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Data, data flows, and model specifications for linking multi-level contribution margin accounting with multi-level fixed-charge problems
دادهها، جریانهای داده، و مشخصات مدل برای پیوند حسابداری حاشیه سهم چندسطحی با مشکلات شارژ ثابت چندسطحی-2021 This article describes the data, data flows, and spreadsheet
implementations for linking multi-level contribution margin
accounting as a subsystem in cost accounting with several
versions of a multi-level fixed-charge problem (MLFCP), the
latter based on the optimization approach in operations research. This linkage can reveal previously hidden optimization potentials within the framework of multi-level contribution margin accounting, thus providing better information for decision making in companies and other organizations. For the data, plausible fictitious values have been assumed taking into consideration the calculation principles
in cost accounting where applicable. They include resourcerelated data, market-related data, and data from cost accounting needed to analyze the profitability of a companys´
products and organizational entities in the presence of hierarchically structured fixed costs. The data are processed and
analyzed by means of mathematical optimization techniques
and sensitivity analysis. The linkage between multi-level contribution margin accounting and MLFCP is implemented in
three spreadsheet files, including versions for deterministic
optimization, stochastic optimization, and robust optimization. This paper provides specifications for compatible solver
add-ins and for executing sensitivity analysis. The data and spreadsheet implementations described in this article were
used in a research article entitled “Making better decisions
by applying mathematical optimization to cost accounting:
An advanced approach to multi-level contribution margin accounting” [1]. The data sets and the spreadsheet implementations may be reused a) by researchers in management and
cost accounting as well as in operations research and quantitative methods for verification and for further development
of the linkage concept and of the underlying optimization
models; b) by practitioners for gaining insight into the data
requirements, methods, and benefits of the proposed linkage,
thus supporting continuing education; and c) by instructors
in academia who may find the data and spreadsheets valuable for classroom use in advanced courses. The complete
spreadsheet implementations in the form of three ready-touse Excel files (deterministic, stochastic, and robust version)
are available for download at Mendeley Data. They may serve
as customizable templates for various use cases in research,
practice, and education.
keywords: حسابداری هزینه | تحقیق در عملیات | مشکل ثابت شارژ | بهینه سازی | برنامه نویسی صحیح | تجزیه و تحلیل میزان حساسیت | بهینه سازی تصادفی | صفحه گسترده | Cost accounting | Operations research | Fixed-charge problem | Optimization | Integer programming | Sensitivity analysis | Stochastic optimization | Spreadsheet |
مقاله انگلیسی |
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Making better decisions by applying mathematical optimization to cost accounting: An advanced approach to multi-level contribution margin accounting
تصمیم گیری های بهتر را با استفاده از بهینه سازی ریاضی به هزینه حسابداری: یک رویکرد پیشرفته به حسابداری حاشیه کمک چند سطح-2021 The purpose of multi-level contribution margin accounting in cost accounting is to analyze the profitability of
products and organizational entities with appropriate allocation of fixed costs and to provide relevant information
for short-term, medium- and longer-term decisions. However, the conventional framework of multi-level
contribution margin accounting does not usually incorporate a mathematical optimization method that simultaneously integrates variable and fixed costs to determine the best possible product mix within hierarchically
structured organizations. This may be surprising in that operations research provides an optimization model in the
form of the fixed-charge problem (FCP) that takes into account not only variable costs but also fixed costs of the
activities to be planned. This paper links the two approaches by expanding the FCP to a multi-level fixed-charge
problem (MLFCP), which maps the hierarchical decomposition of fixed costs in accordance with multi-level
contribution margin accounting. In this way, previously hidden optimization potentials can be made visible
within the framework of multi-level contribution margin accounting. Applying the linkage to a case study illustrates that the original assessment of profitability gained on the sole basis of a multi-level contribution margin
calculation might turn out to be inappropriate or even inverted as soon as mathematical optimization is utilized:
products, divisions, and other reference objects for fixed cost allocation, which at first glance seem to be profitable
(or unprofitable) might be revealed as actually unprofitable (or profitable), when the multi-level contribution
margin calculation is linked to the MLFCP. Furthermore, the proposed concept facilitates assessment of the costs
of an increasing variant diversity, which also demonstrates that common rules on how to interpret a multi-level
contribution margin calculation may have to be revised in some cases from the viewpoint of optimization. Finally,
the impact of changes in the fixed cost structure and other parameters is tested via sensitivity analyses and
stochastic optimization.
keywords: حسابداری هزینه | حد مشارکت، محدوده مشارکت | هزینه های ثابت | نرم افزار | مخلوط محصول | تصمیم گیری | تحقیق در عملیات | مشکل ثابت شارژ | مشکل چند سطح قابل شارژ | بهینه سازی | برنامه نویسی صحیح | تجزیه و تحلیل میزان حساسیت | بهینه سازی تصادفی | صفحه گسترده | مطالعه موردی | Cost accounting | Contribution margin | Fixed costs | Profitability | Product mix | Decision making | Operations research | Fixed-charge problem | Multi-level fixed-charge problem | Optimization | Integer programming | Sensitivity analysis | Stochastic optimization | Spreadsheet | Case study |
مقاله انگلیسی |
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Integrated computer vision algorithms and drone scheduling
الگوریتم های یکپارچه بینایی ماشین و برنامه ریزی هواپیماهای بدون سرنشین-2021 Computer vision algorithms have attained significant accuracy in the
past decade, among which arguably the most important one is deep
neural networks. Unmanned aerial vehicles, commonly called drones,
equipped with cameras, offer a convenient, efficient, and cost-effective
way of collecting a large set of images. Combining drones and computer vision algorithms can automate the monitoring and surveying of
infrastructure systems, for example, car detection (Maria et al., 2016),
pedestrian and bicycle volume data collection (Kim, 2020), and road
degradation survey (Leonardi et al., 2018). However, the existing
research has been largely driven by two independent streams of expertise: computer vision and drone scheduling. Computer scientists strive to
design more accurate computer vision algorithms without much
consideration of how the images are collected, whereas operations researchers endeavor to design drone routing algorithms to collect a given
set of images in the most efficient manner. We suggest that the planning
of images to collect (number and locations of images, amongst others)
and the design of—more often than not, the choice of—computer vision
algorithms should be determined holistically instead of independently.
Section 2 presents an example to show the number of images to collect
depends on the accuracy of the computer vision algorithms. Section 3
lays out the roadmap for future research direction.
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مقاله انگلیسی |
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Big data analytics in health sector: Theoretical framework, techniques and prospects
تجزیه و تحلیل داده های بزرگ در بخش بهداشت و درمان: چارچوب نظری ، تکنیک ها و چشم انداز-2020 Clinicians, healthcare providers-suppliers, policy makers and patients are experiencing exciting opportunities in
light of new information deriving from the analysis of big data sets, a capability that has emerged in the last
decades. Due to the rapid increase of publications in the healthcare industry, we have conducted a structured
review regarding healthcare big data analytics. With reference to the resource-based view theory we focus on
how big data resources are utilised to create organization values/capabilities, and through content analysis of
the selected publications we discuss: the classification of big data types related to healthcare, the associate
analysis techniques, the created value for stakeholders, the platforms and tools for handling big health data and
future aspects in the field. We present a number of pragmatic examples to show how the advances in healthcare
were made possible. We believe that the findings of this review are stimulating and provide valuable information
to practitioners, policy makers and researchers while presenting them with certain paths for future research. Keywords: Big data analytics | Health-Medicine | Decision-making | Machine learning | Operations research (OR) techniques |
مقاله انگلیسی |
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A survey of hybrid metaheuristics for the resource-constrained project scheduling problem
بررسی استعاره ترکیبی برای مشکل برنامه ریزی پروژه با محدودیت منابع-2020 The Resource-Constrained Project Scheduling Problem (RCPSP) is a general problem in scheduling that has a wide variety of applications in manufacturing, production planning, project management, and var- ious other areas. The RCPSP has been studied since the 1960s and is an NP-hard problem. As being an NP-hard problem, solution methods are primarily heuristics. Over the last two decades, the increasing interest in operations research for metaheuristics has resulted in a general tendency of moving from pure metaheuristic methods for solving the RCPSP to hybrid methods that rely on different metaheuristic strategies. The purpose of this paper is to survey these hybrid approaches. For the primary hybrid meta- heuristics that have been proposed to solve the RCPSP over the last two decades, a description of the basic principles of the hybrid metaheuristics is given, followed by a comparison of the results of the dif- ferent hybrids on the well-known PSPLIB data instances. The distinguishing features of the best hybrids are also discussed. Keywords: Project scheduling| Resource constraints | RCPSP | Metaheuristics | Hybrids |
مقاله انگلیسی |
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یادگیری عمیق در تجزیه و تحلیل کسبوکار و تحقیقات عملیاتی: مدلها، کاربردها و مفاهیم مدیریتی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 55 تجزیه و تحلیل کسب و کار به روش ها و شیوه هایی اشاره دارد که از طریق داده ها برای افراد، شرکت ها و سازمان ها ارزش ایجاد می کند. این زمینه در حال حاضر به دلیل ظهور یادگیری عمیق، یک تغییر اساسی را تجربه میکند: شبکههای عصبی عمیق در مقایسه با مدلهای یادگیری ماشین سنتی، نوید بهبود عملکرد پیشبینی را میدهند. با این حال، تحقیقات ما در بدنه ادبیات موجود، کمیابی آثار تحقیقاتی با استفاده از یادگیری عمیق در رشته ما را نشان می دهد. بر این اساس، اهداف این مقاله مروری به شرح زیر است: (1) ما تحقیقات در مورد یادگیری عمیق برای تجزیه و تحلیل کسب و کار را از نقطه نظر عملیاتی مرور می کنیم. (2) ما انگیزه می دهیم که چرا محققان و متخصصان تجزیه و تحلیل کسب و کار باید از شبکه های عصبی عمیق استفاده کنند و موارد استفاده بالقوه، الزامات ضروری و مزایا را بررسی کنند. (3) ما ارزش افزوده تحقیقات عملیات را در مطالعات موردی مختلف با دادههای واقعی از شرکتهای کارآفرینی بررسی میکنیم. همه چنین مواردی بهبودهایی را در عملکرد عملیاتی نسبت به یادگیری ماشینهای سنتی نشان میدهند و در نتیجه سود مستقیم ارزش را نشان میدهند. (4) ما رهنمودها و مفاهیمی را برای محققان، مدیران و دست اندرکاران در تحقیقات عملیاتی ارائه می دهیم که می خواهند قابلیت های خود را برای تجزیه و تحلیل تجاری با توجه به یادگیری عمیق ارتقا دهند. (5) آزمایشهای محاسباتی ما نشان میدهد که معماریهای پیشفرض و خارج از چارچوب اغلب کمتر از حد مطلوب هستند و بنابراین ارزش معماریهای را با پیشنهاد یک شبکه عمیق تعبیهشده جدید برجسته میکنند.
کلید واژه ها: تجزیه و تحلیل | یادگیری عمیق | شبکه های عصبی عمیق | مفاهیم مدیریتی | دستور کار تحقیق |
مقاله ترجمه شده |
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Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017
مرور وضعیت TOPSIS فازی بین سالهای 2007 و 2017-2019 A crucial topic in expert system and operations research is fuzzy multi-criteria decision making (FM- CDM), which is used in different fields. Existing options and gaps in this topic must be understood to prepare valuable knowledge on FMCDM environments and assist scholars. This study maps the research landscape to provide a clear taxonomy. The authors focus on searching for articles related to (i) technique for order of preference by similarity to ideal solution (TOPSIS); (ii) development; and (iii) fuzzy sets in four primary databases, namely, IEEE Xplore, Web of Science, Elsevier ScienceDirect and Springer. These databases include literature that focuses on FMCDM. The resulting final set after the filtering process in- cludes 170 articles, which are classified into four categories. The first, second, third and fourth categories include articles that used a type-1 fuzzy set with the TOPSIS method, a type-2 fuzzy set with the TOPSIS method, two fuzzy membership functions and a survey paper, respectively. The basic attributes of this topic include motivations for utilising FMCDM, open challenges and limitations that obstruct utilisation and recommendations to researchers for increasing the approval and application of FMCDM. Keywords: Multi-criteria decision making | Fuzzy set | FMCDM | Fuzzy-TOPSIS |
مقاله انگلیسی |
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Deep learning in business analytics and operations research: Models, applications and managerial implications
یادگیری عمیق در تجزیه و تحلیل کسب و کار و تحقیقات عملیات: مدل ها ، برنامه ها و پیامدهای مدیریتی-2019 Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning. However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline. Accordingly, the objectives of this overview article are as follows: (1) we review research on deep learning for business analytics from an operational point of view. (2) We motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits. (3) We inves- tigate the added value to operations research in different case studies with real data from entrepreneurial undertakings. All such cases demonstrate improvements in operational performance over traditional ma- chine learning and thus direct value gains. (4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business an- alytics with regard to deep learning. (5) Our computational experiments find that default, out-of-the-box architectures are often suboptimal and thus highlight the value of customized architectures by proposing a novel deep-embedded network. Keywords: Analytics | Deep learning | Deep neural networks | Managerial implications | Research agenda |
مقاله انگلیسی |
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Marketing analytics: Methods, practice, implementation, and links to other fields
تجزیه و تحلیل بازاریابی: روش ها ، تمرین ، اجرا و پیوند به زمینه های دیگر-2019 Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, expert systems, statistics, and operations research. This paper provides an integrative review at the boundary of these areas. The aim is to give researchers in the in- telligent and expert systems community the opportunity to gain a broad view of the marketing analytics area and provide a starting point for future interdisciplinary collaboration. The topics of visualization, segmentation, and class prediction are featured. Links between the disciplines are emphasized. For each of these topics, a historical overview is given, starting with initial work in the 1960s and carrying through to the present day. Recent innovations for modern, large, and complex “big data”sets are described. Prac- tical implementation advice is given, along with a directory of open source R routines for implementing marketing analytics techniques. Keywords: Analytics | Prediction | Marketing | Visualization | Segmentation | Data mining |
مقاله انگلیسی |
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A linearized model for academic staff assignment in a Brazilian university focusing on performance gain in quality indicators
یک مدل خطی سازی شده برای تخصیص ستاد علمی در یک دانشگاه برزیلی تمرکز کننده روی بهره عملکردی در شاخص های کیفیت-2018 Private Higher Education Institutions (HEI) often have shares in stock markets to attract investment. A key element for a good appreciation on the market is a good evaluation in performance indicators of academic quality. In Brazil a main component of such academic quality indicators is directly computed after the assignment of faculty members to courses. We develop mathematical models to support decision making in the assignment of faculty members to courses in a private HEI in Brazil. It turns out that the original problem is a nonlinear integer programming problem, and to deal with large instances found in practice we propose to use a linearized model instead. We conduct computational experiments with two main purposes: to evaluate the quality of the solutions obtained with the linear integer model when compared to the ones obtained with the original nonlinear integer model, and to evaluate the potential of gains with the linear integer model when compared to actual assignments. In the latter case numerical results on real instances from the HEI under study show the proposed approach effective to improve the indicators of the HEI due to a better assignment of faculty members to courses than observed in practice.
keywords: Academic staff assignment problem |Operations research in education |Mathematical modeling |
مقاله انگلیسی |