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The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

This study aims to detect the influential factors and their respective variables that affect the effectiveness or demand-driven level of the industrial parks in Iran. A hybrid procedure is sought to be developed, which embraces both qualitative and quantitative methodologies to identify the aforementioned factors and variables.

This study is incorporated with both qualitative and quantitative methodologies. To implement the qualitative approach, the researchers used focus groups and the related literature. The quantitative methodology has been carried out via a reliable questionnaire that obtained the viewpoints of 700 experts. The reliability of the designed questionnaire has been investigated through Cronbach’s alpha coefficient. By conducting several one-sample t-tests, it was confirmed that the identified factors and variables are significantly influential on the effectiveness of Iran’s industrial parks. The Kruskal–Wallis statistical test was used to determine the priorities of the factors. This research also used a multi-criteria decision-making method, namely, the weighted aggregates sum product assessment (WASPAS) to rank 15 industrial parks of Khorasan province in Iran according to the identified factors.

Comprehensive analyses have been conducted on the identified factors. Results indicate that the infrastructural facilities factor has the highest priority when it comes to affecting the effectiveness of the industrial parks. After that, industrial land and internal factors take the second and the third positions in terms of importance. A total of 15 industrial parks of the aforementioned province have been ranked by the WASPAS. The ranking offered by the WASPAS has been approved by the experts.

Based on the literature investigations, the authors were convinced that there is a scarcity of studies investigating the influential factors that affect the effectiveness or demand-driven level of industrial parks (especially in Iran). Hence, this research has been conducted to propose a procedure equipped with quantitative and qualitative techniques that detect these important factors and their subordinate variables. By means of the developed procedure of this research, it is possible to locate future industrial parks, plan for establishment of future industrial areas and plan for development of current industrial parks.