stochastic programming
در نشریات گروه فنی و مهندسی-
Portfolio optimization is a widely studied problem in financial engineering literature. Its objective is to effectively distribute capital among different assets to maximize returns and minimize the risk of losing capital. Although portfolio optimization has been extensively investigated, there has been limited focus on optimizing portfolios consisting of cryptocurrencies, which are rapidly growing and emerging markets. The cryptocurrency market has demonstrated significant growth over the past two decades, offering potential profits but also presenting heightened risks compared to traditional financial markets. This situation creates challenges in constructing portfolios, necessitating the development of new and improved risk management models for cryptocurrency funds. This paper utilizes a new risk measurement approach called Conditional Drawdown at Risk (CDaR) in constructing portfolios within high-risk financial markets. Traditionally, portfolio optimization has been approached under certain conditions, considering risk and profit as decision criteria. However, recent approaches have addressed uncertainty in the decision-making process. To contribute to the advancement of scientific knowledge in this field, this paper proposes a new mathematical formulation of CDaR based on a chance-constrained programming (CCP) approach for portfolio optimization. To demonstrate the effectiveness of the proposed model, a practical empirical case study is conducted using real-world market data from 10 months focused on cryptocurrencies. The results obtained from this model can provide valuable guidance in making investment decisions in high-risk financial markets.Keywords: Portfolio Selection, Conditional Drawdown At Risk, Stochastic Programming, Chance Constrained Programming, Cryptocurrency
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یکی از تهدیدها و چالش های اساسی در عملکرد پروژه ها دوباره کاری است که می تواند منجربه افزایش ریسک عدم تحویل به موقع و با صرف بودجه بیشتر شود. روش های کلاسیک زما ن بندی پروژه، قابلیت مدل سازی دوباره کاری و ساختار تصادفی پروژه ها در فاز برنامه ریزی پروژه را ندارند. مطالعه حاضر، با استفاده از تکنیک گرت پروژه های تصادفی بافرض وجود امکان دوباره کاری را مدل می کند. هدف از این مطالعه، انتخاب بهترین حالت اجرایی برای هر فعالیت است به گونه ای که پروژه در مدت زمان معین با صرف منابع دردسترس محدود خاتمه یابد. در این مطالعه، محققین یک مساله زما ن بندی پروژه بافرض محدودیت منابع و توازن زمان-هزینه برای یافتن نتایج مطلوب پیش از وقوع قطعی پارامترهای تصادفی توسعه داده اند. مساله موردنظر با استفاده از رویکرد تئوری گراف جریان، قانون میسون و روش شبیه سازی مونت کارلو حل شده است. برای نشان دادن کارایی روش پیشنهادی، یک مثال موردی عددی توسعه یافته است که نتایج حل آن، توانمندی روش پیشنهادی را نشان می دهد.
کلید واژگان: زما ن بندی پروژه، شبکه گرت، دوباره کاری، بهینه سازی تصادفی، شبیه سازی مونت کارلوJournal of Industrial Engineering Research in Production Systems, Volume:12 Issue: 24, 2025, PP 35 -47In practice, rework is a major threat to projects’ performance and increases the risk of not delivering the outcomes on time and on budget. The classical project scheduling methods are incapable in considering the rework and stochastic structure for projects in planning phase. The current study models the stochastic projects through a GERT network in which occurrence of rework is allowed. The aim is to find the optimal execution mode for each activity in a way that the project completes within a desire time and the resource constraint is satisfied. We develop a multi-mode time-cost trade-off resource constraint project scheduling problem under uncertainty to find a here-and now solution to minimize project completion time. The problem is solved using a combination of flow-graph theory, Mason’s rule and Monte-Carlo simulation method. To represent the efficiency of proposed solution methodology, a stochastic numerical experiment is generated and the obtained results show the capability of the solution method.
Keywords: Project Scheduling, GERT Network, Rework, Stochastic Programming, Monte-Carlo Simulation -
The challenge of designing a Closed-Loop Supply Chain (CLSC) under conditions of uncertainty and partial disruptions is complex and demanding. The concept of a CLSC involves integrating reverse logistics into the traditional forward supply chain to establish a sustainable and environmentally friendly system. However, uncertainties and partial disruptions create significant obstacles to achieving an efficient and dependable CLSC. In order to address these challenges, the concept of chance constraint is introduced, allowing for the consideration of probabilistic uncertainties in decision-making. The goal is to develop a robust CLSC model capable of effectively managing uncertain parameters such as demand, rate of return, and product quality. The Markowitz method is utilized to address uncertainty in the objective function by combining the mean with a coefficient of standard deviation. The study's results demonstrate that incorporating uncertainty into the model leads to increased profitability compared to the deterministic model. The uncertain model is more responsive to demands and considers the dynamics of confidence inventory, leading to improved decision-making. Strategic decisions, such as the number of production, distribution, and destruction facilities, remain consistent in both models. However, the capacity of destruction centers in the uncertain model is slightly smaller due to the consideration of uncertain product quality. Furthermore, incorporating uncertainty into the model has contributed to enhancing the model's clarity and facilitating improved decision-making. This increase in profitability can be attributed to the model's heightened responsiveness to demands, as well as its dynamic approach to managing confidence inventory.Keywords: Stochastic Programming, CLSC, Uncertainty, Disruption
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حمل ونقل دریایی شاخه ای از حمل ونقل است که نقشی حیاتی در تجارت بین الملل ایفا می کند. پایانه های کانتینری در بنادر، تنها نقطه اتصال میان حمل ونقل دریایی و زمینی کالا در زنجیره جهانی حمل ونقل کالا را تشکیل می دهند. یکی از چالش برانگیزترین عملیات پایانه کانتینری زمان بندی جرثقیل های اسکله به منظور تخلیه و بارگیری محموله های کانتینری از شناور پهلوگیری شده در اسکله می باشد. با این وجود، به علت دلایل مختلف عدم قطعیت مانند خرابی جرثقیل ها در حین انجام کار، تاخیر در زمان ورود شناور و... باعث ایجاد اختلال در برنامه زمان بندی اولیه جرثقیل های اسکله می شود. در این تحقیق روشی جهت ارائه برنامه زمان بندی مجدد جرثقیل های اسکله در شرایط عدم قطعیت با رویکرد کاهش تاخیر وارده ارائه شده است. مدل سازی به کار گرفته شده به صورت برنامه عدد صحیح مختلط می باشد که برای اجرای مدل برای حل دقیق از روش GAMS و برای حل مسئله در ابعاد واقعی استفاده از الگوریتم تکاملی ژنتیک می باشد. نتایج این تحقیق نشان می دهد که انحراف ایجاد شده بین زمان بندی اولیه جرثقیل های اسکله و زمان بندی مجدد در شرایط تصادفی و اعمال پارامترهای عدم قطعیت به میزان حداقل 27% کاهش می یابد.کلید واژگان: الگوریتم ژنتیک، برنامه ریزی تصادفی، زمان بندی مجدد جرثقیل های اسکله، مدیریت اختلال، عدم قطعیتMaritime transport is a branch of transport that plays a vital role in international trade. Container terminals in ports are the only connection point between sea and land freight in the global supply chain. One of the most challenging operations of the container terminal is scheduling the quay cranes to loading and discharge container from the containership at the berth. However, due to various reasons of uncertainty such as failure of cranes during operation, delay in the arrival time of the vessel, etc., cause disruption in the initial scheduling of quay cranes. In this research, a method has been proposed to adjust the rescheduling schedule of quay cranes in uncertainty conditions with the approach of reducing the delay of operation. The modeling used is a mixed integer program that uses the GAMS method to process the model for accurate solution and to use the genetic algorithm to solve the problem in real dimensions. The results of this study show that the deviation between the initial scheduling of quay cranes and rescheduling in random conditions and the application of uncertainty parameters is reduced by at least 27%.Keywords: Genetic Algorithm, Stochastic Programming, Quay Crane Rescheduling, Disruption Management, Uncertainty
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A coalition loyalty program (CLP) is a business strategy adopted by companies to increase and retain their customers. An operational challenge in this regard is to determine the coordination mechanism with business partners. This study investigated the role of revenue-sharing contracts (RSCs) considering customer satisfaction in coalition loyalty reward supply chain planning. A two-stage stochastic programming approach was considered for the solution considering the demand uncertainty. We aimed to investigate the impact of RSCs on the decision-making and profitability of the host firm of this supply chain taking into account the maximization of the profit coming from the CLP compared to the more common wholesale price contract (WPC). After the model was solved, computational experiments were performed to evaluate and compare the effects of RSCs and WPCs on the performance of the loyalty program (LP). The results revealed that RSC is an effective incentive to increase the host’s profit and reduce its cost. These findings add new insights to the management literature, which can be used by business decision makers.
Keywords: loyalty program, supply chain, reward, uncertainty, stochastic programming, points -
با نفوذ منابع انرژی تجدیدپذیر و نیز افزایش تراکم در شبکه های انتقال، اهمیت مسیله در مدار قرار گرفتن بهینه واحدهای نیروگاهی (UC) بیش از پیش شده و بهره برداران سیستم قدرت نیازمند راهکارهای موثر برای یافتن جواب مناسب برای این مسیله اند. در این مقاله مدلی برای مسیله UC با در نظر گرفتن ظرفیت دینامیکی خطوط انتقال (DLR) در حضور منابع بادی ارایه گردیده تا با الحاق راهکارهای شبکه هوشمند در سیستم قدرت، بهره گیری موثرتر از ظرفیت موجود خطوط انتقال صورت پذیرد. به منظور انطباق رویکرد پیشنهادی با شرایط واقعی، عدم قطعیت پارامترهای نایقین مسیله شامل سرعت باد، بار شبکه و دمای محیط توسط روش سناریو بنیان در قالب یک برنامه ریزی تصادفی مدل می گردد. برای تولید سناریوها روش مونت کارلو و به منظور کاهش سناریو از الگوریتم خوشه بندی K-means استفاده شده است. تمامی محدودیت های مسیله در مدل مورد نظر لحاظ شده و قیود شبکه انتقال با در نظر گرفتن معادلات پخش بار AC در نظر گرفته شده اند. نتایج مطالعات عددی بر روی شبکه 24 باسه IEEE RTS، بیانگر کارایی مدل ارایه شده در کاهش تراکم شبکه انتقال، کاهش هزینه های بهره برداری، کاهش انقطاع توان بادی، و درنهایت بهبود معیارهای فنی شبکه در کنار برنامه ریزی مطمین و پایدار برای سیستم قدرت است. با توجه به نتایج شبیه سازی، حضور DLR باعث افزایش دمای هادی به میزان 7 درجه سانتی گراد در محدوده مجاز می شود که این امر باعث افزایش بارگذاری خط به میزان حدود 34% نسبت به ظرفیت استاتیکی آن شده است. به این ترتیب، حضور DLR کاهش 33/15 درصدی در هزینه کل بهره برداری را موجب می گردد. همچنین مقدار قطع بار اجباری و توان بادی قطع شده به ترتیب کاهش 9/88 و 7/89 درصدی را در حضور DLR نشان می دهند.کلید واژگان: در مدار قرار گرفتن بهینه واحدهای نیروگاهی، ظرفیت دینامیکی خطوط، نیروگاه بادی، برنامه ریزی تصادفیWith penetration of renewable energy sources and increasing congestion in transmission networks, the issue of unit commitment (UC) problem has increased in importance, and power system operators demand effective approaches to find a suitable solution to this problem. In this paper, by considering a dynamic line rating (DLR) in the presence of wind power units, a model has been offeredfor the UC problem to use the existing capacity of lines more effectively through adding smart-grid technologies to the power system. To adapt the proposed approach with real conditions, uncertainties of the problem parameters including wind speed, network load, and ambient temperature were modeled by a scenario-based method in form of a stochastic programming. A Monte-Carlo simulation was used to generate those scenarios, and K-means clustering algorithm was employed so as to reduce scenarios . All problem limitations were taken into account, and transmission network constraints were considered by AC load flow relations. Numerical results on the IEEE RTS-24-bus system demonstrated the efficiency of the presented model in reducing transmission congestion, operating costs, wind power curtailment; and finally the enhancement of the network’s technical criteria along with the reliable and stable planning for the power system. Rgarding simulation results, the presence of DLR increased the conductor temperature up to 7 oC by satisfying the maximum allowable temperature. This resulted in 34% higher loading of line compared to a static rating. In this way, the DLR brought about a 15.33% reduction in total operating cost. In addition, the load shedding and wind power curtailment showed reductions of 88.9% percent and 89.7% in the presence of DLR.Keywords: Unit commitment, Dynamic line rating, Wind power generation, Stochastic programming
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Operation scheduling of a Virtual Power Plant (VPP) includes several challenges for the system according to the uncertain parameters, and security requirements, which intensify the need for more efficient models for energy scheduling and power trading strategies. Making suitable decisions under uncertainties, related to Renewable Energy Resources (RES), loads, and market prices impose extra considerations for the problem to make a clearer insight for the system operators to participate in local markets. This paper proposes a new risk-based hybrid stochastic model to investigate the effects of wind turbine power fluctuations on profit function, energy scheduling, and market participating strategies. Also, an incentivized Demand Response Program (DRP) is used, to enhance the system’s efficiency. The results of the study indicate that the proposed model based on Information Gap Decision Theory (IGDT) approach makes a clearer environment for the decision-maker to be aware of the effects of risk-taking or a risk-averse strategy on financial profits. The results show that a 30% of robustness and opportunity consideration would change the profit function from -12.5% up to 14.5%, respectively. A modified IEEE 33 bus test system is used to simulate a technical VPP considering the voltage stability and thermal capacity of line requirements.
Keywords: Virtual Power Plant, Uncertainty, Stochastic Programming, IGDT, Scheduling -
Due to the importance of the health field, the problem of determining the shift scheduling of care providers has been addressed in many studies, and various methods have been proposed to solve it. Considering different skills and contracts for care providers is one of the essential issues in this field. Given the uncertainty in patients' demands, it is a crucial issue as to how to assign care providers to different shifts. One area facing this uncertainty is the provision of services to cancer patients. This study develops a stochastic programming model to account for patient demand uncertainty by considering different skills and contracts for care providers. In the first step, care providers are assigned to work shifts, then, in the second step, the required overtime hours are determined. The sample average approximation method is presented to determine an optimal schedule by minimizing care providers' regular and overtime costs with different contracts and skills. Then, the appropriate sample size is 100, determined based on the Monte Carlo and Latin Hypercube methods. In the following, the lower and upper bounds of the optimal solution are calculated. As the numerical results of the study show, the convergence of the lower and upper bounds of the optimal solution is obtained from the Latin Hypercube method. The best solution is equal to 189247.3 dollars and is achieved with a difference of 0.143% between the upper bound and lower bounds of the optimal solution. The Monte Carlo simulation method is used to validate the care provider program in the next stage. As shown, in the worst case, the value of the objective function is equal to 197480 dollars.
Keywords: Healthcare, Shift scheduling, Uncertainty, Stochastic programming, Sample average approximation -
Journal of Quality Engineering and Production Optimization, Volume:8 Issue: 1, Winter-Spring 2023, PP 115 -132During pandemics and epidemics, healthcare systems may respond quickly to massive increases in demand by establishing surge capacity in facilities. However, adding new resources may not be the most effective approach. Given the inherent uncertainty of demand during pandemics, this paper develops a stochastic optimization model designed to improve the allocation and sharing of critical resources. The objective is to enhance the responsiveness of healthcare systems to substantial surges in demand during pandemics. The model integrates warehouse selections for vendor-managed inventory (VMI), inventory policies, and delivery decisions to investigate a healthcare supply network configuration problem. This problem considers multiple sourcing, various products, multiple periods, and lateral transshipment. Numerical experiments are conducted to verify the advantage of the proposed stochastic model, which, despite its higher overall cost, demonstrates its superiority over the deterministic approach. The results further indicate that resource sharing can significantly improve the resilience of healthcare systems and enhance patients' access to care during pandemics.Keywords: Epidemics, Pandemics, Healthcare Supply Chain, Resource Sharing, Stochastic Programming, Vendor-Managed Inventory
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As global trade flourishes, terminals endeavor to get higher income while adapting to an expanded intricacy concerning terminal administration tasks. Perhaps the most common issues such terminals encounter is the Berth Allocation Problem (BAP), which involves allotting vessels to a bunch of berths and time allotments while at the same time limiting goals, for example, total stay time or total assignment cost. Complex formats of actual terminals present spatial constraints that restrict the mooring and departure of vessels. In spite of the fact that significant research has been carried out with regard to the BAP, these real-world limitations have not been considered in an overall manner. In this paper, a stochastic Multi-Period Berth Allocation-Scheduling Problem in Different Terminals with Irregular Layouts (SBAP) considering multi-Period modes, generalized precedence relations are developed. To solve the (SBAP), a solution approach based on Stochastic Chance Constraint Programming (SCCP) and a solution approach based on Two-Stage Stochastic Linear Programming with Recourse (TSSLPR) is proposed. A mathematical model is solved to show the applicability of the suggested model and solution approach.
Keywords: Berth Allocation Problem, Stochastic Programming, Multi-Period, Stochastic Optimization -
International Journal of Research in Industrial Engineering, Volume:11 Issue: 2, Spring 2022, PP 188 -204The growing need for adequate and safe blood and the high costs of health systems have prompted governments to improve the functioning of health systems. One of the most critical parts of a health system is the blood supply chain, which accounts for a significant share of the health system's costs. In the present study, with an operational approach, the total network costs are minimized along with the minimization of transportation time and lead time of delivery of blood products. Also, determining the optimal routing decisions is improved the level of responsiveness and reliability of the network. In this research, a multi-objective stochastic nonlinear mixed-integer model has been developed for Tehran's blood supply chain network. Robust scenario-based programming is capable of effectively controlling parametric uncertainty and the level of risk aversion of network decisions. Also, the proposed reliability approach controls the adverse effects of disturbances and creates an adequate confidence level in the capacity of the network blood bank. Lastly, the model is solved through the Lagrangian relaxation algorithm. Comparison of the results shows the high convergence rate of the solutions in the Lagrangian relaxation algorithm.Keywords: blood supply chain, Stochastic programming, robust, Reliablility, Lagrangian Relaxation
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در این مقاله، یک مدل برنامه ریزی تصادفی دو مرحله ای مبتنی بر بهینه سازی چند هدفه به منظور بهره برداری بهینه ریزشبکه هوشمند با هدف کمینه سازی هزینه بهره برداری و آلاینده های زیست محیطی با حضور منابع تجدیدپذیر و پاسخگویی بار پیشنهاد شده است. در مدل پیشنهادی، خطای پیش بینی توان منابع تجدیدپذیر به وسیله توابع چگالی احتمال مدلسازی شده و برنامه های پاسخ بار، جهت پوشش عدم قطعیت توان منابع تجدیدپذیر استفاده شده است. در مدل مساله فرض می شود که بهره بردار ریزشبکه برای مدیریت بهینه شبکه، در دو حوزه بهره برداری در وضعیت پایه و حوزه مربوط به سناریوهای مختلف برای تولید منابع تجدیدپذیر، تصمیم گیری می کند. وضعیت پایه ی ریز شبکه، اشاره به حالتی است که توان منابع تجدید پذیر برابر با مقادیر پیش بینی شده در نظر گرفته شوند. برای حل مساله، از روش بهینه سازی چندهدفه ازدحام ذرات (MOPSO)[i] و برای استخراج خروجی از فضای پرتو، از روش TOPSIS استفاده شده است. مدل پیشنهادی بر روی یک ریزشبکه هوشمند نمونه پیاده سازی شده و نتایج عددی نشان دهنده کارایی مدیریت سمت تقاضا در کاهش هزینه ها، آلایندگی و پوشش عدم قطعیت ناشی از توان تولیدی منابع بادی و خورشیدی می باشد.
کلید واژگان: ریز شبکه هوشمند، منابع تجدید پذیر، عدم قطعیت، پاسخ بار، برنامه ریزی انرژی و رزرو، برنامه ریزی تصادفیJournal of Iranian Association of Electrical and Electronics Engineers, Volume:19 Issue: 1, 2022, PP 75 -88In this paper, a two-stage stochastic programming model based on a multi-objective optimization has been proposed for optimal operation of smart micro-grid (MG) aiming at minimizing operational costs and environmental emissions in presence of renewable resources and demand response. In the presented model, the forecasting error of the renewable resources productions is modeled by probability density functions and demand response has been implemented to cover the uncertainty of the renewable resources. Here, it is assumed that the MG operator decides on two stages for optimum management of its network; first stage is the operation in the base state and the second one is pertaining to the domains of different scenarios for generation of renewable resources. The base state of the micro-grid refers to the situation in which the active power productions of renewables are equal to the predicted values. To solve the problem, Multi-Objective Particle Swarm Optimization method has been used and TOPSIS technique has been applied to extract the output from the Pareto Frontier. The proposed approach is applied to a typical MG and the numerical results show the efficiency of demand side management in reducing costs and environmental emissions as well as covering the uncertainty resulting from renewables.
Keywords: Smart Micro-Grid, Renewable Resources, Uncertainty, Demand Response, Energy, Reserve Scheduling, Stochastic Programming -
Knowledge transfer can occur on two levels: intra-organizational and inter-organizational. Acquiring knowledge from outside an organization usually requires significant budget and considerable time. However, through awareness and reliance on knowledge already acquired by the personnel, and creating a knowledge flow network, knowledge level of the organization can be increased in the shortest possible time. The present paper addresses the design of a knowledge flow network between the personnel of an organization according to the professional and personal trust levels, teaching and learning capabilities, knowledge level of the personnel, organizational commitment level, type and importance of each knowledge, and the stochastic nature of the knowledge transfer duration. This problem was formulated as a stochastic multi-objective mixed-integer programming. The objectives of the proposed model were maximizing the knowledge level and minimizing the knowledge transfer time. The model was solved using the Lagrangian relaxation algorithm and the CPLEX solver. Results indicate the high efficiency of the Lagrangian relaxation algorithm specially in computational time of large-sized problems. Moreover, the results show that the organizational commitment parameter has more significant influence on the knowledge transfer duration, followed by teaching and learning capabilities.Keywords: Nowledge Flow Network, Knowledge transfer, Stochastic programming, Lagrangian Relaxation
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The Location Routing Problem (LRP), Automatic Guided Vehicle (AGV), and Uncertainty Planner Facility (UPF) in Facility Location Problems (FLP) have been critical. This research proposed the role of LRP in Intelligence AGV Location–Routing Problem (IALRP) and energy-consuming impact in CMS. The goal of problem minimization dispatching opening cost and the cost of AGV trucking. We set up multi-objective programming. To solve the model, we utilized and investigate the Imperialist Competitor Algorithm (ICA) with Variable Neighborhood Search (VNS). It is shown that the ICAVNS algorithm is high quality effects for the integrated LRP in AGVs and comparison, with the last researches, the sensitivity analysis, and numerical examples imply the validity and good convexity of the purposed model according to the cost minimization.
Keywords: location-routing, Automatic guided vehicle, Stochastic programming, Uncertainty, meta-heuristic algorithms -
با حضور گسترده خودروهای الکتریکی در جوامع امروزی لازم است مدیریت هوشمند خودروهای الکتریکی مورد بررسی قرار بگیرد. از طرفی، ادغام منابع انرژی تجدیدپذیر در پارکینگ خودروهای هوشمند سبب کاهش وابستگی به شبکه بالادستی و کاهش هزینه های بهره برداری می شود. در همین زمینه، در مقاله پیش رو بهره برداری بهینه پارکینگ هوشمند خودروهای الکتریکی مجهز به منابع انرژی تجدیدپذیر مورد بررسی و بحث قرار گرفته است. همچنین، به منظور مدل سازی عدم قطعیت منابع انرژی تجدیدپذیر، قیمت برق شبکه بالادستی و رفتار صاحبان خودروهای الکتریکی از روش برنامه ریزی تصادفی استفاده شده است. منظور از عدم قطعیت رفتار صاحبان خودروهای الکتریکی شامل زمان ورود خودرو به پارکینگ، زمان خروج از پارکینگ و انرژی اولیه آن ها هنگام ورود به پارکینگ هستند که به صورت پارامتر دارای عدم قطعیت در نظر گرفته شده اند. مسیله بهینه سازی یادشده به صورت یک مدل برنامه نویسی آمیخته با اعداد صحیح (MILP1) ارایه شده و توسط حل کننده CPLEX در نرم افزار GAMS حل شده است. نتایج شبیه سازی نشان می دهد بهره بردار پارکینگ هوشمند با شارژ و دشارژ هوشمند خودروهای الکتریکی می تواند سود کسب کند. همچنین، در حضور سناریو ها و عدم قطعیت های مختلف، استراتژی عملکرد پارکینگ هوشمند متفاوت است، به طوری که بیشترین و کمترین سود کسب شده برابر با 84/1740 و 22/1359 دلار حاصل شد، ولی با در نظر گرفتن احتمال تمام سناریوها سود مورد انتظار برابر با 31/1571 دلار حاصل شد. در نتیجه عدم قطعیت های مختلف، سود حاصل از پارکینگ هوشمند را تحت تاثیر قرار می دهند.
کلید واژگان: برنامه ریزی تصادفی، پارکینگ هوشمند خودروهای الکتریکی، عدم قطعیت، منابع انرژی تجدیدپذیرIntroductionBy the extensive presence of electric vehicles (EVs) in today’s communities, the smart management of EVs should be completely investigated. On the other hand, the integration of renewable energy sources (RESs) in the smart electric vehicles parking lot (SEVsPL) reduces the dependency on the upstream network and also the whole operation costs. So, under finite energy sources around the world, in order to supply various demands along with generating low greenhouse gas emissions (GHGEs), policymakers and different scholars seek to find cost-effective, eco-friendly, efficient, and sustainable solutions. With investigating the literature review in the field of management and optimal operation of SEVsPL can be obviously concluded that multiple approaches and strategies from different perspectives were used to improve the efficient usage of energy based on smart grids (SGs) framework in SEVsPL. It was indicated that none of the analyzed references, addressee the simultaneous effects of different uncertainties, i.e., the random behavior of EVs owners, RES, and the upstream network electricity price on the charging and discharging processes for the operation of SEVsPL. Certainly, applying the nature of such uncertainty sources in an appropriate path will have significant impacts on the bringing results as close as possible to the real-world solutions.
Model description and method of solutionTherefore, in the current paper, a stochastic scheduling methodology for the optimal operation of SEVsPL has been proposed as an interactive user interface between the respective operator and EVs owners to facilitate charging and discharging activities and operation costs. The total capacity of SEVsPL is 50 in which three various EVs’ models with 10 scenarios for the arrival time, departure time, and initial state of energy (SoE) were considered. Furthermore, the other uncertainties oriented from RES and the upstream network electricity price have also taken into account with relative 10 scenarios. It is worth to mention that Mont Carlo simulation (MCS) implemented in MATLAB software to produce 1000 scenarios and then reduce them to 10 scenarios with the assistance of SCENRED algorithm in GAMS software. This stochastic optimization model of SEVsPL was formulated as mixed integer linear programming (MILP) problem, which was solved by CPLEX solver in GAMS software.
Simulation results and discussionAccording to the obtained results in different scenarios, it was obviously seen that the lowest profit of SEVsPL was occurred in scenario 8, which can be considered as the worst case scenario, although, the highest profit has been achieved in scenario 9 and can be taken as the best case scenario. So, it can be mentioned that the optimal utilization of SEVsPL in the presence of RES uncertainty and the unpredictable behavior of EVs can be different. By considering the probabilities of the aforementioned uncertainties, the expected profit for SEVsPL was $ 1571.31. For the plotted figures of photovoltaic (PV) and wind turbine (WT) power plants in scenario 9, the generated WT power can be sold to the upstream network within the period in which EVs have not yet entered the SEVsPL and also for the PV generated power can be utilized to charge EVs. To realize the aim of the SEVsPL operator in maximizing his/her profit, the smart operating strategy was accomplished in purchased /sold power from /to the upstream network, which were indicated in relative figures for two worst case and best case scenarios. These outcomings highlighted that sold power has happened when electricity price is high (e.g., 10 o'clock in scenario 8 and 17 o'clock in scenario 9); however, the purchased power has taken place when electricity price is low (e.g., 10 o'clock in Scenario 9 and 12 o'clock in Scenario 8). This strategy is true for all EVs in all scenarios such that results to gain profit from electricity price arbitrage by the SEVsPL operator. If the EVs owners are looking for the maximum charge when leaving the SEVsPL, the respective operator will earn less profit according to different gamma coefficients presented in relevant expected SEVsPL profit’s figure. In other words, the chance of discharging EVs will be less when attending SEVsPL.
ConclusionsThe proposed stochastic optimal operation strategy of SEVsPL in this paper has been accomplished to flatten charging and discharging processes along with operation costs. In the presence of different considered uncertainties, the optimal operation strategy of SEVsPL can be diverse for different scenarios such that the highest and lowest profits were equal to $1740.84 and $1359.22, respectively. Although by considering the probability of all scenarios, the expected profit was equal to $ 1571.31. These various uncertainties affect the obtained profit of SEVsPL which the respective operator seeks to charge EVs at low price hours and discharge EVs at high price hours under the smart performance strategy.
Keywords: Stochastic Programming, smart electric vehicle parking lots, Uncertainty, renewable energy sources -
International Journal of Industrial Electronics, Control and Optimization, Volume:4 Issue: 4, Autumn 2021, PP 397 -407This paper presents microgrid (MG) operation constrained to the reliability, flexibility, and environment indices in the presence of distributed generations (DGs) and energy storage systems (ESSs). The proposed scheme minimizes the total expected operating cost of MGs and DGs. It is also subject to alternating current (AC) power flow equations of MGs, constraints of operation, reliability, and flexibility in MG, and operation model of power sources and storage devices. Stochastic programming is incorporated to model uncertainties of load, energy price, the active power of renewable energy generation, availability of MG equipment, sources, and storage devices. Following on, a hybrid solver formed by combining artificial bee colony (ABC) and sine-cosine algorithm (SCA) is adopted to achieve the optimal solution with approximate conditions of unique ultimate response. Eventually, the suggested scheme is implemented on a 69-bus radial MG, where the numerical results confirm the capability of the scheme in improving the operation, reliability, flexibility, and environment status of the MG.Keywords: Clean microgrid operation, Flexibility index, Hybrid evolutionary algorithm, Reliability index, Stochastic programming
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برنامه ریزی عملیات نگهداری و تعمیر روسازی نقش بسزایی در اجرای کارآمد سیستم مدیریت روسازی ایفا می کند. این مسئله معمولا با فرض قطعی بودن تمامی پارامترهای موجود بررسی می شود. در حالی که عدم قطعیت زیادی در مسئله دیده می شود. به طور مثال، میزان بودجه به عنوان یکی از مهمترین پارامترهای تاثیرگذار به دلایل مختلفی مانند محدودیت منابع و تغییر سیاست ها با نوسان همراه است. برنامه ریزی عملیات با فرض قطعی بودن بودجه ممکن است به جوابی منجر شود که با جواب بهینه واقعی، فاصله زیادی داشته باشد. هدف این پژوهش، برنامه ریزی عملیات نگهداری و تعمیر روسازی در سطح شبکه با در نظر گرفتن عدم قطعیت بودجه با رویکردی جدید و کاربردی می باشد. برای این منظور، یک مدل برنامه ریزی تصادفی چند مرحله ای عدد صحیح خطی که هدف آن یافتن جوابی است که برای تمامی حالات عدم قطعیت امکان پذیر و بهینه باشد، ارایه می شود. نتایج مدل معرفی شده بر روی یک شبکه موردی با 6 قطعه روسازی نشان داد مدل برنامه ریزی تصادفی به خوبی نوسانات بودجه را لحاظ کرده و راه حلی ارایه می کند که برای سناریوهای مختلف بهینه باشد. مقایسه نوع عملیات انتخاب شده توسط مدل-های تصادفی و قطعی نشان داد که تعداد عملیات پیشگیرانه انتخاب شده توسط مدل تصادفی بیشتر از مدل قطعی است به طوری که از 67/36% از کل عملیات انتخابی در مدل قطعی به 91/40% در مدل تصادفی می رسد. این موضوع موید این نکته است که مدل تصادفی سعی دارد بودجه را با توجه به اثرات منفی افت احتمالی آن بین قطعات بیشتری از طریق انتخاب عملیات پیشگیرانه تقسیم کند.
کلید واژگان: سیستم مدیریت روسازی، برنامه ریزی عملیات نگهداری و تعمیر، عدم قطعیت، بهینه سازی، برنامه ریزی تصادفیMaintenance and rehabilitation planning plays a pivotal role in the implementation of efficient pavement management system. The variables are generally considered deterministic to solve the problem. Nevertheless, this problem tackles with high level of uncertainty. For instance, the budget, as one of the essential criteria, is fluctuated owing to resource limitation, and policy alteration. If the budget is taken into account as deterministic, the result of problem may be considerably different from the absolute optimal solution to the problem. This investigation aims to solve maintenance and rehabilitation problem by consideration of a novel and powerful uncertainty approach. To this end, a multi-stage integer linear uncertainty model is introduced in order to find a solution, which is feasible and optimal in all of the uncertainty modes. The case study of this paper is a network including six pavements. The outcomes indicate that the proposed model is competent to consider budget fluctuation, and it introduces a solution that is optimal for all uncertainty scenarios. The comparison of deterministic and uncertainty models reveals that the number of preventative maintenance selected by uncertainty model is more than that of deterministic model. The number of preventative maintenance is increased from 36.67% to 40.91% if uncertainty is considered in the problem, and it can be postulated that the uncertainty model tries to allocate budget to more segments so as to reduce the likely negative impacts of budget fluctuation on the project.
Keywords: Pavement Management, Maintenance, Uncertainty, optimization, Stochastic Programming -
Level of blood and blood products in human body is very important. Therefor, managing supply of blood is critical issue in healthcare system specially when the system faced with high demand for the product. In natural disasters, demand for blood units increase sharply because of injuries. Hence, efficiency in blood supply chain management play a significant role in this situation in supplying blood for transfusion centers, it is vital to supply in right time to prevent from casualties. Present paper proposes an optimization model for designing blood supply chain network in case of an earthquake disaster. The proposed two-stage stochastic model is Programmed based on scenarios for earthquake in a populated mega-city. The designed network has three layers; first layer is donation areas, the second layer consists distribution centers and facilities and the last layer is transfusion centers. In proposed two-stage stochastic optimization model, decisions of locating permanent collection facilities and amount of each blood type pre-inventory are made in first stage and operation decisions that have dependent on possible scenarios are made in second stage. The model also considers the possibility of blood transfusion between different blood types and its convertibility to blood derivatives regarding medical requirements. In order to solve the proposed two-stage stochastic model, L-shaped algorithm, an efficient algorithm to solve scenario based stochastic models, has been used. In addition, application of the model and the algorithm tests with real data of likely earthquake in Tehran mega city (Densest city of Iran).
Keywords: Blood Supply Chain, Network Design, Stochastic Programming, L-Shaped Exact Method -
در این مقاله، به منظور اجرای بازآرایی شبکه توزیع هوشمند و تعیین ظرفیت تولیدات پراکنده به طور هم زمان، چارچوبی چندهدفه و تصادفی ارایه می شود که در آن اهداف کاهش تلفات، کاهش هزینه های بهره برداری و کاهش انتشار آلاینده های زیست محیطی دنبال می شود. در بیشتر تحقیقات انجام شده درزمینه ی بازآرایی، بار مشترکین و پست های توزیع ثابت فرض شده است؛ درحالی که در واقعیت این بارها دایما در حال تغییر هستند. عموما به جهت برنامه ریزی، بدترین حالت که همان حداکثر بار است را مدنظر قرار می دهند، ولی آرایش به دست آمده با این فرضیه، به طور حتم آرایش بهینه نیست. به همین دلیل برای رفع این مشکل، در این مقاله به منظور پوشش عدم قطعیت های مربوط به بار، چارچوب مسئله بازآرایی و تعیین ظرفیت تولیدات پراکنده در قالب یک مسئله ی برنامه ریزی مقید به شانس ارایه شده و در آن شبیه سازی مونت کارلو به کار گرفته شده است. مسئله بازآرایی و تعیین ظرفیت تولیدات پراکنده به طور هم زمان به منظور دست یابی به اهداف از پیش تعیین شده، یک مسئله بهینه سازی چندهدفه است. به منظور حل مسئله ی بهینه سازی ارایه شده در چارچوب چندهدفه تصادفی این مقاله، از یک روش بهینه سازی قدرتمند تازه معرفی شده به نام رهیافت بهینه سازی مبتنی بر رفتار انسان استفاده شده است. در نهایت چارچوب چندهدفه ی تصادفی ارایه شده، بر روی یک شبکه ی 33 شینه IEEE پیاده سازی شده و نتایج حاصل از آن تحلیل شده است.
کلید واژگان: بازآرایی شبکه توزیع، تولیدات پراکنده، برنامه ریزی مقید به شانس، بهینه-سازی مبتنی بر رفتار انسانJournal of Iranian Association of Electrical and Electronics Engineers, Volume:18 Issue: 2, 2021, PP 153 -161In this paper, to implement the reconfiguration of the intelligent distribution network and sizing the distributed generations (DGs), simultaneously, a stochastic multi-objective framework is presented in which the goals of reducing the energy losses, operating costs and emissions of environmental pollutants are followed. In most of the researches carried out for the network reconfiguration, the customer and distribution substation loads have been assumed to be fixed, while in reality these loads are changing. Generally, in the power system planning, the worst case which is the maximum possible load is considered, but the obtained configuration with this assumption is certainly not the optimal configuration. Therefore, in this paper, in order to cover the uncertainties associated with the load, the framework for the reconfiguration and DGs sizing problem is presented as a chance constrained programming problem and the Monte Carlo simulation is used in it. The problem of reconfiguration and DGs sizing at the same time in order to achieve predetermined goals is a multi-objective optimization problem. In order to solve it, this paper uses a newly introduced powerful optimization method called human-behavior based optimization approach. Finally, the proposed stochastic multi-objective framework is implemented on an IEEE 33-bus network, and the results are analyzed.
Keywords: Distribution system reconfiguration, Distributed generation, Stochastic programming, Human behavior based optimization -
Natural and technological disasters threaten human life all around the world significantly and impose many damages and losses on them. The current study introduces a multi-objective three-stage location-routing problem in designing an efficient and timely distribution plan in the response phase of a possible earthquake. This problem considers uncertainty in parameters such as demands, access to routes, time and cost of travels, and the number of available vehicles. Accordingly, a three-stage stochastic programming approach is applied to deal with the uncertainties. The objective functions of the proposed problem include minimizing the unsatisfied demands, minimizing the arriving times, and minimizing the relief operations costs. A modified algorithm of the improved version of the augmented ε-constraint method, which finds Pareto-optimal solutions in less computational time, is presented to solve the proposed multi-objective mixed-integer linear programming model. To validate the model and evaluate the performance of the methods several test problems are generated and solved by them. The computational results show the satisfactory performance of the proposed methods and effectiveness of the proposed model for delivery of relief commodities in the affected areas.
Keywords: Humanitarian Logistics, Location-routing problem, Disaster management, Multi-objective optimization, Stochastic Programming
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