF WINE GRAPE FARMING IN DODOMA, TANZANIA
    Halidi Lyeme*, JAIROS SHINZEH

    Optimum cropping pattern in vineyard irrigated farming is one of the vital tasks for obtaining the best irrigation water reserves of the command. In this article the linear programming model was developed for optimal use of water and land resources. The model was tested by the data from Chinangali irrigated farmland with 120 cultivated hectares found in Dodoma, Tanzania. The results show that, the savings of 16 470.40 m3 of water per annum will be observed if the planting of 14.18 hectares of Chardonnay, 27.97 hectares of Cabernet sauvignon, 56.14 hectares of Riesling and 21.39 hectares of Chenin blanc. Thus, it was recommended that 1 173 359.60 m3 of water should be released to the irrigated farmland per annum for the best irrigation planning versus the 1 189 830 m3 of water supplied currently per annum.

    Keywords: linear programming, Optimization, Irrigation, MATLAB, FAO Penman-Monteith Equation
  • Ali Abbasi Molai *
    The linear programming problem provided to bipolar fuzzy relation equation constraints is considered in this paper. The structure of bipolar fuzzy relation equation system is studied with the max-product composition. Two new concepts, called covering and irredundant covering, are introduced in the bipolar fuzzy relation equation system. A covering-based sufficient condition is proposed to check its consistency. The relation between two concepts is discussed. Some sufficient conditions are presented to specify one of its optimal solutions or some its optimal components based on the concepts. Also, some covering-based sufficient conditions are given for uniqueness of its optimal solution. These conditions enable us to design some procedures for simplification and reduction of the problem. Moreover, a matrix-based branch-and-bound method is presented to solve the reduced problem. The sufficient conditions and algorithm are illustrated by some numerical examples. The algorithm is compared to existing methods.
    Keywords: Bipolar fuzzy relation equations, covering, irredundant covering, Linear programming, max-product composition
  • Ali Mahmoodirad *
    The literature of linear programming problem with trapezoidal intuitionistic parameters is full of solution approaches which are mainly ranking function based. Use of ranking function in the solution approaches could be a weakness as different ranking functions mag give different solutions. This paper, proposes a new solution approach without any ranking function for linear programming problem with trapezoidal intuitionistic parameters. For this aim, the trapezoidal intuitionistic fuzzy objective function is converted to a multi-objective function, and consequently, the problem is converted to a multi-objective crisp problem. As another contribution, in order to solve the obtained multi-objective problem for its efficient solutions, a new multi-objective optimization approach was developed and suited to the obtained multi-objective problem. The computational experiments of the study show the superiority of the proposed multi-objective optimization approach over the multi-objective optimization approaches of the literature.
    Keywords: linear programming, Trapezoidal intuitionistic fuzzy number, Multi-Objective Optimization, Fuzzy programming approach
  • Sabiha Djebara, Farida Achemine *, Ouiza Zerdani
    The main purpose of this study is to construct a new approach for solving a constrained matrix game where the payoffs and the constraints are LR-fuzzy numbers. The method that we propose here is based on chance constraints and on the concept of a comparison of fuzzy numbers. First, we formulate the fuzzy constraints of each player as chance constraints with respect to the possibility measure. According to a ranking function $\mathcal{R}$, a crisp constrained matrix game is obtained. Then, we introduce the concept of $\mathcal{R}$-saddle point equilibrium. Using results on ordering fuzzy numbers, sufficient existence conditions of this concept are provided. The problem of computing this solution is reduced to a  pair of primal-dual linear programs. To illustrate the proposed method, an example of the market competition game is given.
    Keywords: Chance-constraints, constrained matrix games, fuzzy games, Linear programming, saddle point equilibrium
  • Sara Fanati Rashidi *

    Congestion is one of the main problems in economic issues. In fact, congestion is a wasteful step in the production process, where outputs decrease as a result of increasing inputs. In the economy, congestion is important because its elimination reduces the cost and also increases the output. Therefore, there is a great benefit in identifying congestion and reducing it. Since it is difficult to compute congestion for DMUs with interval data, owing to the computational complexity of the existing methods, we first present a new method for computing congestion and then obtain congestion in the case of interval data. It is well known that if DEA inputs and outputs are in the form of intervals, there will be an efficiency interval for each DMU. Since we assume interval data in this paper, we obtain an interval for the amount of congestion possible in each DMU and prove that the interval indicates the upper and lower bounds of congestion for each DMU.

    Keywords: Data Envelopment Analysis, Congestion, Linear Programming, Efficiency, Decision Maker Unit
  • Halidi Lyeme *, Leonard Katalambula
    It is challenging to follow all nutritional requirements simultaneously. A good mathematical tool for converting nutrient-based suggestions into realistically nutritionally ideal food combinations integrating locally accessible foods is the diet optimization model. The objective of this study is to design a linear programming model that figures out how many grams of each food type need to be mixed to produce an instant meal complement for infants between the ages of 6 and 23 months. The mathematical model developed computes the grams of each food type – Quelea mixed with either Green Banana or White Rice or Irish Potato and Onions, Tomatoes, Carrots and Green bell Pepper. When those foods were combined, an instant food complement will be created and entirely satisfy the preset needs of malnourished infants. Thus, Tanzanian public health technologists and nutritionists may apply the linear programming approach explored in this study to create new ready-to-use food formulations.
    Keywords: Malnutrition, Linear Programming, Instant Compliment Food, Food Type, Quelea
  • Jamshid Saeidian *, Muhammad Sarfraz, Sajad Jalilian

    In this work, we study a data visualization problem which is classified in the field of shape-preserving interpolation. When   function  is known to be bounded, then it is  natural to expect its interpolant to adhere boundedness. Two spline-based techniques are proposed to handle this kind of problem. The proposed methods   use quadratic splines as basis and involve solving a linear programming or a mixed integer linear  programming problem which gives $C^1$ interpolants. An energy minimization technique is employed to gain the optimal smooth solution. The reliability  and  applicability of  the proposed techniques  have been illustrated through examples.

    Keywords: Shape preserving interpolation, Boundedness, quadratic splines, Linear programming
  • Abbasali Monzeli, B. Daneshian, G .Tohidi, M. Sanei, Sh .Razavian

    Data envelopment analysis (DEA) assigns a score to each unit of the decision-making units being analyzed indicating the efficiency or inefficiency of that unit over the other units. However, in the early DEA models, there is no strategy to improve the efficiency of the efficient units. Therefore, in Paradi & Sowlati's (2004) practical boundary theory, they tried to expand these models to increase the efficiency of the efficient decision-making units. They had a basis for improving performance to a certain extent, thus, they presented the P-DEA linear programming model to extend the efficiency of the efficient units. Because of the staff management in organizations, it is important to increase the efficiency units in order to improve the organization based on the possible changes in the level of input and output of decision-making units. This is done to produce newly advanced based on the efficiency of these new units. In this research, after studying the P-DEA model thoroughly, we identified its drawbacks and proposed a new method for determining the practical boundary by developing an additive model using an example.

    Keywords: Data envelopment analysis, Decision-making unit, Linear programming, practical boundary
  • Venkata Subba Reddy P *, Mangal Vikas
    For a simple, undirected, connected graph G=(V,E), a function f : V(G) →{0, 1, 2} which satisfies the following conditions is called a quasi-total Roman dominating function (QTRDF) of G with weight f(V(G))=ΣvΕV(G) f(v).C1). Every vertex uεV for which f(u) = 0 must be adjacent to at least one vertex v with f(v) = 2, and C2). Every vertex uεV for which f(u) = 2 must be adjacent to at least one vertex v with f(v)≥1.  For a graph G, the smallest possible weight of a QTRDF of G denoted γqtR(G) is known as the quasi-total Roman domination number of G. The problem of determining γqtR(G) of a graph G is called minimum quasi-total Roman domination problem (MQTRDP). In this paper, we show that the problem of determining whether G has a QTRDF of weight at most l is NP-complete for split graphs, star convex bipartite graphs, comb convex bipartite graphs and planar graphs. On the positive side, we show that MQTRDP for threshold graphs, chain graphs and bounded treewidth graphs is linear time solvable. Finally, an integer linear programming formulation for MQTRDP is presented.
    Keywords: Domination number, quasi-total Roman domination, complexity classes, graph classes, linear programming
  • علی نمکین، سید اسماعیل نجفی*، محمد فلاح، مهرداد جوادی

    در این مقاله ، یک روش جدید ترکیبی از شبکه های عصبی پرسپترون چند لایه و تحلیل پوششی داده ها ارایه می شود که در آن مقادیر ورودی و خروجی برای تعداد زیادی واحد تصمیم گیرنده به عنوان ورودی های شبکه عصبی تعیین می شود. می توان دید که با بکارگیری شبکه عصبی برای حل مسایل تحلیل پوششی داده ها نیاز به حل مدل مورد نظر برای هر واحد تصمیم گیرنده نیست و لذا الگوریتم ارایه شده زمان پردازش و استفاده از حافظه را نسبت به آنچه مورد نیاز روش متعارف در تحلیل پوششی داده ها است، به مقدار زیادی کاهش می دهد.جهت بررسی دقت شبکه ارایه شده،  چندمطالعه موردی از جمله مجموعه ای از  500شعبه بانک  مورد استفاده قرار می گیرد.نتایج نشان دهنده دقت بالا وزمان محاسباتی کمتر(اعتبارلازم) مدل ترکیبی پیشنهادی است.

    کلید واژگان: تحلیل پوششی داده ها، شبکه عصبی مصنوعی، کارایی
    A. Namakin, S. E. Najafi *, M. Fallah, M. Javadi

    In this paper, a new method of combining ANN and DEA (ANN-DEA) presented in which the input and output values for a large number of DMUs determined as neural network inputs. We have also compared the new model with the existing approach of ANN-DEA. To illustrate the ability of the proposed methodology some case studies are used, including a set of 500 Iranian bank branches.

    Keywords: Data Envelopment Analysis, Artificial Neural Network, Levenberg Marquardt, Efficiency, Linear Programming
  • Seyed Mohammadtaghi Azimi, Hu Chun, Chen Zhihong, Amirhossein Nafei *

    Linear Programming as a practical technique for solving optimization problems with linear objective functions and linear constraint plays an essential role in mathematical programming. Most of the real-world problems are included in inconsistent and astute uncertainty. That's why the optimal solution can't be found easily. The Neutrosophic theory, as an extension of fuzzy set theory, is a powerful instrument to handle inconsistent, indeterminate, and incomplete information. This paper presents an applied approach for solving Interval Neutrosophic Integer Programming problems. By using the proposed approach, we can handle both incomplete and indeterminate data. In this respect, using a ranking function, we present a technique to convert the Interval Neutrosophic Integer Programming problem into a crisp model and then solve it by standard methods.

    Keywords: Neutrosophic, linear programming, Integer programming, Interval neutrosophic number
  • MohammedAhmed Alkailany, MohammedSadiq Abdalrazzaq

    We formulate a new bond portfolio optimization model as a two-stage stochastic programming problem in which a decision maker can optimize the cost of bond portfolio selection while deciding which bonds to sell, which bonds to hold, and which bonds to buy from the market, as well as determine the quantity of additional cash in period t under different scenarios and varying assumptions, The model proved its efficiency by finding the optimal values and giving an investment plan that, it will reduce the cost of the portfolio.

    Keywords: Stochastic Portfolio Programming model, linear programming, nonlinear programming, constrained optimization
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