با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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تجزیه و تحلیل پوششی داده مبتنی بر نسبت: یک رویکرد تعاملی برای شناسایی معیار
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 40 در دنیای واقعی ما با موارد زیادی مواجه هستیم که در آن نسبت داده های ورودی/خروجی برای مدیران بسیار مهم است، بنابراین در این رابطه نمی توان از مدل های سنتی تحلیل پوششی داده (DEA) برای ارزیابی کارایی واحدهای تصمیم گیری (DMU) استفاده کرد، و باید از مدل های DEA بر اساس داده های نسبت بهره برد. برای بدست آوردن معیار مربوطه برای هر واحد تصمیمگیری ناکارآمد، باید ورودیها و خروجیها را به ترتیب کاهش و افزایش دهیم و به یک پیشبینی واحد و منسجم تصمیمگیرنده در مرز کارایی برسیم. در این مقاله ما یک مدل برنامهریزی خطی چندهدفه (MOLP) (multi-objective linear programming) را برای ارزیابی کارایی بر اساس تعریف مجموعه امکان تولید در حضور دادههای نسبت و به دست آوردن معیار مربوطه برای هر واحد تصمیمگیری DMU ارائه میکنیم. ما از روش تعاملی زایونتس و والنیوس (Z-W) برای حل مدل MOLP ارائه شده استفاده میکنیم. با استفاده از تنظیم هدف توسط مدیر از بین راه حل های حاصل از مسئله MOLP، بهترین راه حل را با توجه به ترجیحات مدیران به عنوان معیار انتخاب می کنیم و در پایان نتایج تحقیق را ارائه می کنیم.
واژگان کلیدی: کارایی | DEA-R | معیار | برنامه ریزی چند هدفه | روش تعاملی |
مقاله ترجمه شده |
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Building organizational resilience in the face of multiple disruptions
ایجاد برجهندگی سازمانی در مواجهه با انقطاع های مختلف-2018 The increasing number of natural and man-made hazards is forcing organizations to build resilience against numerous types of disruptions that threaten continuity of their business processes. This paper presents an integrated business continuity and disaster recovery planning (IBCDRP) model to build organizational resilience that can respond to multiple disruptive incidents, which may occur simultaneously or sequentially. This problem involves multiple objectives and accounts for inherent epistemic uncertainty in input data. A multi-objective mixed-integer robust possibilistic programming model is formulated, which accounts for sensitivity and feasibility robustness. The model aims to plan both internal and external resources with minimal resumption time, restoration time, and loss in the operating level of critical functions by making tradeoffs between required resources for continuity plans, recovery time, and the recovery point. A real case study in a furniture manufacturing company is conducted to demonstrate the performance and applicability of the proposed IBCDRP model. The results confirm the capability of the proposed model to improve organizational resilience. In addition, the proposed model demonstrates the interaction between the organizational resilience and required resources, particularly in respect to the total budget and external resources, which is necessary for developing continuity and recovery strategies.
keywords: Organizational resilience |Business continuity management |Disaster operations management |Multi-objective programming |Robust possibilistic programming |
مقاله انگلیسی |
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The fuzzy multi-objective distribution planner for a green meat supply chain
برنامه ریزی توزیع چند منظوره فازی برای یک زنجیره تامین گوشت سبز-2017 It is often a complex task for developing a product distribution plan of a supply chain (SC) network and a
supportive decision tool can be useful for easing the role of decision-making. On the other hand, it has been
increasingly becoming a demand to design a supply chain network considering the environmental impact as a
new dimension as required by authorities in many countries. This paper describes a development of a product
distribution planner for a three-echelon green meat supply chain (MSC) design in terms of issues including
numbers and locations of facilities that should be opened in association with the product quantity flows. These
issues were formulated into a fuzzy multi-objective programming model (FMOPM) with an aim to minimize the
total cost of transportation and implementation, the amount of CO2 emissions in transportation and the
distribution time of products from farms to abattoirs and from abattoirs to retailers, and maximize the average
delivery rate in satisfying product quantity as requested by abattoirs and retailers. To optimize the four
objectives simultaneously, three solution methods were investigated and used; which are the LP-metrics
method, the ε-constraint method and the goal programming method. The best solution was determined using
the Max-Min method by comparing the obtained Pareto solutions. A case study was examined based on the
developed model that demonstrates its applicability in making an optimal product distribution plan in trade-offs
among the four objectives.
Keywords: Distribution plan | Food supply chain design | Fuzzy multi-objective programming |Green supply chain |RFID |
مقاله انگلیسی |
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Liquefied natural gas importing security strategy considering multi-factor: A multi-objective programming approach
استراتژی امنیتی وارداتی گاز مایع با توجه به چند عامل: یک رویکرد برنامه ریزی چند هدفه-2017 Article history:Received 28 March 2016Revised 28 May 2017Accepted 29 May 2017Available online 7 June 2017Keywords:Liquefied natural gas (LNG ) Multi-objective Programming Extreme eventsImproved Simulated Annealing Algorithm Software implementationLNG importing strategies, in the literature, are primarily studied under a common single-factor frame- work. However, LNG importing strategies are affected by a variety of factors. To address this existing gap, this paper proposes a Multi-Objective Programming model, which takes into account the cost, the coun- try risk, the shipping risk, and the impact of extreme events. A pure structural change model is used to determine the risk impact coefficient for extreme events. An enhanced Simulated Annealing Algorithm is then used to solve the LNG-importing optimization problem. An experimental study is further con- ducted to verify the practicability of the proposed approach in the case of China’s LNG-importing data. The software implementation of the proposed model is developed in Python. The proposed model pro- vides a decision support tool for LNG importing companies to find an efficient portfolio strategy for LNG importing. The optimization model can be used for analyzing similar scenarios involving such dimensions as economy, energy security, and especially energy diversification.© 2017 Elsevier Ltd. All rights reserved. Keywords: Liquefied natural gas (LNG ) | Multi-objective Programming | Extreme events | Improved Simulated Annealing Algorithm | Software implementation |
مقاله انگلیسی |
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Evaluation of multi-objective optimization approaches for solving green supply chain design problems
ارزیابی روشهای بهینه سازی چند منظوره برای حل مشکلات طراحی زنجیره تامین سبز-2017 This paper evaluates the applicability of different multi-objective optimization methods for envir
onmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO2 and
fine dust (also known as PM – Particulate Matters) emissions. We approximate the Pareto front using the
weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected
parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies,
and their interactive counterparts that incorporate Decision Makers (DMs) indirect preferences into the
search process. Within this case study, the CO2 emissions could be lowered significantly by accepting a
marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of
the Pareto front, but produced significantly worse solutions than the exact optimization methods. The
interactive methods outperformed their a posteriori counterparts, and could discover solutions corre
sponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and
starting generation of the elicitation, the number of pairwise comparisons needed by the interactive
evolutionary methods to construct a satisfactory solution could be decreased.
Keywords: Supply chain management | Green logistics | Multi-objective programming | Indirect preference information| Evolutionary algorithms |Interactive evolutionary multi-objective | optimization |
مقاله انگلیسی |