12 research outputs found
Using Weighted Goal Programming Model for Planning Regional Sustainable Development to Optimal Workforce Allocation:An Application for Provinces of Iran
Due to the urbanization and economic growth, planning of regional sustainable development has become one of the major challenges in the world. The key indicators such as gross domestic product (GDP), electricity and energy consumption and greenhouse gas emission (GHG) are considered in sustainable development planning. This paper determines number of required workforce in diferent sectors of each province in Iran considering targets/goals for sustainable development indicators in the 2030 macroeconomic and regional planning. First, the relative goals are designed for GDP, electricity, energy and GHG emission and then, two weighted goal programming models are applied to allocate the optimal workforce among four sectors: agriculture, industry, services and transportation. The frst model minimizes recruitment of new workforce and allows current workforce exchange among the four sectors in each province in order to achieve the goals, while the second model indicates equitable distribution of new workforce recruitment in diferent sectors within each province. In both models, the workforce changes have been investigated based on achieving the desirable growth rates of GDP, GHG, electricity and energy consumption as planned by the government. Based on the results of this paper, policy makers can manage workforce and the government can make optimized decisions to macroeconomic and regional planning
Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention
A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/
Quick fix for obstacles emerging in management recruitment measure using IOT-based candidate selection
An integrated approach of SWARA and fuzzy COPRAS for maintenance technicians’ selection factors ranking
Multi-granularity unbalanced linguistic group decision-making with incomplete weight information based on VIKOR method
Environmental effects of tourism and its seasonality on Mediterranean islands: the contribution of the Interreg MED BLUEISLANDS project to build up an approach towards sustainable tourism
Sustainability evaluation for phosphorus mines using a hybrid multi-criteria decision making method
Comparative State-of-the-Art Survey of Classical Fuzzy Set and Intuitionistic Fuzzy Sets in Multi-Criteria Decision Making
Diversifying Mediterranean Tourism as a Strategy for Regional Resilience Enhancement
Tourism-based regions, especially in southern Europe, are extremely vulnerable to
the expected impacts of climate change (Amelung and Moreno 2009, pp. 16–25). In
the case of the south of Portugal, the Algarve region’s socio-economic base is
grounded on summer sun and beach tourism, i.e. coastal tourism with large-scale
and concentrated accommodation. This could be drastically affected by climate
change, essentially derived from sea level rise and potential beach area reduction,
constraining the future of a region that strongly depends on this type of tourism.
From the assumed weakness of the touristic use of this Mediterranean climate
region to the impacts of climate change, it urges a reflection about what adaptive
strategies can be considered.info:eu-repo/semantics/publishedVersio