Application of AI Programming Techniques to the Analysis of Structures

Performance analysis of mutual funds is usually made on the basis of return-risk framework. Traditionally, excess return (over risk-free rate) to risk ratios were used for the purpose mutual fund evaluation. Subsequently, the application of non-parametric mathematical programming techniques in the context of performance evaluation facilitated multi-criteria decision making. However,the estimates of performance on the basis of conventional programming techniques like DEA and FDH are affected by the presence of outliers in the sample observations. The present, accordingly uses more robust benchmarking techniques for evaluating the performance od sectoral mutual fund schemes based on observations for the second half of 2010. The USP of the present study is that it uses two partial frontier techniques (Order-m and Order- a) which are less susceptible to the problem of extreme data.

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Proceedings of the 4th ACM SIGPLAN International Workshop on Meta-Programming Techniques and Reflection - META 2019

10.1145/3358502 ◽

2019 ◽

Keyword(s):

International Workshop ◽

Meta Programming ◽

Programming Techniques

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Introducing Microsoft Access Using Macro Programming Techniques

10.1007/978-1-4842-6555-0 ◽

2021 ◽

Author(s):

Flavio Morgado

Keyword(s):

Microsoft Access ◽

Programming Techniques

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A Hybrid Methodology to Minimize Freshwater Consumption during Shrimp Shell Waste Valorization Combining Multi-Contaminant Pinch Analysis and Superstructure Optimization

Polymers ◽

10.3390/polym13111887 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1887

Author(s):

Viviana Quintero ◽

Arturo Gonzalez-Quiroga ◽

Angel Darío Gonzalez-Delgado

Keyword(s):

Process Integration ◽

Preliminary Design ◽

Water Network ◽

Hybrid Strategy ◽

Shrimp Shell ◽

Optimal Value ◽

Shell Waste ◽

Programming Techniques ◽

Shrimp Shell Waste ◽

Freshwater Consumption

The conservation and proper management of natural resources constitute one of the main objectives of the 2030 Agenda for Sustainable Development designed by the Member States of the United Nations. In this work, a hybrid strategy based on process integration is proposed to minimize freshwater consumption while reusing wastewater. As a novelty, the strategy included a heuristic approach for identifying the minimum consumption of freshwater with a preliminary design of the water network, considering the concept of reuse and multiple pollutants. Then, mathematical programming techniques were applied to evaluate the possibilities of regeneration of the source streams through the inclusion of intercept units and establish the optimal design of the network. This strategy was used in the shrimp shell waste process to obtain chitosan, where a minimum freshwater consumption of 277 t/h was identified, with a reuse strategy and an optimal value of US $5.5 million for the design of the water network.

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An analytics-based heuristic decomposition of a bilevel multiple-follower cutting stock problem

OR Spectrum ◽

10.1007/s00291-021-00638-9 ◽

2021 ◽

Author(s):

Adejuyigbe O. Fajemisin ◽

Laura Climent ◽

Steven D. Prestwich

Keyword(s):

Real Life ◽

Forest Harvesting ◽

Cutting Stock Problem ◽

Cutting Stock ◽

Decomposition Approach ◽

New Class ◽

Bilevel Problem ◽

Programming Techniques ◽

Bilevel Problems ◽

Better Than

AbstractThis paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k-medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k-medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approach.

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A Constrained Markovian Diffusion Model for Controlling the Pollution Accumulation

Mathematics ◽

10.3390/math9131466 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1466

Author(s):

Beatris Adriana Escobedo-Trujillo ◽

José Daniel López-Barrientos ◽

Javier Garrido-Meléndez

Keyword(s):

Dynamic Programming ◽

Dirichlet Problem ◽

Stochastic Control ◽

Finite Time ◽

Time Horizon ◽

Closed Loop ◽

Programming Techniques ◽

Pollution Accumulation ◽

Finite Time Horizon ◽

The Cost

This work presents a study of a finite-time horizon stochastic control problem with restrictions on both the reward and the cost functions. To this end, it uses standard dynamic programming techniques, and an extension of the classic Lagrange multipliers approach. The coefficients considered here are supposed to be unbounded, and the obtained strategies are of non-stationary closed-loop type. The driving thread of the paper is a sequence of examples on a pollution accumulation model, which is used for the purpose of showing three algorithms for the purpose of replicating the results. There, the reader can find a result on the interchangeability of limits in a Dirichlet problem.

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