Partial solution to last issue's homework assignment models of infection: person to person

In this paper, an integrated simulation optimization model for the assignment problems is developed. An effective algorithm is developed to evaluate and analyze the back-end stored simulation results. This paper proposes simulation tool SIMASI (Simulation of assignment models) to simulate assignment models. SIMASI is a tool which simulates and computes the results of different assignment models. This tool is programmed in DOT.NET and is based on analytical approach to guide optimization strategy. Objective of this paper is to provide a user friendly simulation tool which gives optimized assignment model results. Simulation is carried out by providing the required values of matrix for resource and destination requirements and result is stored in the database for further comparison and study. Result is obtained in terms of the performance measurements of classical models of assignment system. This simulation tool is interfaced with an optimization procedure based on classical models of assignment system. The simulation results are obtained and analyzed rigorously with the help of numerical examples.

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Big Questions in Ecology and Evolution

10.1093/oso/9780199548606.001.0001 ◽

2009 ◽

Cited By ~ 3

Author(s):

Thomas N. Sherratt ◽

David M. Wilkinson

Keyword(s):

Partial Solution ◽

Fundamental Importance ◽

Evolutionary Perspective ◽

Text Book ◽

Broad Audience ◽

The Future ◽

The Tropics ◽

Made In ◽

Do So

Why do we age? Why cooperate? Why do so many species engage in sex? Why do the tropics have so many species? When did humans start to affect world climate? This book provides an introduction to a range of fundamental questions that have taxed evolutionary biologists and ecologists for decades. Some of the phenomena discussed are, on first reflection, simply puzzling to understand from an evolutionary perspective, whilst others have direct implications for the future of the planet. All of the questions posed have at least a partial solution, all have seen exciting breakthroughs in recent years, yet many of the explanations continue to be hotly debated. Big Questions in Ecology and Evolution is a curiosity-driven book, written in an accessible way so as to appeal to a broad audience. It is very deliberately not a formal text book, but something designed to transmit the excitement and breadth of the field by discussing a number of major questions in ecology and evolution and how they have been answered. This is a book aimed at informing and inspiring anybody with an interest in ecology and evolution. It reveals to the reader the immense scope of the field, its fundamental importance, and the exciting breakthroughs that have been made in recent years.

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Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art

Data Science and Engineering ◽

10.1007/s41019-021-00155-3 ◽

2021 ◽

Author(s):

Yun Peng ◽

Byron Choi ◽

Jianliang Xu

Keyword(s):

Machine Learning ◽

Combinatorial Optimization ◽

Graph Embedding ◽

Partial Solution ◽

Complex Data ◽

Learning Methods ◽

Graph Learning ◽

Second Stage ◽

End To End ◽

Embedding Methods

AbstractGraphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.

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Hamiltonicity of a class of toroidal graphs

Mathematica Slovaca ◽

10.1515/ms-2017-0367 ◽

2020 ◽

Vol 70 (2) ◽

pp. 497-503

Author(s):

Dipendu Maity ◽

Ashish Kumar Upadhyay

Keyword(s):

Connected Graph ◽

Hamiltonian Cycle ◽

Partial Solution ◽

The Other ◽

Hamiltonian Cycles ◽

The Face ◽

Toroidal Graphs

Abstract If the face-cycles at all the vertices in a map are of same type then the map is said to be a semi-equivelar map. There are eleven types of semi-equivelar maps on the torus. In 1972 Altshuler has presented a study of Hamiltonian cycles in semi-equivelar maps of three types {36}, {44} and {63} on the torus. In this article we study Hamiltonicity of semi-equivelar maps of the other eight types {33, 42}, {32, 41, 31, 41}, {31, 61, 31, 61}, {34, 61}, {41, 82}, {31, 122}, {41, 61, 121} and {31, 41, 61, 41} on the torus. This gives a partial solution to the well known Conjecture that every 4-connected graph on the torus has a Hamiltonian cycle.

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Negative existentials as corrections: a partial solution to the problem of negative existentials in segmented discourse representation theory

Linguistics and Philosophy ◽

10.1007/s10988-020-09317-4 ◽

2021 ◽

Author(s):

Lenny Clapp

Keyword(s):

Representation Theory ◽

Partial Solution ◽

Discourse Representation ◽

Discourse Representation Theory ◽

Negative Existentials

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Designing a Bonus-Malus system reflecting the claim size under the dependent frequency–severity model

Probability in the Engineering and Informational Sciences ◽

10.1017/s0269964821000188 ◽

2021 ◽

pp. 1-25

Author(s):

Rosy Oh ◽

Joseph H.T. Kim ◽

Jae Youn Ahn

Keyword(s):

Recent Literature ◽

Insurance Industry ◽

Random Effect ◽

Random Effect Model ◽

Partial Solution ◽

Risk Classification ◽

Transition Rule ◽

Auto Insurance ◽

Dependent Frequency ◽

Effect Model

In the auto insurance industry, a Bonus-Malus System (BMS) is commonly used as a posteriori risk classification mechanism to set the premium for the next contract period based on a policyholder's claim history. Even though the recent literature reports evidence of a significant dependence between frequency and severity, the current BMS practice is to use a frequency-based transition rule while ignoring severity information. Although Oh et al. [(2020). Bonus-Malus premiums under the dependent frequency-severity modeling. Scandinavian Actuarial Journal 2020(3): 172–195] claimed that the frequency-driven BMS transition rule can accommodate the dependence between frequency and severity, their proposal is only a partial solution, as the transition rule still completely ignores the claim severity and is unable to penalize large claims. In this study, we propose to use the BMS with a transition rule based on both frequency and size of claim, based on the bivariate random effect model, which conveniently allows dependence between frequency and severity. We analytically derive the optimal relativities under the proposed BMS framework and show that the proposed BMS outperforms the existing frequency-driven BMS. Later, numerical experiments are also provided using both hypothetical and actual datasets in order to assess the effect of various dependencies on the BMS risk classification and confirm our theoretical findings.

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