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multi-objective modeling

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تکرار جستجوی کلیدواژه multi-objective modeling در مقالات مجلات علمی
  • سید مصطفی نصرت آبادی*، علی پیوند، مرتضی جدیدالاسلام
    امروزه با توجه به نگرانی آلودگی و گازهای گلخانه ای، تولید یک انرژی پاک و استفاده از انرژی های تجدیدپذیر به بهترین نحو (با بازدهی بالا) مسئله بسیار مهمی است. اگر چه همیشه اهداف اقتصادی از اهداف زیست محیطی بیشتر مورد توجه قرار گرفته است، اما در این مقاله، در برنامه ریزی بهینه پیشنهادی سیستم در شبکه میکروانرژی ملاحظات بیشتری به منظور در نظرگیری مسئله زیست محیطی صورت گرفته است. این سیستم بهینه، سیستم هاب انرژی را که بخش اصلی شبکه میکروانرژی است، به صورت شبکه مبتنی بر CCHP که با انرژی های تجدیدپذیر ترکیب شده است، مورد مطالعه قرار می دهد. این سیستم از سه هاب انرژی و دستگاه های ذخیره ساز و مبدل انرژی استفاده می کند. از این رو در این مقاله، یک چارچوب برنامه ریزی چندمرحله ای برای سیستم هاب انرژی و برای بهینه کردن عملکرد آن شامل کاهش آلودگی و هزینه عملیاتی پیشنهاد شده است. در این مدل برای توان تولیدی توسط منابع انرژی تجدیدپذیر حدود بالا و پایین در نظر گرفته شده است تا بیانگر احتمال انقطاع توان به دلیل نوسانات آن ها باشد. همچنین، با در نظر گرفتن چندین تابع هدف میتوان شرایط تصمیم گیری بهینه را برای اپراتور تصمیم گیرنده تضمین کرد. برای حل مسئله چند هدفه در این مقاله از روش اپسیلون پیشرفته استفاده شده است. علاوه برآن در این مقاله دو روش تصمیم گیری بهینه پیشنهاد و با یکدیگر مقایسه شده اند. نتایج بدست آمده پس از اجرای مدل پیشنهادی نشان دهنده کارایی مدل در کاهش هزینه و آلودگی زیست محیطی می باشد.
    کلید واژگان: مدلسازی چند مرحله ای، روش اپسیلون مقید پیشرفته، شبکه میکرو انرژی، مدلسازی چند هدفه، هاب انرژی، تولید همزمان برق گرمایش و سرمایش (CCHP)
    Seyyed Mostafa Nosratabadi *, Ali Peivand, Morteza Jadidoleslam
    Today, due to the concern of emission and greenhouse gases, generation of a clean energy and using renewable energies in the best way (with high efficiency) is a very important issue. Although economic goals have always been more important than environmental goals. In this paper, more considerations have been made in order to consider the environmental issue in the proposed optimal scheduling of the system in the micro energy grid. This optimal system studies the energy hub system, which is the main part of the micro-energy grid, in the form of a CCHP-based network combined with renewable energies. This system uses three energy hubs, energy storage, and converter devices. Therefore, in this paper, a multi-stage planning framework is proposed for the energy hub system and to optimize its performance, including reducing emission and operational cost. In this model, upper and lower limits are considered for the power produced by renewable energy sources to indicate the possibility of power interruption due to their fluctuations. Also, by considering multiple objective functions, optimal decision conditions can be guaranteed for the decision operator. To solve the multi-objective problem, the augmented epsilon-constraint method is used. In addition, two optimal decision-making methods have been proposed and compared too. The results obtained after the implementation of the proposed model show the efficiency of the model in reducing the cost and environmental emission.
    Keywords: Multi-Stage Modeling, Augmented Epsilon-Constraint Method, Micro-Energy Grid, Multi-Objective Modeling, Energy Hub, CCHP
  • Masoomeh Zeinalnezhad *, Zohreh Ebrahimi, Towhid Pourrostam
    Nowadays, one of the major concerns of investors is choosing a realistic stock portfolio and making proper decisions according to an individual's utility level. It is essential to consider two conflicting goals of return and risk for profitability; as a result, balancing the above goals has been identified as an investment concern. This paper modifies and optimizes a multi-objective and multi-period stock portfolio considering cone constraints and uncertain and stochastic discrete decisions. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to solve the model due to the issue's complexity. Two objective functions in the model could be explained by maximizing expected returns and minimizing investment risk. The Pareto chart of the problem was drawn, which allows investors to make decisions based on various levels of risk. Another result obtained in this study is calculating the percentage of optimal amounts assigned to each asset, providing a base for investors to avert investing in unsuitable assets and incurring losses. Finally, a sensitivity analysis of essential parameters was performed, which is critical in this issue. According to the results, increasing the number of problem constraints provides a base for the model reaction, and the optimal percentage allocated to each asset varies. Therefore, this prioritizes restrictions in different situations and according to the investors' choice.
    Keywords: Genetic Algorithm, Optimization, Stock Portfolio, Cone Constraints, Multi-Objective Modeling, Discrete Decisions
  • Fateme Ghaffarifar*, Seyed Hadi Nasseri, Reza Tavakkoli Moghaddam

    One of the most important and widely used problems in the logistics part ‎of ‎any supply chain is the location-routing problem (LRP) of vehicles. The ‎‎purpose is to select distribution centers to supply goods for ‎‎customers and create suitable travel routes for vehicles to serve ‎customers.‎ Studies conducted in the field of supply chain logistics systems have shown that if vehicle travel routing is neglected when locating supply centers, the costs of the logistics system may increase dramatically. Therefore, in the LRP problem, the location of supply centers and the routing of vehicles are considered simultaneously. In this paper, we will present a multi-objective model for vehicle location-routing problems with a flexible fuzzy ‎approach. Its' goals are to make strategic decisions to deploy ‎candidate supply centers at the beginning of the planning horizon, as well as ‎form the vehicle travel at the tactical level to serve the customers in ‎short-term periods of time. Therefore, in ‎order to adapt the mathematical model to the real conditions, the ‎constraints related to the capacity of the vehicles have been considered in a ‎flexible fuzzy state, and also the problem has been modeled in a multi-period state along with the presence of the distance limit and the ‎accessibility factor for each vehicle. The evaluation criterion is to minimize costs related to the establishment of candidate supply centers, the fixed cost of using vehicles and transportation costs, as well as maximizing customer satisfaction by reducing shortage costs and reducing harmful environmental effects. To solve the model, it is first converted into a single-objective model using the weight method and then solved using the proposed algorithm. Finally, using a numerical example in the field of waste management, the effectiveness of the proposed solution method is shown. It should be mentioned that the model was solved using GAMS software and the results are shown.

    Keywords: Supply Chain, Location-routing problem, Fuzzy flexible programming, Multi-objective modeling, Waste management
  • Ali Salmasnia, Mohammad Mousavi Saleh, Hadi Mokhtari*

    This paper addresses a situation in which a firm is willing to locate several new multi-server facilities in a geographical area to provide a service to his customers within the M/M/m/K queue system. As a new assumption, it is also considered that there is already operating competitors in such system. This paper is going to find the location of facilities in a way that the market share of entering firm is maximized. For this purpose, simultaneous minimization of total cost and maximum idle time in each facility is considered as two objective functions in the model. The total cost consists of two parts: (1) the fixed cost for opening a new facility, and (2) the operational costs regarding to the customers, which depends on travel time to the facility and the waiting time at the facility. In addition, in order to make the problem more adapted to real-world situations, two new constraints on budget and number of the servers in each facility are added to the model. Eventually, to tackle the suggested problem, a non-dominated sorting genetic algorithm (NSGA-II) and a non-dominated ranked genetic algorithm (NRGA) are utilized. Finally, the performance of algorithms are investigated via analyzing a set of test problems.

    Keywords: Competitive location problem, 𝑀 𝑚 𝐾 queuing system, Multi-server facilities, Multi-objective modeling, NSGA-II, NRGA
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