14,376 research outputs found
Opportunities for the digital transformation of the banana sector supply chain based on software with artificial intelligence
Artificial intelligence offers great opportunities for the supply chain, being this a competitive advantage for today’s changing market. This article aims to identify the impacts and opportunities that artificial intelligence software can offer to facilitate the operation and improve the performance of the supply chain in the banana sector in Colombia. The work methodology consists of six steps in which a total of 72 investigations were obtained. The sources of information were four databases. As a main conclusion, the supply chain of the banana sector has everything necessary for intelligent software based solutions to be implemented in order to achieve adaptation, flexibility and sensitivity to the context and domain of execution
Analytic Hierarchy Process and Supply Chain Management: A Bibliometric Study
AbstractA comparative study was used to outline the literature in the research topic. This paper aims to present a bibliometric study ofmulti-criteria decision-making methods most applied in publications from 1990 to 2014. Our research presented relations of papers published in the Web of Science Core Collection, regarding the following keywordsAnalytic Hierarchy Process and Supply Chain. The research evidenced that the Analytic Hierarchy Process has been the method mostapplied in publications from 1993. It also showed the analysis of the predecessor and successor citation network for the selected publications under topics as supplier selection, supply development, performance measurement and value chain through the CitNetExplore software
Initialization of a Multi-objective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants
pp. 185-204The design of Renewable Energy Power Plants (REPPs) is crucial not only for the
investments' performance and attractiveness measures, but also for the maximization of
resource (source) usage (e.g. sun, water, and wind) and the minimization of raw
materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te)
consumption. Hence, several appropriate and satisfactory Multi-objective Problems
(MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only
be managed by very well organized knowledge acquisition on all REPPs' design
equations and models. The proposed MOPs need to be solved with one or more multiobjective algorithm, such as Multi-objective Evolutionary Algorithms (MOEAs). In this
respect, the first aim of this research study is to start gathering knowledge on the REPPs'
MOPs. The second aim of this study is to gather detailed information about all MOEAs
and available free software tools for their development. The main contribution of this
research is the initialization of a proposed multi-objective evolutionary algorithm
knowledge acquisition system for renewable energy power plants (MOEAs-KAS-FREPPs) (research and development loopwise process: develop, train, validate, improve,
test, improve, operate, and improve). As a simple representative example of this
knowledge acquisition system research with two selective and elective proposed
standard objectives (as test objectives) and eight selective and elective proposed
standard constraints (as test constraints) are generated and applied as a standardized
MOP for a virtual small hydropower plant design and investment. The maximization of
energy generation (MWh) and the minimization of initial investment cost (million €)
are achieved by the Multi-objective Genetic Algorithm (MOGA), the Niched Sharing
Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the
NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all
proposed standardized MOEAs on two desktop computer configurations (Windows 10
Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet
connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB
RAM with internet connection). The algorithm run-times (computation time) of the
current applications vary between 20.64 and 59.98 seconds.S
A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications
Enterprise financial risk analysis aims at predicting the enterprises' future
financial risk.Due to the wide application, enterprise financial risk analysis
has always been a core research issue in finance. Although there are already
some valuable and impressive surveys on risk management, these surveys
introduce approaches in a relatively isolated way and lack the recent advances
in enterprise financial risk analysis. Due to the rapid expansion of the
enterprise financial risk analysis, especially from the computer science and
big data perspective, it is both necessary and challenging to comprehensively
review the relevant studies. This survey attempts to connect and systematize
the existing enterprise financial risk researches, as well as to summarize and
interpret the mechanisms and the strategies of enterprise financial risk
analysis in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. This paper provides a
systematic literature review of over 300 articles published on enterprise risk
analysis modelling over a 50-year period, 1968 to 2022. We first introduce the
formal definition of enterprise risk as well as the related concepts. Then, we
categorized the representative works in terms of risk type and summarized the
three aspects of risk analysis. Finally, we compared the analysis methods used
to model the enterprise financial risk. Our goal is to clarify current
cutting-edge research and its possible future directions to model enterprise
risk, aiming to fully understand the mechanisms of enterprise risk
communication and influence and its application on corporate governance,
financial institution and government regulation
An Expert System to Improve the Energy Efficiency of the Reaction Zone of a Petrochemical Plant
Energy is the most important cost factor in the petrochemical industry.
Thus, energy efficiency improvement is an important way to reduce these
costs and to increase predictable earnings, especially in times of high energy
price volatility. This work describes the development of an expert system for
the improvement of this efficiency of the reaction zone of a petrochemical
plant. This system has been developed after a data mining process of the variables
registered in the plant. Besides, a kernel of neural networks has been
embedded in the expert system. A graphical environment integrating the proposed
system was developed in order to test the system. With the application of
the expert system, the energy saving on the applied zone would have been about
20%.Junta de Andalucía TIC-570
Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design
This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization
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