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The goal of this research is to develop a mixed methodology consisting of qualitative and quantitative approaches to find the factors that significantly affect the effectiveness of industrial parks of Iran. Through using this methodology, the impacts of the detected factors are analyzed. The methodology seeks to direct Iran’s industrial parks to sustainable development; hence, the factors include sustainability-related elements as well. For the qualitative approach, the literature has been scrutinized and with the help of practitioners in focus groups, some of the most important factors influencing the effectiveness of industrial parks have been discovered. For the quantitative approach, a reliable questionnaire has been designed which asked the importance of the identified factors from a community of experts. Having acquired the viewpoints of the experts, the Confirmatory Factor Analysis (CFA) has been applied to investigate the relations between the factors and the effectiveness of parks. Results of the model have been thoroughly analyzed and it has been revealed that the factors “accessibility to infrastructural facilities”, “economic elements”, and “existence of supporting services” had the highest impact on the effectiveness of Iran’s parks. The proposed model has been applied to provinces of Iran and they have been ranked according to their effectiveness scores. [ABSTRACT FROM AUTHOR]

In this paper, we propose an energy-efficient mathematical model for the resourceconstrained project scheduling problem to optimize makespan and consumption of energy, simultaneously. In the proposed model, resources are speed-scaling machines. The problem is NP-hard in the strong sense. Therefore, a multi-objective fruit fly optimization algorithm (MOFOA) is developed. The MOFOA uses the VIKOR as a multi-criteria decision making (MCDM) method to rank solutions in vision-based search procedure. The proposed algorithm is applied to small, medium and large size problems to evaluate its performance. Comprehensive numerical tests are conducted to evaluate the performance of the MOFOA in comparison to three other meta-heuristics in terms of convergence, diversity and computation time. The experimental results significantly show that the proposed algorithm can surpass other methods in terms of most of the metrics. Besides, the results of meta-heuristics are compared with the outputs of GAMS software for small size problems. [ABSTRACT FROM AUTHOR]