A novel modeling approach for the fleet deployment problem within a short-term planning horizon

This paper describes a decision support system that forest managers can use to help evaluate short-term, site-specific silvicultural operating plans in terms of their potential impact on long-term, forest-level strategic objectives. The system is based upon strategic and tactical forest-level silvicultural planning models that are linked with each other and with a geographical information system. Managers can first use the strategic mathematical programming model to develop broad silvicultural strategies based on aggregate timber strata. These strategies help them to subjectively delineate specific candidate sites that might be treated during the first 10 years of a much longer planning horizon using a geographical information system and to describe potential silvicultural prescriptions for each candidate site. The tactical model identifies an annual silvicultural schedule for these candidate sites in the first 10 years, and a harvesting and regeneration schedule by 10-year periods for aggregate timber strata for the remainder of the planning horizon, that will maximize the sustainable yield of one or more timber species in the whole forest, given the candidate sites and treatments specified by the managers. The system is demonstrated on a 90 000 - ha area in northeastern Ontario.

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The early bird catches the retirement savings

Qualitative Research in Financial Markets ◽

10.1108/qrfm-03-2018-0030 ◽

2019 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Lucía Rey-Ares ◽

Sara Fernández-López ◽

María Milagros Vivel-Búa ◽

Rubén Lado-Sestayo

Keyword(s):

Social Factors ◽

Cognitive Abilities ◽

Design Methodology ◽

Empirical Studies ◽

Planning Horizon ◽

Retirement Savings ◽

Short Term ◽

Relevant Variable ◽

Content Type

Purpose This paper aims to investigate whether individuals’ planning horizon influences their decision to save privately for their retirement. Design/methodology/approach Focussing on Spain, this empirical research uses the fifth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE)[1]. Logit models are estimated considering variables related to demographic characteristics, economic situation, education and cognitive abilities and psychological and social factors. Findings The results confirm that the planning horizon significantly influences the decision to save for retirement. Long-term planners are more likely to save for retirement than short-term planners. Originality/value Although previous literature has identified the planning horizon as a relevant variable in the decision to save for retirement, few empirical studies have evaluated their impact. This paper shows that it is important to develop habits of financial planning in societies, especially in societies with a prominent orientation towards the present.

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A residual load modeling approach for household short-term load forecasting application

Energy and Buildings ◽

10.1016/j.enbuild.2019.01.009 ◽

2019 ◽

Vol 187 ◽

pp. 132-143 ◽

Cited By ~ 8

Author(s):

Fatima Amara ◽

Kodjo Agbossou ◽

Yves Dubé ◽

Sousso Kelouwani ◽

Alben Cardenas ◽

...

Keyword(s):

Load Forecasting ◽

Load Modeling ◽

Short Term ◽

Modeling Approach ◽

Short Term Load Forecasting

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Abstract: Short-Term Financial Planning under Uncertainty

Journal of Financial and Quantitative Analysis ◽

10.1017/s0022109000023498 ◽

1977 ◽

Vol 12 (4) ◽

pp. 665-665

Author(s):

J. G. Kallberg ◽

R. W. White ◽

W. T. Ziemba

Keyword(s):

Linear Programming ◽

Financial Planning ◽

Programming Model ◽

Planning Horizon ◽

Planning Under Uncertainty ◽

Stochastic Linear Programming ◽

Short Term ◽

Simple Recourse ◽

The Cost ◽

Novel Algorithm

The essence of short-term financial planning is to determine an asset and liability mix that minimizes the cost of financing the firm's cash surpluses and deficits over the planning horizon. Seasonal and other effects cause uncertainty in forecasted cash requirements, liquidation, and termination costs. We develop a stochastic linear programming model that is computationally feasible for a firm's financial planning over several periods with all three types of uncertainty when there is a rich structure over the set of possible asset choices. The models' solution is facilitated using a recent novel algorithm for finitely distributed simple recourse SLPR problems developed by Wets and coded by Collins, Kallberg and Kusy. The algorithm uses a “working basis” that has the same dimension as the corresponding (approximate) “mean” linear program.

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A NOVEL APPROACH FOR SHORT-TERM LOAD FORECASTING USING SUPPORT VECTOR MACHINES

International Journal of Neural Systems ◽

10.1142/s0129065704002078 ◽

2004 ◽

Vol 14 (05) ◽

pp. 329-335 ◽

Cited By ~ 24

Author(s):

LIANG TIAN ◽

AFZEL NOORE

Keyword(s):

Neural Network ◽

Load Forecasting ◽

Support Vector ◽

Short Term ◽

Feed Forward Neural Network ◽

Modeling Approach ◽

Novel Approach ◽

Power Load ◽

Short Term Load Forecasting ◽

Network Approaches

A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.

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Predicting mobile apps spread: An epidemiological random network modeling approach

SIMULATION ◽

10.1177/0037549717712600 ◽

2017 ◽

Vol 94 (2) ◽

pp. 123-130 ◽

Cited By ~ 1

Author(s):

Juan Alegre-Sanahuja ◽

Juan-Carlos Cortés ◽

Rafael-Jacinto Villanueva ◽

Francisco-José Santonja

Keyword(s):

Network Model ◽

Mobile Applications ◽

Mobile Apps ◽

Random Network ◽

Network Modeling ◽

Numerical Results ◽

Business Managers ◽

Short Term ◽

Modeling Approach ◽

Proposed Model

The mobile applications business is a really big market, growing constantly. In app marketing, a key issue is to predict future app installations. The influence of the peers seems to be very relevant when downloading apps. Therefore, the study of the evolution of mobile apps spread may be approached using a proper network model that considers the influence of peers. Influence of peers and other social contagions have been successfully described using models of epidemiological type. Hence, in this paper we propose an epidemiological random network model with realistic parameters to predict the evolution of downloads of apps. With this model, we are able to predict the behavior of an app in the market in the short term looking at its evolution in the early days of its launch. The numerical results provided by the proposed network are compared with data from real apps. This comparison shows that predictions improve as the model is fed back. Marketing researchers and strategy business managers can benefit from the proposed model since it can be helpful to predict app behavior over the time anticipating the spread of an app.

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