Economic Analysis and Generic Algorithm for Optimizing the Investments Decision-Making Process in Oil Field Development

Due to the substantial amounts of money involved and the complex interactions of a number of different factors, managers of oil and gas companies are faced with significant challenges when making investment decisions that will increase business efficiency and achieve competitive advantages, especially through cost control. Due to the various uncertainties of the current period, optimal investment strategies are difficult to determine. Thus, through an economic analysis that includes data analysis, quantitative risk analysis scenarios, modelling and simulations, a work framework, in the form of a generic algorithm, is proposed with the aim of generating a complex procedure for optimizing investment decisions in oil field development. A complex set of elements is considered in the analysis: costs (operational expenditures (OPEX) and capital expenditures (CAPEX), daily drilling rig costs), prices (oil, gas, separation and water injection preparation), production profiles, different types of taxes and discount factors. Above all, oil price volatility plays an essential role and creates uncertainty in relation to profitability and the strategic investment decisions made by oil exploration and production companies.

Proctors Assignment at the University of Information Technology – VNU HCM

This paper is concerned with the proctor assignment problem. Although there have been many proposed models for solving this kind of problem, it turns out that those models perform well only in their context. Also, no models can solve all problems in any context. Hence, creating a model for a specific problem is essential. In this paper, we define the problem and challenges of assigning proctors at our university as an optimization model. We experiment on two methods: the Generic Algorithm and the Integer Programming, then compare the results. For the Integer programming, we proposed an objective loss called zero-min loss, which minimizes the spans of days the proctors must be at the university during the examination time. Our main result shows that the proposed Integer Programming gives a 2285 higher score in fitness value compare to the Generic Algorithm.

Comparison of Algorithms of Individual and Group Behavior

It has been suggested that the functionality of matter, life, and mind can be described by algorithms containing a sequence of steps and feedback mechanisms. Social processes were until now not considered. Consequently, we examine algorithms of behavior of groups of various kinds, identify their common parameters, and undertake a comparative analysis to the algorithm of individual behavior. We conclude, that despite some application-specific differences, groups operate in accordance with a unified algorithm and, furthermore, this algorithm is the same as the generic algorithm of individual behavior. We demonstrate that in the generally perceived progression matter-life-mind-culture/society, the latter transition cannot be validated. Homogeneity of algorithms of individual and group behavior leads to the proposition that the human mind/psyche and social processes belong to the same level of complexity of nature. This challenges the commonly held perception that society/culture is a standalone perspective of reality separate from the mind.

THE GENERAL COMPLEXITY OF THE PROBLEM TO RECOGNIZE HAMILTONIAN PATHS

Generic-case approach to algorithmic problems has been offered by A. Miasnikov, V. Kapovich, P. Schupp, and V. Shpilrain in 2003. This approach studies an algorithm behavior on typical (almost all) inputs and ignores the rest of inputs. In this paper, we study the generic complexity of the problem of recognition of Hamiltonian paths in finite graphs. A path in graph is called Hamiltonian if it passes through all vertices exactly once. We prove that under the conditions P 6= NP and P = BPP for this problem there is no polynomial strongly generic algorithm. A strongly generic algorithm solves a problem not on the whole set of inputs, but on a subset, the sequence of frequencies of which exponentially quickly converges to 1 with increasing size. To prove the theorem, we use the method of generic amplification, which allows to construct generically hard problems from the problems hard in the classical sense. The main component of this method is the cloning technique, which combines the inputs of a problem together into sufficiently large sets of equivalent inputs. Equivalence is understood in the sense that the problem is solved similarly for them.

A generic algorithm for the membership problem in the semigroups of integer matrices

Generic-case approach to algorithmic problems was suggested by A. Miasnikov, I. Kapovich, P. Schupp and V. Shpilrain in 2003. This approach studies behavior of an algorithm on typical (almost all) inputs and ignores the rest of inputs. In this paper we prove generic decidability of the membership problem and the mortality problem for semigroups of integer matrices of arbitrary order.