A mathematical model for balancing (cost-time-quality and environmental risks) in oil and gas projects and solving it by multi-objective Bee Colony Algorithm

Subject Areas : Financial and Economic Modelling

Hossein Ali  Heydari ¹ (Department of Industrial Management, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.)
Heresh  Soltanpanah ² (Department of Management, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran.)
Ayub  Rahimzadeh ³ (Department of Industrial Management, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.)

Received: 2019-03-09 Accepted : 2019-06-23 Published : 2019-10-01

Keywords: Bee Colony algorithm, Generalized pre-requisites, Project quality, environmental risks, Cost,

Abstract :

Today, in large projects such as constructing oil, gas and petrochemical refineries, it is inevitable to use modern management methods and project timing. On the other hand, in classic scheduling case, the focus is on balance between time and cost of carrying out projects, which in such a situation, one of possible solutions to shorten time of implementing project is to accelerate activities. This acceleration can affect the quality of conducting projects and environmental impacts, in addition to impose more costs. Hence, in such studies, environmental impacts and quality of activities were also considered as new indicators in case of project time-cost balance. There has been proposed a new mathematical model with four indicators: cost, time, quali-ty and environmental impacts. The provided model is a multi-objective mathematical model of zero-and-one programming type that despite traditional models, in which there is only considered an implementation mode for carrying out activities and a pre-conditional relationship between activities, modes of implementing activities are as multi-form and the dependence relationship between the activities is a generalized pre-requisite. Including the relationships brings the problem closer to the real world. Because of NP-hard of the problem in large dimensions and the necessity of using meta-heuristic Algorithms, we used MOBEE algorithm to solve the model.

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