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Introduction to Mathematical Modeling for Business Analytics
Published in William P. Fox, Mathematical Modeling for Business Analytics, 2017
Let us describe a mathematical model as a mathematical description of a system by using the language of mathematics. Why mathematical modeling? Mathematical modeling, business analytics, and operations research are all similar descriptions that represent the use of quantitative analysis to solve real problems. This process of developing such a mathematical model is termed as mathematical modeling. Mathematical models are used in the natural sciences (such as physics, biology, earth science, and meteorology), engineering disciplines (e.g., computer science, systems engineering, operations research, and industrial engineering), and in the social sciences (such as business, economics, psychology, sociology, political science, and social networks). The professionals in these areas use mathematical models all the time. A mathematical model may be used to help explain a system, to study the effects of different components, and to make predictions about behavior (Giordano et al., 2014, pp. 58–60). So let us make a more formal definition of a mathematical model: a mathematical model is the application of mathematics to a real-world problem.
Overview of Operations Research
Published in Howard Eisner, Operations Research and Systems Engineering, 2023
Finally, as a third area of expertise, these authors recommend specialized training in some other field, for example:MathematicsStatisticsIndustrial engineeringBusinessEconomics
The Concepts of Optimization and Efficiency in Digitalized Economy
Published in Walter Amedzro St-Hilaire, Value-Based Management in an Open Economy, 2023
If admits, on the one hand that optimization is a procedure established to produce coherent results, a procedure in which it is a question of breaking down a process into successive stages, and on the other hand that optimization is associated with a rational analysis, it is clear that for some models, are part of a rather conservative analysis of strategy optimization, whereas others models stand out more as an environmental progressive. Also, since efficiency is at the heart of the theory of business economics and strategic management, the evolution of this concept has supported the emergence of new currents of thought, endeavoring to integrate into the reflections an increasingly realistic representation of economic actors when confronted with contextual issues. From the pure and simple search for profit to the consideration of various realities, the questions raised by these emerging currents bear witness to the complexity, variety, and inconsistencies of companies and institutions. They show that an issue requiring corporate attention involves a power struggle over the allocation of resources, and that the issue goes far beyond simple profit maximization. In order to cope, institutions are forced to deal with the constraints by using multi-faceted efficiency. Indeed, to be realistic, we need to consider the stakeholders of the firm in the totality of their strategies, some of which do not easily respond to the maximization of a stable objective function. The industrial strategy of the actors can be explained less by exclusive reference to a maximizing calculation than by the good reasons given by the actors. Agents’ choices can be based just as much on calculations as on values, habits, or rules of strategy considered legitimate, and often despite their cost. Several models have been proposed to try to account for the way in which companies and institutions deal with their context.
A state-of-art review and a simple meta-analysis on deterministic scheduling of diffusion furnaces in semiconductor manufacturing
Published in International Journal of Production Research, 2023
M. Vimala Rani, Muthu Mathirajan
The number of D-SDF research articles published in various journals and proceedings during the period 1992 to 2021 is computed and the same is presented in Table 13. It is observed from Table 13 that, out of 72 articles, 25 articles are from various conference proceedings (IEEE, IEEE-IEEM, IEEE-ASMC, OR, WSC), one research article is from lecture notes in computer science, two research article is from International Series in Operations Research & Management Science, and 44 research articles are from 24 different journals. In that, the journals: International Journal of Production Research, and Computers & Operations Research have the highest number of articles. As the number of articles published in some of the journals is very few, the published sources of journals are grouped into disciplines wise such as Computer Science (CS), Industrial Engineering (IE), Manufacturing (MANUF), Operations Management (OM), Operations Research (OR), Scheduling (SCH), and Business Economics (BE).
Present and future trends of supply chain management in the presence of COVID-19: a structured literature review
Published in International Journal of Logistics Research and Applications, 2023
Xiaoran Shi, Weihua Liu, Jiahui Zhang
After the initial search, we perform the include and exclude criteria to select the papers. Followed by Queiroz et al. (2020), we firstly consider the journals with an explicit focus on supply chain management issues. Meanwhile, since the pandemic significantly affects both the demand-side and the supply-side of the supply chain, which thereby arose issues related to consumers’ buying behaviours, manufacturers’ operation disruptions and technology transformation, we also scout journals in fields of ‘marketing’, ‘economics and businesses’, ‘operations management’, ‘engineering’ and ‘technology’. In the three databases aforementioned, they have categorised journals in terms of the research scopes. Therefore, we select papers published in journals marked with ‘management’, ‘operations research management science’, ‘business economics’, ‘marketing’, ‘engineering industrial/manufacturing’ and ‘green sustainable science technology’, which are believed more consistent with our research themes. For papers belonging to the area of health care, material science, humanity, and social issues, etc., they are not the interest of this research.
Applications of DEA and SFA in benchmarking studies in forestry: state-of-the-art and future directions
Published in International Journal of Forest Engineering, 2021
Niels Strange, Peter Bogetoft, Giovanna Ottaviani Aalmo, Bruce Talbot, Anders Holm Holt, Rasmus Astrup
We found 56 studies, which complied with our inclusion criteria. However, a total of 96 studies (of 33,113 records) in Web of Science, which use the terms of stochastic frontier analysis, data envelopment analysis, SFA OR DEA have been classified as belonging to the forestry research field. The difference is mainly caused by the inclusion criteria that the study should include an application of DEA or SFA on forest-related data. Not surprisingly, research fields such as business economics (25.8%), engineering (17.3%), operations research management science (14.2%), and computer science (9.5%) dominate the literature. Interestingly, agriculture represents 6% of all studies compared to only 0.3% presenting evaluations in forestry. A recent study on dynamic DEA found that most studies address efficiency analysis in the agriculture and farming sector, followed by banking and energy sectors (Mariz et al. 2018). This may indicate that the uptake of modern benchmarking may also be higher in the agricultural sector than in the forestry sector. Examples document that benchmarking has been applied extensively in agricultural practice. One such example is the UK Farm Business Survey (farmbusinessurvey.co.uk) which provides online information on the physical and economic performance of farm businesses in England and Wales. Farmbench (ahdb.org.uk/farmbench) is another online tool which farmers can anonymously sign up to and compare key performance indicators (KPI) with neighboring, local or national farmers.