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stochastic programming

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تکرار جستجوی کلیدواژه stochastic programming در نشریات گروه فنی و مهندسی
  • Roghaye Zarezade, Rouzbeh Ghousi *, Emran Mohammadi, Hossein Ghanbari
    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
  • شادی صدری، سید محمدتقی فاطمی قمی*

    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 کارلو
    Shadi Sadri, Seyyed Mohammadtaghi Fatemi Ghomi *

    In 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
  • Taha Hejazi *, Mirmehdi Seyyed-Esfahani, Hurieh Dezhahang, Donya Ramezani
    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
  • Shahla Zandi*, Reza Samizadeh, Maryam Esmaeili

    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
  • Hajar Shirneshan, Ahmad Sadegheih *, Hasan Hosseini-Nasab, MohammdMehdi Lotfi

    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
  • Shaghayegh Heyhat, Donya Rahmani *
    During 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
  • S. Farid Mousavi, Zahra Mahdavi, Kaveh Khalili-Damghani*, Arezoo Gazori-Nishabori

    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
  • Alireza Hamidieh *, Ali Johari
    The 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
  • MohamadEbrahim Tayebi Araghi, Fariborz Jolai *, Reza Tavakkoli Moghaddam, Mohammad Molana

    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
  • Faraz Salehi *, Yasin Allahyari Emamzadeh, Seyyed MohammadJavad Mirzapour Al E Hashem, Reyhaneh Shafiei Aghdam

    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
  • Fatemeh Zafari, Davood Shishebori *

    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
  • Amin Reza Kalantari Khalil Abad, Seyed HamidReza Pasandideh *

    The process of designing and redesigning supply chain networks is subject to multiple uncertainties. Given the growing environmental pollution and global warming caused by societies' industrialization, this process can be completed when environmental considerations are also taken into account in the decisions. In this study, an integrated four-level closed-loop supply chain network, including factories, warehouses, customers, and disassembly centers (DCs) is designed to fulfill environmental objectives in addition to economic ones. The reverse flow, including recycling and reprocessing the waste products, is considered to increase production efficiency. Also, the different transportation modes between facilities, proportional to their cost and greenhouse gas emissions, are taken into account in the decisions. A random cost function and chance constraints are presented firstly to handle the uncertainties in different parameters. After defining the random constraints using the chance-constraint programming approach, a deterministic three-objective model is presented. The developed model is solved using the GAMS software and the goal attainment (GA) method. Also, the effect of the priority of the goal, uncertain parameters, and confidence level of chance constraints on objective function values has been carefully evaluated using different numerical examples.

    Keywords: Green Closed-loop supply chain network design, Stochastic programming, Chance-constrained programming, Goal-attainment
  • محمد مهدی والی سیر، عماد روغنیان*

    امروزه زنجیره های تامین در معرض ریسک های مختلفی قرار دارند. بی توجهی به این ریسک ها می تواند خسارات جبران ناپذیری را برای آن ها به وجود آورد. از سوی دیگر، موضوع بازطراحی در مواقعی که زنجیره تامین با توجه به تغییر شرایط بهینگی خود را از دست می دهد یا نیاز به تغییر دارد دارای اهمیت است. در این مقاله بر خلاف غالب پژوهش های ادبیات موضوع، مساله بازطراحی شبکه زنجیره تامین تاب آور تحت ریسک های عملیاتی و اختلال مورد بررسی قرار می گیرد. ساختار شبکه مورد بررسی در این پژوهش، ترکیبی حلقه باز و بسته می باشد که در ادبیات موضوع به ندرت مورد توجه قرار گرفته است. برای مدیریت عدم قطعیت مساله یک مدل جدید بهینه سازی استوار تصادفی ارایه شده است. مساله به صورت یک مدل برنامه ریزی عدد صحیح مختلط خطی فرمول بندی شده که تابع هدف آن بیشینه سازی سود می باشد. به علت پیچیدگی بالای مدل و وجود چالش برای حل آن در ابعاد بزرگ، یک الگوریتم آزادسازی لاگرانژ ارایه شده و عملکرد خوب آن توسط محاسبات مربوطه نشان داده شده است. به منظور سنجش کارایی و اعتبار مدل یک مطالعه موردی در صنعت تایر خودرو ارایه شده است. نتایج نشان می دهد که استفاده از استراتژی های تاب آوری برای ارتقای سودآوری زنجیره تامین و جلوگیری از ضرر اختلالات بسیار موثر می باشند. همچنین استفاده از شبکه زنجیره تامین ترکیبی نسبت به شبکه زنجیره تامین رو به جلو باعث افزایش سود کل زنجیره تامین می شود.

    کلید واژگان: طراحی شبکه زنجیره تامین، تاب آوری، ریسک های عملیاتی و اختلال، برنامه ریزی تصادفی، شبکه ترکیبی حلقه باز و بسته
    MohammadMahdi Vali Siar, Emad Roghanian *

    Today, supply chains are exposed to a variety of risks. Ignoring these risks can cause irreparable damage to them. On the other hand, the subject of redesigning is essential when the supply chain loses its optimality or needs to be altered due to changing conditions. In this paper, in contrast to most researches done in the literature, the problem of resilient supply chain network redesign is investigated under operational and disruption risks. The network structure addressed in this paper is a mixture of open and closed loop schemes, which has been rarely considered in the literature. A novel stochastic robust optimization model is developed to manage the uncertainty of the problem. The problem is formulated as a linear mixed-integer programming model with the objective function of profit maximization. Due to the high complexity of the model and the challenge to solve it in large-scale dimensions, a Lagrangian relaxation algorithm is developed, and its excellent performance is shown by the relevant calculations. In order to measure the efficiency and validity of the model, a case study has been presented in the automotive tire industry. The results show that using resilience strategies is very effective in improving the profitability of the supply chain and preventing losses. In addition, the use of a mixed supply chain network increases the overall profitability of the supply chain in comparison to a forward supply chain network.

    Keywords: Supply chain network design, Resilience, Operational, disruption risks, Stochastic programming, Mixed open, closed loop supply chain
  • 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 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.

    Keywords: Hub location problem, Stochastic programming, Absolute deviation · Robust solution, Benders decomposition, Pareto, optimal cuts
  • Mojtaba Salehi *, Hamid Tikani
    This paper introduces a two stage stochastic programming to address strategic hub location decisions and tactical flight routes decisions for various customer classes considering uncertainty in demands. We considered the airline network with the arc capacitated single hub location problem based on complete–star p-hub network. In fact, the flight routes are allowed to stop at most two different hubs. The first stage of the model (strategic level) determines the network configuration, which does not change in a short space of time. The second stage is dedicated to specify a service network consists of determining the flight routes and providing booking limits for all itineraries and fare classes after realization of uncertain scenarios. To deal with the demands uncertainty, a stochastic variations caused by seasonally passengers’ demands through a number of scenarios is considered. Since airline transportation networks may face different disruptions in both airport hubs and communication links (for example due to the severe weather), proposed model controls the minimum reliability for the network structure. Due to the computational complexity of the resulted model, a hybrid algorithm improved by a caching technique based on genetic operators is provided to find a near optimal solution for the problem. Numerical experiments are carried out on the Turkish network data set. The performance of the solutions obtained by the proposed algorithm is compared with the pure GA and Particle Swarm Optimization (PSO) in terms of the computational time requirements and solution quality.
    Keywords: Customer segmentation, scenario generation method, Network Reliability, Stochastic programming, meta-heuristic algorithms
  • محمدباقر فخرزاد *، الهه قاسمی قاسمی

    در سال های اخیر مکان یابی تسهیلات موقت و سیار، در سیستم های سلامت بسیار مورد توجه قرار گرفته است. یکی از حوزه های مهم در این زمینه، طراحی سیستم های جمع آوری و توزیع خون است. در این مقاله یک مدل تصادفی چهار سطحی دو مرحله یی تصادفی برای تامین محصولات خونی در شرایط بحران ارائه می شود. هدف از این مطالعه، کمینه سازی هزینه ها با توجه به تامین محصولات خونی مورد نیاز در زمان استاندارد است. در مدل سازی ارائه شده همچنین به موضوع فساد محصولات خونی در چرخه ی تامین توجه می شود. در این مقاله سعی شده است که با ارائه ی یک رویکرد ترکیبی از یک الگوریتم فراابتکاری و روش ابتکاری، روش حل ارائه شده بهبود داده شود. به منظور اعتبارسنجی مدل و رویکرد پیشنهادی برای حل مسئله، نتایج محاسبات حاصل از تعدادی مثال عددی تولید شده نشان داده می شود.

    کلید واژگان: زنجیره ی تامین خون، مدیریت بحران، برنامه ریزی تصادفی، قیود احتمالی، مکان یابی تخصیص، الگوریتم ژنتیک
    M.B. Fakhrzad*, E. Ghasemi

    In recent years, the location of mobile facilities has been highly regarded in design of health system. One of the important areas in this field is design of blood collection and distribution systems. Since blood is as perishable and vital goods and donation of blood is a voluntary work, supply blood and blood products is one of the most challenging issues in the supply chain in emergency and non-emergency situation. In this paper, we propose a four-echelon two-stage stochastic model to supply whole blood and its products in disaster. The hospitals, regional blood centers, local blood centers and bloodmobile facilities are considered as main elements of blood supply chain. It is assumed that the blood collection from the donors just done in bloodmobile facilities and local blood centers. Also, processing operation of blood products from collected whole blood are done only in the regional blood centers. In the designed supply network in this paper, every hospital could provide needed blood products from other near hospitals in the emergency situation as well as other fixed and mobile blood centers. One of the most important issues in the blood supply in disasters is delivery time of blood, so here; we consider an upper bound to blood delivery time to the affected areas and the objective of our presented model is blood supply in the standard time so that the total supply cost be minimized. In the other hand, the time solution is very important to obtain an acceptable solution in a reasonable time. We present a heuristic approach based on genetic algorithm and exact method to solve the proposed MIP model. The locations of fixed and mobile facility are computed through genetic algorithm then other variables are calculated by solve model with CPLEX. In order to validate the proposed approach, we generate 8 examples with different sizes and numerical results are presented. Also the results of comparison our approach with exact and metaheurisitc method are presente.

    Keywords: Blood supply chain, crisis management, stochastic programming, chance constraint, location-allocation, genetic algorithm
  • Zahra Sadat Hosseini, Mohammad Saber Fallah Nezhad *

    Supplier selection is one of the critical issues in the supply chain. Green supplier selection is performed based on the assessment of quantitative and qualitative criteria in two fields, including economic, environmental attributes. In this study, a two-level supply chain model has been proposed for green supplier selection and order allocation in a multi-period and single-product environment. In the first phase, the analytic hierarchical process (AHP) method is used to rank the suppliers and in the second phase, a model is designed based on constraints such as demand, capacity, and allowed level of inventory and shortage to maximize the total value of purchase (TVP) and total profit of purchase (TPP). Demand is assumed to be stochastic in different periods. Thus random demand leads to create the various scenarios in the planning horizon. A new integrated approach is presented based on stochastic programming and dynamic programming to solve the problem. The incorporation of stochastic demand condition and application of dynamic programming is a novel idea. Finally, a Numerical example is provided to investigate the procedure in details.

    Keywords: Green Supplier Selection, Order Allocation, Dynamic Programming, Stochastic Programming
  • Hadis Derikvand, Seyed Mohammad Hajimolana *, Armin Jabarzadeh, Esmaeil Najafi
    Emergency blood distribution seeks to employ different means in order to optimize the amount of blood transported while timely provision. This paper addresses the concept of blood distribution management in disastrous conditions and develops a fuzzy scenario-based bi-objective model whereas blood compatibility concept is incorporated in the model, and the aim is to minimize the level of unsatisfied demand of affected areas (AAs) while minimizing the cost of the supply chain. The blood supply chain network under investigation consists of blood suppliers (hospitals or blood centers), blood distribution centers (BDCs), and AAs. Demand and capacity, as well as cost, are the sources of uncertainty and in accordance with the nature of the problem, the fuzzy-stochastic programming method is applied to deal with these uncertainties. After removing nonlinear terms, Ɛ-constraint solves the bi-objective model as a single objective one. Finally, we apply a case from Iran to show the applicability of the model, results prove the role of blood distribution management in decreasing the unsatisfied demand about 38%.
    Keywords: blood supply chain, disaster, fuzzy programming, Stochastic programming, Ɛ-constraint
  • Mir Saman Pishvaee *, Atiye Yousefi
    The impact of financial challenges on the profit of a supply chain, have caused the researcher to model the supply chain network by considering the operational and financial dimensions. Also, the establishment of a closed loop supply chain (CLSC) network has a high effect on economic profit. So, the purpose of this study is to design a stochastic closed loop supply chain network by considering the operational and financial dimensions and tactical decision-making level. First, a deterministic mixed-integer linear programming model is developed. Then, the scenario-based of the proposed mixed integer linear programming model is presented. The main innovation of this research is to develop a mathematical model that simultaneously focuses on optimizing the financial and physical flows in an integrated manner and uses the financial ratios in the form of a closed loop supply chain. In order to illustrate the applicability of the proposed model, a test problem from the recent literature is used. The analysis of the results obtained from the developed stochastic mathematical model shows an averagely 4% increase in profit and a 27% reduction in semi-variance compared to deterministic developed models.
    Keywords: Financial flow, Closed-loop supply chain, Supply chain management, Stochastic programming, Scenario-based approach
  • 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 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.

    Keywords: Mixed - model assembly lines, Car resequencing, Heuristics, Stochastic programming
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