فهرس المقالات Stochastic programming

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  1 - مدل برنامهریزی تصادفی جهت تخصیص منابع گاز در ایران با رویکرد هزینه امنیت انرژی
  رضا علیخانی عادل آذر علیرضا رشیدی کمیجان
  هدف از این تحقیق، ارائه مدلی جهت تخصیص منابع گاز طبیعی به زیربخشهای مصرفی در ایران میباشد که بااستفاده از برنامه ریزی غیر خطی صورت گرفته است. رویکرد برنامهریزی تصادفی به منظور حصول ترکیب سیستمیبهینه، بکار گرفته شده تا تقاضای گاز بخشهای مختلف مصرف را بر اساس گاز تولید شد أکثر

  هدف از این تحقیق، ارائه مدلی جهت تخصیص منابع گاز طبیعی به زیربخشهای مصرفی در ایران میباشد که بااستفاده از برنامه ریزی غیر خطی صورت گرفته است. رویکرد برنامهریزی تصادفی به منظور حصول ترکیب سیستمیبهینه، بکار گرفته شده تا تقاضای گاز بخشهای مختلف مصرف را بر اساس گاز تولید شده در دسترس، برآورده سازد. مدلبه بررسی تهدیداتی که امکان رخداد آنها در عرضه گاز طبیعی محتمل و ممکن است، میپردازد. خروجی مدل مبینمقادیر تخصیص گاز به هر یک از بخشها بر اساس تهدیدات ممکن در سطح کلان میباشد. نتایج نشان میدهد، سهمقابل توجه گاز به ترتیب باید به بخشهای خانگی-تجاری، نیروگاهها، صنعت، تزریق گاز سبک، صادرات، پتروشیمی، تزریقگاز غنی، حمل و نقل و کشاورزی اختصاص یابد. نوآوری این تحقیق، توانمندی آن در کمی کردن تهدیدات محتمل الوقوعبا استفاده از برنامه ریزی تصادفی و مفهوم هزینه امنیت انرژی است، در حالیکه یک دیدگاه کلنگر از عوامل امنیت انرژیکه بر تخصیص گاز در ایران تأثیرگذار هستند، ایجاد میگردد. تفاصيل المقالة
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  2 - برنامه ریزی هندسی با پارامترهای تصادفی
  راشد خانجانی شیراز
  برنامه ریزی هندسی روش کارایی برای حل رده ای از مسائل بهینه سازی غیر خطی است. برنامه ریزی هندسی به منظور بهینهسازی مسایل طراحی مهندسی تعمیم و توسعه یافته است ولی اکنون به عنوان ابزار قوی در بهینه سازی سایر مواردی که بهشکلی متغیرهای تصمیم گیری در مدل مساله بهینه سازی به ص أکثر

  برنامه ریزی هندسی روش کارایی برای حل رده ای از مسائل بهینه سازی غیر خطی است. برنامه ریزی هندسی به منظور بهینهسازی مسایل طراحی مهندسی تعمیم و توسعه یافته است ولی اکنون به عنوان ابزار قوی در بهینه سازی سایر مواردی که بهشکلی متغیرهای تصمیم گیری در مدل مساله بهینه سازی به صورت نمایی هستند، بکار گرفته می شود.برنامه ریزی هندسی معمولاً با پارامترهای معلوم و مشخص به کار برده شده است. اما واقعیت امر این است که عمدتاً درمسایل واقعی، دسترسی به داده های قطعی برای تصمیم گیرنده امکانپذیر نیست و داده ها به صورت دقیق مشخص نیستند.این داده ها ممکن است به صورتهای مختلف از قبیل کراندار، بازهای، فازی و تصادفی باشند. در این مقاله برنامه ریزیهندسی با پارامترهای تصادفی در نظر گرفته می شود. سپس برای حل آن ابتدا برنامه ریزی تصادفی به یک مساله بهینه سازیهندسی با پارامترهای قطعی تبدیل می شود. با بدست آوردن مدل دوگان مساله برنامه ریزی هندسی قطعی شده، جواب بهینهمساله بهینه سازی تصادفی هندسی بدست می آید. برای توصیف کارایی روش ارایه شده دو مثال ارایه میشود. تفاصيل المقالة
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  3 - Choosing the Best Bundle of Projects: A DEA Approach
  Mahnaz Mahnaz Mirbolouki
  One of the many important decisions organizations must make is project selection. Every project includes an initial plan to run, but not every plan can be implemented as a project. In situations where they lack resources or funds, all different plans must first be able أکثر

  One of the many important decisions organizations must make is project selection. Every project includes an initial plan to run, but not every plan can be implemented as a project. In situations where they lack resources or funds, all different plans must first be able to assess profitability in an accurate way, leading to the selection of a combination of proposals to carry out as projects. This paper provides a method to select the most effective set of proposals while considering the maximum use of available resources. Assessing efficiency is considered by a data envelopment analysis (DEA) model. Note that it is assumed that a vector of limited sources is at hand. This vector of resources can be contained human resource, budget, equipment, and facilities. Here, while reviewing some of the models in the selection project field, a common set of weight approach performance evaluation model for assessing the efficiency of the selected proposals is proposed. In this article it has tried to resolve the problems of former models. تفاصيل المقالة
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  4 - A multi-product vehicle routing scheduling model with time window constraints for cross docking system under uncertainty: A fuzzy possibilistic-stochastic programming
  B. Vahdani SH. Sadigh Behzadi
  Mathematical modeling of supply chain operations has proven to be one of the most complex tasks in the field of operations management and operations research. Despite the abundance of several modeling proposals in the literature; for vast majority of them, no effective أکثر

  Mathematical modeling of supply chain operations has proven to be one of the most complex tasks in the field of operations management and operations research. Despite the abundance of several modeling proposals in the literature; for vast majority of them, no effective universal application is conceived. This issue renders the proposed mathematical models inapplicable due largely to the fact that real-life supply chain problems are set forth in restrained terms or represented less strikingly than they would bear out. This paper is triggered to bridge this gap by proposing a universal mixed integer linear programming (MILP) framework which to large extent simulates many realistic considerations in vehicle routing scheduling problems in cross-docking systems which might have separately been attempted by other researchers. The developed model is pioneer in excogitating the vehicle routing scheduling problem with the following assumptions: a) multiple products are transported between pick-up and delivery nodes, b) delivery time-intervals are imposed on each delivery node, c) multiple types of vehicles operate in the system, d) capacity constraints exists for each vehicle type, and finally e) vehicles arrives simultaneously at cross-docking location. Moreover, to solve the model a hybrid solution methodology is presented by combining fuzzy possibilistic programming and stochastic programming. Finally, in order to demonstrate the accuracy and efficiency of the proposed model, an extensive sensitivity analysis is performed to scrutinize its parameters’ demeanors. تفاصيل المقالة
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  5 - Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange
  Seyed Alireza Miryekemami Ehsan Sadeh Zeinolabedin Sabegh
  10.22034/amfa.2017.536271
  Investor decision making has always been affected by two factors: risk and returns. Considering risk, the investor expects an acceptable return on the investment decision horizon. Accordingly, defining goals and constraints for each investor can have unique prioritizati أکثر

  Investor decision making has always been affected by two factors: risk and returns. Considering risk, the investor expects an acceptable return on the investment decision horizon. Accordingly, defining goals and constraints for each investor can have unique prioritization. This paper develops several approaches to multi criteria portfolio optimization. The maximization of stock returns, the power of liquidity of selected stocks and the acceptance of risk to market risk are set as objectives of the problem. In order to solve the problem of information in the Tehran Stock Exchange in 2017, 45 sample stocks have been identified and, with the assumption of normalization of goals, a genetic algorithm has been used. The results show that the selected model provides a good performance for selecting the optimal portfolio for investors with specific goals and constraints. تفاصيل المقالة
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  6 - Effects of Probability Function on the Performance of Stochastic Programming
  Mohammad Ebrahim Karbaschi Mohammad Reza Banan
  10.22094/joie.2017.567.63
  Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the probl أکثر

  Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochastic optimization problem is transformed intoan equivalent deterministic problem,which can be solved byany known classical methods (interior penalty method is applied here).The paper mainly focuseson investigatingthe effect of applying various probability functions distributions(normal, gamma, and exponential) for design variables. The following basic required equations to solve nonlinear stochastic problems with various probability functionsfor random variables are derived and sensitivity analyses to studythe effects of distribution function typesand input parameterson the optimum solution are presented as graphs and in tables by studyingtwoconsidered test problems. It is concluded that thedifference between probabilistic and deterministic solutions toa problem, when the normal distribution ofrandom variables isused, is very different fromthe results when gamma and exponential distribution functions are used. Finally, it is shownthat the rate of solution convergence tothe normal distribution is faster than the other distributions. تفاصيل المقالة
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  7 - A Benders-Decomposition and Meta-Heuristic Algorithm for a Bi- Objective Stochastic Reliable Capacitated Facility Location Problem Not Dealing with Benders Feasibility-Cut Stage
  AmirHossien ZahediAnaraki Gholamreza Esmaeilian
  10.22094/joie.2021.578550.1599
  This paper addresses a bi-objective two-stage stochastic mixed-integer linear programming model for a stochastic reliable capacitated facility location in which the optimum numbers, locations and as well as shipment quantity of the product between the network nodes for أکثر

  This paper addresses a bi-objective two-stage stochastic mixed-integer linear programming model for a stochastic reliable capacitated facility location in which the optimum numbers, locations and as well as shipment quantity of the product between the network nodes for all scenarios should be determined. Unlike most of previous relevant works, multiple levels of capacities available to the manufacturers in different scenarios are permitted in this study. The proposed objectives of the model include: the minimization of expected sum of installation, production, transportation under uncertainty of parameters, such as transportation and production and disruption of facilities, as well as minimizing expected standard deviation of network costs for whole scenarios. Since one of the most important reasons for researchers' reluctance to apply Benders-decomposition algorithm in facility-location concept is the time-consuming nature of its feasibility-cut stage, one of the most outstanding innovation in this paper is to add a strengthening redundant constraint to the proposed model in order to eliminate the mechanism related to feasibility cuts in master problem. to the best of our knowledge, it is the first time that this technique, not being involved in keeping master-problem feasibility, is used to solve a reliable capacitated facility location problem. In this approach, in terms of time-consuming the Benders algorithm is able to powerfully compete with metaheuristic algorithms, but with an exact solution. To prove advantage of this algorithm satisfying both ultimate solution optimality and appropriate running time compared to metaheuristic algorithms at the same time, one metaheuristic algorithm, namely Imperialist Competitive Algorithm (ICA), is presented. Usefulness and practicality of the proposed model and solution method demonstrated through a case example in different class with variable size. تفاصيل المقالة
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  8 - A Stochastic Model for Water Resources Management
  سلیم باوندی هادی ناصری
  Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. Over the past decades, controversial and conflictladen water-allocation issues among competing municipal, industrial and agricu أکثر

  Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. Over the past decades, controversial and conflictladen water-allocation issues among competing municipal, industrial and agricultural interests have raised increasing concerns. Particularly, growing population, varying natural conditions and shrinking water availabilities have exacerbated such competitions. Shrinking water availabilities can result in reduced water supplies, while growing population can lead to increased water demands, these two facts can further intensify the water shortage. Stochastic programming methodology is applied in this paper to a capital investment problem in water resources. A framework is offered for the evaluation of electricity generation and water supply for agricultural irrigation. This essessment is conducted through the construction of an appropriate stochastic optimization model. A recursive least squares algorithm is incorp-orated in the model which enablee more accurate estimation of model parameters. تفاصيل المقالة
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  9 - Multi-choice stochastic bi-level programming problem in cooperative nature via fuzzy programming approach
  Sumit Kumar Maiti Sankar Kumar Roy
  In this paper, a Multi-Choice Stochastic Bi-Level Programming Problem (MCSBLPP) is considered where all the parameters of constraints are followed by normal distribution. The cost coefficients of the objective functions are multi-choice types. At first, all the probabil أکثر

  In this paper, a Multi-Choice Stochastic Bi-Level Programming Problem (MCSBLPP) is considered where all the parameters of constraints are followed by normal distribution. The cost coefficients of the objective functions are multi-choice types. At first, all the probabilistic constraints are transformed into deterministic constraints using stochastic programming approach. Further, a general transformation technique with the help of binary variables is used to transform the multi-choice type cost coefficients of the objective functions of Decision Makers(DMs). Then the transformed problem is considered as a deterministic multi-choice bi-level programming problem. Finally, a numerical example is presented to illustrate the usefulness of the paper. تفاصيل المقالة
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  10 - Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach
  Mohsen Yahyaei Mahdi Bashiri
  The hub location problem arises in a variety of domains such as transportation and telecommunication systems. In many real-world situations, hub facilities are subject to disruption. This paper deals with the multiple allocation hub location problem in the presence of f أکثر

  The hub location problem arises in a variety of domains such as transportation and telecommunication systems. In many real-world situations, hub facilities are subject to disruption. This paper deals with the multiple allocation hub location problem in the presence of facilities failure. To model the problem, a two-stage stochastic formulation is developed. In the proposed model, the number of scenarios grows exponentially with the number of facilities. To alleviate this issue, two approaches are applied simultaneously. The first approach is to apply sample average approximation to approximate the two stochastic problem via sampling. Then, by applying the multiple cuts Benders decomposition approach, computational performance is enhanced. Numerical studies show the effective performance of the SAA in terms of optimality gap for small problem instances with numerous scenarios. Moreover, performance of multi-cut Benders decomposition is assessed through comparison with the classic version and the computational results reveal the superiority of the multi-cut approach regarding the computational time and number of iterations. تفاصيل المقالة
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  11 - A two-stage stochastic rule-based model to determine pre-assembly buffer content
  Elif Elcin Gunay Ufuk Kula
  This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly b أکثر

  This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model. تفاصيل المقالة
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  12 - Robust uncapacitated multiple allocation hub location problem under demand uncertainty: minimization of cost deviations
  Aleksejs Lozkins Mikhail Krasilnikov Vladimir Bure
  The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuraci أکثر

  The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of demand scenarios. The problem is formulated as a nonlinear stochastic optimization problem to minimize the hub installation costs, expected transportation costs and expected absolute deviation of transportation costs. To eliminate the nonlinearity, the equivalent linear problem is introduced. The expected absolute deviation is the robustness measure to derive the solution close to each scenario. The robust hub location is assumed to deliver the least costs difference across the scenarios. The number of scenarios increases size and complexity of the task. Therefore, the classical and improved Benders decomposition algorithms are applied to achieve the best computational performance. The numerical experiment on CAB and AP dataset presents the difference of resulting hub networks in stochastic and robust formulations. Furthermore, performance of two Benders decomposition strategies in comparison with Gurobi solver is assessed and discussed. تفاصيل المقالة
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  13 - A Novel Charging Plan for PEVs Aggregator Based on Combined Market and Network Driven Approach
  Behzad Hashemi Majid Shahabi Payam Teimourzadeh Baboli
  With the large-scale production of plug-in electric vehicles (PEVs), a new entity, the PEV fleet aggregator manages charging and discharging processes of the vehicles. The main objective of an individual aggregator in interaction with electricity markets is maximizing i أکثر

  With the large-scale production of plug-in electric vehicles (PEVs), a new entity, the PEV fleet aggregator manages charging and discharging processes of the vehicles. The main objective of an individual aggregator in interaction with electricity markets is maximizing its profit. In this paper, the performance of this aggregator in day-ahead and real-time electricity markets, considering (a) customers’ satisfaction constraints, (b) the effects of driving patterns and real-time energy market prices uncertainties and (c) resulted effects on the network operation, is studied. Then, the capability of a bilateral contract between the aggregator and distribution system operator as a regulation for satisfying the network’s limitations is investigated. The proposed model is formulated as a two-stage stochastic programming problem and implemented in GAMS software. The findings of the study reveal the effectiveness of the proposed algorithm on maximizing the aggregator’s profit-making as well as both customers’ and Distribution System Operator’s financial and technical satisfaction. تفاصيل المقالة
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  14 - Energy Scheduling in Power Market under Stochastic Dependence Structure
  Mehdi Farhadkhani
  Since the emergence of power market, the target of power generating utilities has mainly switched from cost minimization to revenue maximization. They dispatch their power energy generation units in the uncertain environment of power market. As a result, multi-stage sto أکثر

  Since the emergence of power market, the target of power generating utilities has mainly switched from cost minimization to revenue maximization. They dispatch their power energy generation units in the uncertain environment of power market. As a result, multi-stage stochastic programming has been applied widely by many power generating agents as a suitable tool for dealing with self-scheduling strategies under uncertainty. However, dependence structure between stochastic variables has been almost ignored in the literature. Copula function is a new concept in the probability and statistics field which has the capability to represent the dependence structure among stochastic variables. However, Copula function has recently taken into account in power system studies by some articles. In this article, self-scheduling strategy of a generation utility owning thermal units is investigated while the dependence structure among stochastic load and market price variables is taking into account. We assume that the generation utility is a price-taker agent in a power market, and it also has to meet the load of a specific region as a retailer. The results indicates that as the stochastic dependence structure among load and price variables is considered in modeling load and price scenarios, the output of unit commitment problem changes so that the revenue of generation utility increases. تفاصيل المقالة
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  15 - بهینه‏ سازی چندهدفه سبدسهام با استفاده از برنامه‌ریزی تصادفی چندمرحله‌ای
  حامد عسگری جواد بهنامیان
  در این تحقیق به ارائه مدلی با توجه به ماهیت داده‌های ورودی مسئله و همچنین ماهیت تصادفی رخدادهای آتی سهم‌ها پرداخته‌ شده است. به ‌منظور پویاسازی سبد سهام، مدل برنامه‌ریزی استفاده‌ شده است که در آن هر یک از زمان‌های تصمیم‌گیری به عنوان یک مرحله در مدل برنامه‌ریزی تصادفی د أکثر

  در این تحقیق به ارائه مدلی با توجه به ماهیت داده‌های ورودی مسئله و همچنین ماهیت تصادفی رخدادهای آتی سهم‌ها پرداخته‌ شده است. به ‌منظور پویاسازی سبد سهام، مدل برنامه‌ریزی استفاده‌ شده است که در آن هر یک از زمان‌های تصمیم‌گیری به عنوان یک مرحله در مدل برنامه‌ریزی تصادفی در نظر گرفته‌ شده است. به دلیل وابستگی جواب‌های حاصل از مدل برنامه‌ریزی تصادفی با بازخورد به روش تولید سناریو، به ارائه روش مناسب تولید سناریو با توجه به ماهیت ورودی داده‌های مسئله پرداخته‌ شده است. در نهایت اعتبار مدل ارائه شده پس از حل با نرم‌افزار گمز ارزیابی شده است. همان‌طور که نشان داده‌ شده است استفاده از برنامه‌ریزی تصادفی با بازخورد و ترکیب آن با روش تولید سناریوی معرفی شده، این امکان را به سرمایه‌گذاران می‌دهد که بتوانند برنامه‌ریزی‌های کوتاه‌مدت و بلندمدت برای خریدها و فروش‌های خود در بازارهای مالی را داشته و نتایج مدل تا حد خوبی نشان‌دهنده کارایی مدل حاضر در بازارهای مالی است. تفاصيل المقالة