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Using Weighted Goal Programming Model for Planning Regional Sustainable Development to Optimal Workforce Allocation: An Application for Provinces of Iran

  • Hashem Omrani
  • Mahsa Valipour
  • Ali Emrouznejad
Article

Abstract

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 different 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 first 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 different 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.

Keywords

Sustainable development Weighted goal programming Regional planning Optimal workforce allocation 

Notes

Acknowledgements

The authors would like to thank Professor Filomena Maggino, the editor of Social Indicators Research Journal, and five reviewers for their insightful comments and suggestions. As results this paper has been improved substantially.

Supplementary material

11205_2018_1868_MOESM1_ESM.docx (23 kb)
Supplementary material 1 (DOCX 23 kb)

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Hashem Omrani
    • 1
  • Mahsa Valipour
    • 1
  • Ali Emrouznejad
    • 2
  1. 1.Faculty of Industrial EngineeringUrmia University of TechnologyUrmiaIran
  2. 2.Aston Business SchoolAston UniversityBirminghamUK

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