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جستجوی مقالات مرتبط با کلیدواژه

meta-heuristic algorithm

در نشریات گروه صنایع
تکرار جستجوی کلیدواژه meta-heuristic algorithm در نشریات گروه فنی و مهندسی
  • Mohammadreza Pourhassan, Mohammadreza Khadem Roshandeh, Peiman Ghasemi *, Mehrnaz Sadat Seyed Bathaee

    In this study, we aim to explore the modeling and solution approach for a multi-objective location-routing-inventory problem. The focus is on planned transportation with the goal of minimizing total costs and reducing the maximum working hours of drivers. To achieve these objectives, we need to consider the routing of vehicles between customers and distribution centers, as well as the optimal allocation of product transfer flow between the production center and customers. Therefore, the proposed model incorporates location, routing-inventory, and allocation simultaneously. To solve the two-objective model, we employed the Epsilon-constraint method for small-sized problems. For large-sized problems, we utilized the NSGA-II and MOWOA meta-heuristic algorithms with a new chromosome. The computational results indicate that in order to reduce the maximum working hours of drivers, it is necessary to increase the number of vehicles and minimize travel distances. However, this leads to higher costs due to vehicle utilization and the need for constructing distribution centers closer to customers, which in turn increases construction costs. Finally, based on the analysis, the NSGA-II algorithm outperformed the MOWOA algorithm with a weighted value of 0.983 compared to 0.016, making it the selected algorithm.

    Keywords: Facility Location, Vehicle Routing, Allocation, Inventory, Meta-Heuristic Algorithm
  • Abbas Toloie Eshlaghy, Amir Daneshvar *, Ali Peivandizadeh, Abdulrahman S Senathirajah, Irwan Ibrahim

    In this article, a health service delivery model based on the Internet of Things (IoT) under uncertainty is presented. The considered model includes a set of patients, doctors, vehicles, and services that should be provided in the shortest time and cost. The most important decisions of the network include the allocation of specialist doctors to patients, the routing of vehicles, and doctors to provide health services. The dataset of the problem has been provided to the hospital and centers using IoT tools and an integration framework has been designed for this problem. The results of solving the numerical examples show that to reduce the service delivery time and the distance traveled by vehicles, the design costs of the model should be increased. Also, the increase in the rate of uncertainty during service delivery leads to an increase in total costs in the health system. In this article, Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-objective imperialist Competitive algorithm (MOICA) were proposed to solve the model, and the results showed that the proposed methods are more efficient than the exact methods. These algorithms have achieved close to optimal results in the shortest possible time. Also, the calculation results in large numerical examples show the high efficiency of the MOICA.

    Keywords: Healthcare System, Uncertainty, Iot, Meta-Heuristic Algorithm, Vehicle Routing
  • Ali Roghani, Akbar Alem Tabriz *, Mohammad Mehdi Movahedi, Gholam Hassan Shirdel
    The purpose of this research is to optimize the use of water resources in dams in Khuzestan province. For this purpose, in this research, we seek to optimize the cost and time of sending water to each of the cities from the total dams in Khuzestan province. The model is solved using the deterministic epsilon constraint method and NSGA-II and MOPSO algorithms meta-heuristically. According to the results presented in this research, the water supply from the Balaroud dam to the cities of Ahvaz, Izeh, Abadan, Baghmolk, and Bandar Imam Khomeini has not been determined to be optimal. The same dam sends a certain amount of water to the cities of Andimeshk, Dezful, Shush, Shushtar and Gotvand. The results showed that NSGA-II has a more acceptable performance than the MOPSO algorithm from the point of view of three criteria, and the MOPSO algorithm has a better condition than the NSGA-II algorithm only in terms of the distance to the ideal point. In addition, according to the sensitivity analysis, it has been determined that the increase in water demand can increase the shipping time by 1.9% and the shipping cost by 60%. Therefore, the effect of water demand is more on time and not on cost. Increasing the budget can have an effect on cost and time, which of course has more effect on time than cost.
    Keywords: Water Management Of Dams, Optimization, Epsilon Constraint, Meta-Heuristic Algorithm
  • Adel Pourghader Chobar, Hamid Bigdeli *, Nader Shamami
    By providing timely transportation and dispatch of raw materials and finished goods, freight transport plays an essential role in industries, commercial activities, and trade war industries. It also has a significant impact on the overall performance of associated organizations and the ultimate costs of their products. Therefore, freight transport providers are under pressure to decrease costs and increase their service levels and should overcome these pressures by redesigning and improving their logistics processes on strategic, tactical, and operational levels. In this research, a multi-objective model is proposed for hub location in the field of war equipment under uncertainty. The first objective is to minimize costs, the second objective is to maximize the fulfillment of demands, and the third objective is to minimize congestion on the routes. Taking into account the parameters in the state of uncertainty, the mathematical model is modeled in a robust state and a robust counterpart model of the problem is proposed. In order to solve the problem on a small scale, the exact epsilon constraint method is used in GAMS software. Also, meta-heuristic approaches of grey wolf optimizer (GWO) and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the model in medium and large dimensions. Next, the solution time of two algorithms was compared. 10 numerical experiments with different dimensions were designed and implemented through GWO and NSGAII algorithms. The results showed that the time to solve the GWO problem is less than the other algorithm. Finally, proper performance indicators are used to compare the performance of the used algorithms, and as a result of solving several numerical examples and calculating their performance indicator, it is concluded that the GWO algorithm has a better performance in solving the model.
    Keywords: Hub Location, War Equipment, Uncertainty, Meta-Heuristic Algorithm
  • Seyed Mohammadhassan Hosseini, Hossein Amoozad Khalili *, Moujan Shirali

    Scheduling for flexible flowshop environments is generally limited by resources such as manpower and machines. However, the majority of efforts tackle machines as the only constrained resource. This paper aims to investigate the problem of scheduling in flexible flowshop environments considering different skills as human resource constraints to minimize the total completion time. In this way, a mathematical model of complex integer linear programming is presented for solving small-sized problems in a reasonable computational time. In addition, due to the NP-hard nature of the problem, a whale hybrid optimization algorithm is tuned to solve the problem in large-sized dimensions. In order to evaluate the performance of the proposed optimization algorithm, the results are compared with five known optimization algorithms in the research background. All evaluations and results show the good performance of the whale hybrid algorithm. Especially, the final solution of the proposed algorithm shows a 0.75% deviation of the best solution in solving different instances on large-scale sizes. However, the genetic algorithm, memetic global and local search algorithm, and hybrid salp swarm algorithm are in the next ranks with 3.31, 3.52, and 4.02 percent respectively. In addition, proper discussions and managerial insights are provided for the relevant managers.

    Keywords: Flexible Flowshop, Scheduling, Meta-Heuristic Algorithm, Manpower Skills
  • Abbas Toloie Ashlaghi, Amir Daneshvar *, Adel Pourghader Chobar, Fariba Salahi

    In this article, a sustainable network of distribution of agricultural items with suppliers, distribution centers, and retailers is considered. The main purpose of presenting the mathematical model in this article is to determine the optimal number and location of suppliers, assigning suppliers to distribution centers and optimal routing for the distribution of agricultural items to retailers in a predefined time window. Also, determining the optimal amount of inventory and the reorder point in retailers and distribution centers is another problem decision. To model the problem, some parameters of the model were considered non-deterministic and were controlled by the probabilistic fuzzy method. The results of solving numerical examples in different sizes showed that with the increase of the total costs of the distribution network of agricultural items, the amount of greenhouse gas emissions decreases, and the employment rate increases. Also, with the increase of the uncertainty rate, due to the increase of the real demand and the change in the optimal amount of production, distribution, storage and reorder point, the values of all the objective functions also increase. It was also observed by solving different numerical examples with NSGA II and MOGWO algorithms, these algorithms have been able to solve the problem in a much shorter period than the epsilon constraint method, and comparison indicators such as NPF, MSI, SM, and computing time show These algorithms have a high efficiency in solving numerical examples of the problem of the distribution network of agricultural items.

    Keywords: Stable Network, Agricultural Items, Uncertainty, Meta-Heuristic Algorithm, Jimenez Fuzzy, Time Window
  • Hamed Nozari*, Maryam Rahmaty

    In this paper, the modeling of a make-to-order problem considering the order queue system under the robust fuzzy programming method is discussed. Considering the importance of timely delivery of ideal demand, a four-level model of suppliers, production centers, distribution centers, and customers has been designed to reduce total costs. Due to the uncertainty of transportation costs and ideal demand, the robust fuzzy programming method is used to control the model. The analysis of different sample problems with LCA, PSO, and SSA methods shows that with the increase in the uncertainty rate, the amount of ideal demand has increased and this has led to an increase in total costs. On the other hand, with the increase of the stability coefficients of the model, contrary to the reduction of the shortage costs, the total costs of the model have increased due to transportation. Also, the analysis showed that with the increase in the number of servers in the production and distribution centers, the average waiting time for customers' order queues has decreased. Because by reducing the waiting time, the total delivery time of customer demand decreases, and the amount of actual demand increases. On the other hand, due to the lack of significant difference between the OBF averages among the solution methods, they were prioritized and SSA was recognized as an efficient algorithm. By implementing the model in a real case study in Iran for electronic components, it was observed that 4 areas of the Tehran metropolis (8-18-16-22) were selected as actual distribution centers. Also, the costs of the whole model were investigated in the case study and the results show the high efficiency of the solution methods in solving the MTOSC problem.

    Keywords: order-based manufacturing, order queuing system, uncertainty, robust fuzzy programming, meta-heuristic algorithm
  • Samira Sameye, Javad Rezaeian Zaidi *, MohammadReza Lotfi

    This research proposes and solves a mathematical problem of parallel machine scheduling to minimize the total completion time and energy cost. This research aims to design and optimize a multi-objective mathematical model by minimizing energy consumption and total completion time for the parallel machine scheduling problem in Semnan Polyethylene Factory. First, the mathematical model of the problem is provided, and then the solution method is investigated using the epsilon constraint method in the GAMS optimization software and the meta-heuristic imperialist competitive algorithm (ICA). The mathematical model is validated using GAMS software and the constraint epsilon method and a real problem is implemented in large dimensions regarding the case study of the polyethylene factory in Semnan province using the meta-heuristic ICA. Finally, the performance of the ICA is measured in terms of the RPI index for small dimensions and the MID index for examples with large dimensions. Numerical results show that the value of the index for distance from the ideal point in the ICA is lower than that of the index obtained from solving the problem in GAMS. With these interpretations, it can be concluded that the ICA has a better performance than GAMS for optimizing the parallel machine scheduling problem in this research. According to the obtained answers, it can be concluded that with the increase in the time to do a task, the time to complete all tasks also increases and the cost of energy remains constant. While the cost of doing the task and the price of the electricity signal increase, energy costs increase and the time to complete tasks remains constant.

    Keywords: Scheduling problem, parallel machines, energy cost, meta-heuristic algorithm
  • نازیلا نیکدل*
    هدف

    امروزه از سیستم های رباتیک به صورت وسیعی در پیشبرد عملیات صنعتی بهره گرفته می شود؛ بنابراین، اتخاذ تصمیمات کنترلی مناسب جهت تضمین کارایی این سیستم ها امری حیاتی است. لازم است معیارهایی هم چون مدت زمان عملیات و سرعت پاسخ دهی، هزینه کنترلی و خطای سیستم با ارایه روش مناسبی به صورتی کنترل شوند تا عملیات صنعتی به نحو موفقیت آمیزی انجام شود؛ لذا این مقاله دو هدف اصلی را دنبال می کند: 1- کنترل سیستم رباتیک از طریق ارایه یک روش مبتنی بر حسابان مرتبه کسری به صورتی که قادر باشد باوجود پیچیدگی و غیر خطی گری در سیستم آن را کنترل نماید و 2- ارایه الگوریتم فرا ابتکاری "بهبود یافته گرگ خاکستری" جهت بهینه سازی پاسخ سیستم.

    روش شناسی پژوهش:

     ابتدا مدل ریاضی ربات بر مبنای قوانین لاگرانژ ارایه می شود و سپس از حسابان مرتبه کسری جهت طراحی کنترل کننده بهره گرفته می شود. علاوه بر آن، کارایی الگوریتم گرگ خاکستری با ارایه روش بهبودیافته افزایش می یابد.

    یافته ها

    توابع هزینه مختلف برمبنای معیارهای عملکردی اصلی سیستم رباتیک معرفی و الگوریتم بهبود یافته بر آن ها اعمال می شود. نتایج حاصل از مقایسه الگوریتم پیشنهادی با الگوریتم های دیگر، نشان دهنده عملکرد مطلوب الگوریتم پیشنهادی هستند. علاوه بر آن، کارایی کنترل کننده مرتبه کسری با معادل مرتبه صحیح آن مقایسه می شود و نتایج نشان دهنده بهبود قابل توجه عملکرد سیستم هستند.

    اصالت/ارزش افزوده علمی:

     کنترل کننده پیشنهادی قادر است باوجود پیچیدگی و غیر خطی گری در مدل سیستم، آن را به خوبی کنترل نماید. علاوه بر آن با الهام از الگوریتم گرگ خاکستری، روش بهینه سازی بهبود یافته ای ارایه می شود که قادر به افزایش کارایی سیستم کنترل شده باشد. نتایج عددی عملکرد مطلوب کنترل کننده و الگوریتم بهینه سازی بهبودیافته را نشان می دهند.

    کلید واژگان: بهینه سازی، الگوریتم های فرا ابتکاری، الگوریتم گرگ خاکستری بهبود یافته، سیستم رباتیک صنعتی، کنترل کننده مرتبه کسری
    Nazila Nikdel *
    Purpose

    Nowadays, robotic systems are widely used in advanced industrial operations. Therefore, making appropriate control decisions to ensure the efficiency of these systems is critical. Criteria such as operation time and response speed, control cost, and system error need to be controlled by providing appropriate methods to ensure the successful performance of industrial operations. Therefore, this article pursues two main objectives1) controlling the robotic system by presenting a method based on fractional-order calculus so that it can control the system despite its complexity and non-linearity, 2) presenting the meta-heuristic algorithm "Improved Grey Wolf" to optimize the system response.

    Methodology

    First, the mathematical model of the robot is presented based on Lagrange rules, and then the fractional-order calculus is used to design the controller. In addition, the efficiency of the grey wolf algorithm is increased with the introduction of an improved method.

    Findings

    Different cost functions based on the main performance criteria of the robotic system are introduced, and an improved algorithm is applied to them. The comparison results of the proposed algorithm and other algorithms, indicate its satisfying performance. In addition, the efficiency of the fractional-order controller is compared with its integer-order counterpart, and the results show a significant improvement in system performance.

    Originality/Value: 

    The proposed controller can control the system well despite its complexity and non-linearity. In addition, inspired by the Grey Wolf algorithm, an improved optimization method is proposed that can increase the efficiency of the controlled system. Numerical results show the satisfying performances of the proposed controller and the improved optimization algorithm.

    Keywords: optimization, meta-heuristic algorithm, Improved grey wolf algorithm, industrial robotic system, fractional-order controller
  • Peiman Ghasemi *, Hossein Hemmaty, Adel Pourghader Chobar, MohamadReza Heidari, Mahdi Keramati

    Today, logistics costs often make up a major part of large organizations’ expenses. These costs can be reduced with optimal design and its implementation in the supply chain. As a result, in present study, a two-objective mathematical location-routing model is presented, where an objective is to minimize the costs and the next is to maximize the reliability in order to deliver the goods timely to customer according to the probable time and time window. The proposed problem has two levels of distribution. The first level, which is called transportation level, points to the distribution of products from a factory to an open distribution center, and the latter is known as routing level, which is related to a part of the problem in which we deliver products from the warehouse to customers. The proposed mathematical model is solved by Epsilon-constraint and NSGA-II approaches in small and medium, and large scales problem, respectively. The present study has provided the following contributions: concurrent locating and routing in the supply chain in accordance with the customer’s time window, probable travel time in the supply chain and customer’s reliability in the supply chain. The assessment metric results indicate the proper performance of our proposed model.

    Keywords: Location-routing problem, Reliability, Time window, meta-heuristic algorithm
  • Meysam Donyavi Rad, Ehsan Sadeh *, Zeinolabedin Amini Sabegh, Reza Ehtesham Rasi
    The natural disasters of the last few decades clearly reveal that natural disasters impose high financial and human costs on governments and communities. Concerns in this regard are growing day by day. Making the right decisions and taking appropriate and timely measures in each phase of the crisis management cycle will reduce potential damage at the time of the disaster and reduce the vulnerability of society. Therefore, in this research, a mathematical model of crisis logistics planning considering the problem of primary and secondary crisis in disaster relief is introduced, which is the innovation of this research. In the primary crisis, the goal is to provide services and relief goods to crisis areas, and in the second stage, the secondary crisis that occurs after the primary crisis seeks to provide relief to crisis centers and transfer the injured to relief centers.  Therefore, this research proposes a mathematical fuzzy ideal programming model in two primary and secondary crises. In the primary crisis, the goal is to provide services and relief goods to crisis-stricken areas. The secondary crisis, which occurs after the primary crisis, aims to support crisis-stricken centers and move injured people to relief bases in the second step. According to the proposed model, Bertsimas-Sim’s fuzzy programming that formulation proposed by Bertsimas and Sim [1] and robust approach we initially used. The Epsilon constraint method was used to solve the low-dimensional model. Multi-objective meta-heuristic algorithms have been designed to handle the computational complexity of large-scale real-time problems. Multiple comparisons and analyses have been proposed to assess the performance of the model and problem-solving capabilities. The results indicate that the proposed approach can be applied and implemented to develop a real-world humanitarian logistics network.
    Keywords: Critical Logistics, Primary, Secondary Crises, Fuzzy Robust Integrated Programming, meta-heuristic algorithm
  • زهرا کوچک زاده، سعیده غلامی*، دنیا رحمانی

    آلودگی هوا و انتشارگاز CO2، یک مساله مهم جهانی است، و کاهش انتشار کربن یکی از مهم ترین اهداف است. در زنجیره تامین محصولات فسادپذیر، تحویل و توزیع موضوع بسیار مهمی است، که همواره بسیاری از پژوهشگران به آن توجه نموده اند. در این پژوهش برای مدل سازی زنجیره تامین سبز، برنامه ریزی خطی عدد صحیح مختلط استفاده نموده ایم، که شامل دو تابع هدف بیشینه سازی سود و کمینه سازی انتشار گاز CO2 است. به دلیل بروز پدیده های تصادفی، پارامترهای هزینه و قیمت به صورت غیرقطعی است که از رویکرد بهینه سازی استوار استفاده نموده ایم، و یک مدل جدید بهینه سازی استوار ارایه نمودیم. این مساله در دسته مسایل ان پی هارد است و روش حل دقیق در ابعاد بزرگ کارآمد نیست. برای حل دقیق مساله روش اپسیلون محدودیت و برای حل در ابعاد بزرگ الگوریتم های فراابتکاری ژنتیک مرتب سازی غیرمسلط، شبیه سازی تبرید چندهدفه و بهینه سازی ازدحام ذرات چندهدفه استفاده شد. به منظور دستیابی به بهترین و دقیق ترین جواب ها در الگوریتم های فراابتکاری، پارامترها با کمک طراحی آزمایش تاگوچی، تنظیم شد. باتوجه به نتایج حاصل از آزمون های محاسباتی، میانگین اختلاف با جواب بهینه 2/0 تا 8/0 درصد و در مدل های استوار 7/0 تا 9/0 درصد است، این مقدار قابل قبول است. کمترین زمان اجرا به ترتیب مربوط به الگوریتم شبیه سازی تبرید چندهدفه، ژنتیک مرتب سازی غیرمسلط و بهینه سازی ازدحام ذرات چندهدفه است، و کارآمدی این الگوریتم ها باتوجه به معیارهای استاندارد چندهدفه تفاوت معنی داری ندارند

    کلید واژگان: شبکه زنجیره تامین محصولات فسادپذیر، برنامه ریزی عدم قطعیت، بهینه سازی استوار، الگوریتم فراابتکاری
    Z. Kochakzadeh, S. Gholami*, D. Rahmani

    Air pollution and CO2 emissions are important global issues, and reducing carbon emissions is one of the most important goals. In the supply chain of perishable products, delivery and distribution are significant issues, which many researchers have always paid attention to. In this research, we have used mixed integer linear programming to model the green supply chain, which includes two objective functions profit maximization and CO2 emission minimization. Due to the occurrence of uncertainties, cost and price are uncertain, we have used the robust optimization approach, and presented a new robust optimization model. This problem is included in NP-hard problems and the exact method is not efficient in large dimensions. The exact solution method of the constraint epsilon, and for the large-scale problems, meta-heuristic algorithms of NSGA, MOSA and MOPSO were used. In order to obtain the best and most accurate solutions in meta-heuristic algorithms, the parameters were adjusted with the help of Taguchi's design. According to the results of validity tests, the average difference with the optimal solution is 0.2 to 0.8% and in robust models, it is 0.0.7 to 0.9%, which is acceptable. The lowest execution time is related to the MOSA algorithm, NSGA and MOPSO respectively, and the efficiency of these algorithms is not significantly different according to the standard multi-objective criteria.

    Keywords: Supply Chain Network of, Perishable Products, Uncertainty Planning, Robust Optimization, Meta-Heuristic Algorithm
  • عبدالرضا حمدی اصل، حسین عموزادخلیلی*، رضا توکلی مقدم، مصطفی حاجی آقایی
    در سالیان اخیر، زنجیره های تامین مواد غذایی و کشاورزی توجه محققان زیادی را به خود جلب کرده اند. زیرا این محصولات غالبا ارزش زیادی برای سهامداران خود فراهم می کنند. ازاین رو رویکرد جدیدی برای طراحی یک شبکه زنجیره تامین حلقه بسته برای محصولات خرما در این مقاله توسعه یافته است. محصولات خرما و محصولات جانبی نیز در این شبکه برای استفاده در بازارهای هدف خود درنظر گرفته شده اند. دراین راستا، یک مدل ریاضی جدید برای بهینه سازی کل هزینه ها شامل هزینه های ثابت، هزینه های پردازش، هزینه های عملیاتی و هزینه های حمل ونقل در هردو جریان روبه جلو و معکوس فرموله شده است. نوآوری اصلی این مقاله درنظر گرفتن یک مدل شبکه زنجیره تامین نوآورانه براساس ویژگی های منحصربه فرد محصول خرما بوده است. در این شبکه حلقه بسته، پایداری محصول خرما در شبکه زنجیره تامین برای اولین بار مورد بررسی قرار گرفته است. همچنین دراین مدل برای اولین بار جمع آوری پسماند محصولات به مراکز بازیافت معرفی گردیده و برای حل مدل پیشنهادی مساله، مجموعه ای از الگوریتم های فراابتکاری و ترکیبی به همراه استفاده از سالور سیپلکس استفاده شده است. در ادامه نیز برای اعتبارسنجی مدل پیشنهادی و عملکرد این الگوریتم ها، چندین اندازه مساله تولید و برازش انجام گردید. برای دست یابی به نتایج بهینه، پارامترهای الگوریتم های پیشنهادی براساس روش تاگوچی تنظیم شده و در پایان تحلیل حساسیت انجام شده که نتایج آن، کاهش معنی داری در هزینه های کلی صنعت خرما با درنظر گرفتن محصولات جانبی و جمع آوری زباله در جریان معکوس را نشان می دهند.
    کلید واژگان: طراحی شبکه، زنجیره تامین کشاورزی، لجستیک معکوس، الگوریتم فراابتکاری
    Abdolreza Hamdiasl, Hossein Amoozadkhalili *, Reza Tavakkoli-Moghaddam, Mostafa Haji Aghaie
    Recently, food and agricultural supply chains have attracted researchers’ attention as they provide more values for their stockholders. Hence a new approach to design a closed-loop supply chain network for date products is firstly developed in this work, which is one of the most well-known, rich, and desirable fruits. Date products and by-products are also considered in this network for the use in their target markets. In this regard, a new mathematical model is formulated to optimize the total costs including fixed, processing, operating, and transportation costs in both forward and reverse flows. The main contribution of this paper is to consider an innovative supply chain network model based on the unique characteristics of the date product. In this closed-loop network, the sustainability of the date product in the supply chain network is investigated for the first time. Also, product waste collection to the recycling centers is introduced in this model. To address the developed model, a set of meta-heuristic algorithms and a hybrid one along with the CPLEX solver of GAMS are utilized. Moreover, to validate the proposed model and the performance of these algorithms, several problem sizes are generated and solved. To achieve the best results, the parameters of the proposed algorithms are tuned based on the Taguchi method. Last but not least, sensitivity analyses are conducted and the results show a meaningful decrease in the overall costs of the date industry considering by-products and waste collection in the reverse flow.
    Keywords: Network Design, Agricultural Supply Chain, Reverse logistics, Meta-Heuristic Algorithm
  • Majid Alimohammadi Ardekani *
    In recent years, supply chains have become an attractive topic for managers and industrialists, and the life and death of organizations and businesses somehow depend on the activity of intertwined chains. On the other hand, in today's highly competitive environment, the high speed of change and evolution has increased the uncertainty and ambiguity of decisions, which makes it difficult to predict future conditions in supply chains. Therefore, reliable planning should be done in uncertain and ambiguous conditions for better and more accurate planning. One of the new and reliable approaches is the robust programming approach. In this study, transferring petroleum products from supply points to consumption areas is examined through a supply chain. Due to the uncertainty in the product demand, a mathematical model is used with two objectives including the reduction of shipping costs and the reduction of the number of loads. Due to the high volume of calculations and the problem data as well as the lack of ability to use exact solution methods, especially on a large scale, PSO and MOGA-II meta-heuristic algorithms are used to solve the proposed model. The results show that the model has the required efficiency in large dimensions and the proposed solution methods provide appropriate answers.
    Keywords: Oil Supply Chain, Robust optimization, Multi-objective optimization, Meta-heuristic algorithm
  • Samrad Jafarian Namin, MohammadSaber Fallahnezhad*, Reza Tavakkoli Moghaddam, Ali Salmasnia, MohammadHossein Abooei

    In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems. In the literature of triple-concept integrated models, it has generally been assumed that the observations are independent. However, the existence of correlated structures in some practical applications put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) control chart and the ARMA control chart are effective tools to monitor the mean of autocorrelated processes. This paper proposes an integrated model subject to some constraints for determining the decision variables of triple concepts in the presence of autocorrelated data. Three types of autocorrelated processes are investigated to study their effects on the results. Moreover, the results of the MEC and ARMA charts are compared. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to select optimal decision variables. An industrial example and extensive comparisons are provided

    Keywords: Statistical process control, Production, Maintenance policy, Autocorrelated process, Meta-heuristic algorithm
  • Niloofar Khalili, Parisa Shahnazari Shahrezaei *, Amir Gholm Abri

    The current study, according to ergonomic factors, aims to model the nurses’ work shift scheduling problem. Considering the urgent needs of the hospitals in providing better services to patients, it seems significant to take the preferences of nurses in scheduling shifts into account. Therefore, in this paper, a multi-objective model of nurses’ scheduling with emphasis on reducing their fatigue during the career shift is presented. To evaluate the outputs of the model, two numerical instances in small and large sizes with real data of Labbafinejad Hospital were designed in 18-person and 90-person wards. To solve a small size problem, a comprehensive standard decision method is employed, the results of which showed that nurses take their most rest during the night shift and in the middle of their working hours to reduce fatigue. Furthermore, due to the NP-Hard nature of the nurses' scheduling problem, in the problem of the 90-person ward, MOPSO and NSGA II algorithms are applied based on the design of a new chromosome. Using the TOPSIS method and entropy weighting method shows that the designed NSGA II algorithm can solve the nurses’ scheduling problem of Labbafinejad Hospital faster and better.

    Keywords: Combined optimization, Nurses’ Scheduling, Ergonomic, fatigue, meta-heuristic algorithm
  • سعید سلجوقی*، رامین صادقیان

    امروزه انتقال سریع و کم هزینه فرآورده های خونی با توجه به اهمیت کمک های اورژانسی وامدادی و همین طور پیشرفت درروش های درمان در علم پزشکی اهمیت فراوانی یافته است با توجه به اینکه خون هیچ جانشینی ندارد و انسان ها خود به عنوان منبع تولید آن به شمار می آیند ازاین رو مشخص شدن محل استقرار بهینه مراکز دریافت و نگهداری و انتقال خون و فواصل آن ها از نقاط تقاضا به جهت بالا بردن سطح سرویس دهی از اهمیت فراوانی برخوردار است لذا با توجه به اهمیت موضوع در این تحقیق به مدل سازی یک زنجیره تامین فرآورده های خونی با دو هدف معین یعنی کاستن از هزینه های حمل ونقل و زمان سرویس دهی در زنجیره با در نظر گرفتن مراکز مختلف انتقال خون محلی و منطقه ای و انتخاب محل بهینه استقرار مراکز سیار خون شده است همین طور با در نظر گرفتن مراکز تقاضا شامل بیمارستان ها و نقاط پرحادثه طراحی شبکه مناسبی از زنجیره تامین جهت سرویس دهی در نزدیکی این نقاط در جهت بهینه سازی اهداف تعیین شده انجام گرفته و نهایتا مدل طراحی شده در مقاله را جهت اطمینان از کارکرد مطلوب با اطلاعات استخراج شده از محل استقرار مراکز و واحدهای دریافت و انتقال خون در شهر زنجان آزمون نموده و نتایج حل مدل را که از روش الگوریتم فرا ابتکاری به دست آمده مورد تجزیه وتحلیل قرارگرفته است.

    کلید واژگان: زنجیره خون رسانی، بهره وری، فاصله پوششی، الگوریتم فرا ابتکاری

    Nowadays, the transfer of blood products to emergency and relief services has become increasingly important, given that blood has no substitute and that humans themselves are the source of its production, as well as advances in medical science that have led to surgical and therapeutic approaches. A new one has made the need to supply blood products less expensive and time-consuming in the modern age, hence the location of blood collection and storage centers and their distances from demand points to raise the level of service. Given the importance of the topic in this research to modeling a chain, it is abundant The purpose of blood products has been to meet two specific goals, namely to reduce costs and service time of the chain by considering different local and regional blood transfusion centers and selecting the optimal location of mobile blood centers, as well as by considering hospital demand centers and Accidental points have been tried to create a suitable network of supply chains near these points. Finally, the model is implemented as a case study with information obtained from the location of units in Zanjan city and is analyzed through meta-heuristic algorithm and results obtained have been analyzed.

    Keywords: Blood Supply Chain, productivity, Coverage distance, Meta-Heuristic Algorithm
  • Alireza Birjandi, Seyed Meysam Mousavi*, Mahdi Hajirezaie, Behnam Vahdani

    Process adjustment, also known as process targeting, is one of the classical problems in the field of quality control and production economics. In the process adjustment problem, it is assumed that process parameters are variables and the aim is to determine these parameters such that certain economic criteria are optimally satisfied. The aim of this paper is to determine the optimal process adjustment in a two-stage production system with rework loops. An absorbing Markov chain model is developed in which all items are inspected for conformance with their specification limits. The cycle time of production process is included in the model for optimizing total profit of the system. Also, effects of inspection errors are investigated.

    Keywords: Flexible production networks, RCPSP, Production projects, Production scheduling problem, Mathematical model, Meta-heuristic algorithm, Multiple routes
  • Alireza Birjandi, Seyed Meysam Mousavi*, Mahdi Hajirezaie, Behnam Vahdani

    In production environments, multi-route Resource-Constrained Project Scheduling Problem (RCPSP) is more complex and consists of two types of flexible and fixed parts. The flexible parts comprise the semi-finished products and each part has multiple routes denoted independently with activities and predictive relationships. This research develops a new Mixed‐Integer Nonlinear Programming (MINLP) model to minimize the makespan. The proposed mathematical model identifies the optimal routes and, consequently, determines the optimal project network. Also, it allocates renewable resources to each production activity. Production sequencing of activities is optimized by the proposed model. A new hybrid approach by regarding GA and PSO in a binary solving space is introduced to handle two main sub-problems of RCPSP-MR in production environments, namely route selection and production scheduling. To evaluate the presented optimization model and algorithm, 60 test problems in various sizes are reported in detail.

    Keywords: Flexible production networks, RCPSP, Production projects, Production scheduling problem, Mathematical model, Meta-heuristic algorithm, Multiple routes
  • A. Esmaili Dooki *, P. Bolhasani, A. Alam Tabriz
    Nowadays the Resource Constrained Project Scheduling Problem (RCPSP) has triggered a substantially significant issue among scheduling problems. The purpose of RCPSP is minimizing the duration of the projects due to both limited available resources and precedence constraints. Indeed, it attempts to consume the total resources by finding the best duration for each activity. This paper proposes a new multi-objective mathematical model for multi-mode RCPSP with interruption to minimize the completion time of the project, maximize the Net Present Value (NPV) of the project, and minimize the allocating workforce’s costs to perform required skills of all activities. To solve the proposed model, an efficient method based on Me measure is used to cope with the uncertainties, and TH method is utilized to convert the multi-objective method into the single one. Furthermore, this paper presents a novel hybrid meta-heuristic algorithm based on Imperialist Competitive Algorithms (ICA) named Self-Adaptive Imperialist Competitive Algorithm (SAICA) to solve the mathematical model which has never been used to solve this type of problems before. Also, to evaluate the proposed method, its performance is investigated against some meta-heuristic algorithms: Differential Evolution (DE) and Imperialist Competitive Algorithm (ICA). Then, a numerical example, two case studies and a real case study have been carried out to embody both validity and efficiency of the presented approach. The obtained results embody that the proposed SAICA is more effective and practical in comparison with DE, ICA, and BCO in decreasing the project duration and also, the considerable effect on solutions confirms the quality of the proposed method.
    Keywords: Project scheduling, Resource-Constrained Project Scheduling Problem (RCPSP), Meta- heuristic algorithm, Self-Adaptive Imperialist Competitive Algorithm (SAICA)
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