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

محورهای موضوعی : Financial and Economic Modelling

Hossein Ali Heydari ¹ , Heresh Soltanpanah ² , Ayub Rahimzadeh ³

1 - Department of Industrial Management, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
2 - Department of Management, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran.
3 - Department of Industrial Management, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

تاریخ دریافت : 1397/12/18 تاریخ پذیرش : 1398/04/02 تاریخ انتشار : 1398/07/09

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

چکیده مقاله :

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.

منابع و مأخذ:

[1] Tabrizi, B., Ghaderi, H., A robust bi-objective model for concurrent planning of project scheduling and material procurement, Computers & Industrial Engineering, 2016, 98, P.11–29. Doi.org/10.1016/j.cie.2016.05.017.

[2] Afshar-Nadjafi, B., Karimi, H., Rahimi, A., Khalili, S., Project scheduling with limited resources using an efficient differential evolution algorithm, Journal of King Saud University – Engineering Sciences, 2015, 27, P.176–184. Doi.org/10.1016/j.jksues.2013.08.003.

[3] Bernardo, F., Almeida, Isabel, C., Francisco Saldanha-da Gama, Priority-based heuristics for the multi-skill resource constrained project scheduling problem, Expert Systems with Applications,2016, 57, P.91–103. Doi.org/10.1016/j.eswa.2016.03.017.

[4] Ebrahimnezhad, S., Ahmadi, V., Javanshir, H., Time-Cost-Quality Trade-off in a CPM1 Network Using Fuzzy Logic and Genetic Algorithm, International Journal of Industrial Engineering &Production management, 2013, 24(3), P.361—376. (In Persian).

[5] Eidi A. R., Farughi, H., Eidi F., Presenting two innovative methods to solve problem of multi-objective time-cost-project quality balance in a discrete state with generalized pre-requisite constraints, Journal of Sharif Industrial Engineering and Management, 2016, 32(1), P.35-46. (In Persian).

[6] Haupt R.L., Haupt S.E, Practical genetic algorithms, Hoboken, New Jersey, A Wiley-Inter science publication.

[7] Jafari Eskandari, M., Yaghoubi, S., Farahmandnazar, M., Solving scheduling problem in oil projects under conditions of resource constraints using the firefly algorithm. Scientific-Expeditionary Magazine of Exploration and Production of Oil and Gas. 131, P.40-46. (In Persian).

[8] Jeroen, B., Mario, V., Maximizing the weighted number of activity execution modes in project planning, European Journal of Operational Research, 2018, 270(3) P.1–15. Doi.org/10.1016/j.ejor.2018.04.035.

[9] Jing, X., Zhou, W., Hong, X. X., Jian-Chao, T., Tang, Y., Integration of electromagnetism with multi-objective evolutionary algorithms for RCPSP, European Journal of Operational Research, 2015, 251(1) P.22-35. Doi.org/10. 1016/j.ejor.2015.10.059.

[10] Martin, T., Anulark, N., Rainer, K., A Hybrid Meta-heuristic for Resource-Constrained Project Scheduling with Flexible Resource Profiles, European Journal of Operational Research, 2017, 262(1), P.1-36. Doi.org/10.1016/j.ejo r.2017.03.006.

[11] Miryekemami, S., Sadeh, E., Amini Sabegh. Z., Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange., Advances in mathematical finance  & applications,2017, 2(4), P.107-120.Doi: 10.22034/amfa.2017.536271.

[12] Mohammed El-Abbasy, S., Elazouni, A., Tarek Zayed., MOSCOPEA: Multi-objective construction scheduling optimization using elitist non-dominated sorting genetic algorithm, Automation in Construction, 2016, 71, P.153–170. Doi.org/10.1016/j.autcon.2016.08.038.

[13] Muritiba, A., Einstein,F, Rodrigues, Carlos Diego, Francíio Araùjo da Costa., A Path-Relinking algorithm for the multi-mode resource-constrained project scheduling problem, Computers and Operations Research, 2018, 92, P. 145–154. Doi.org/10.1016/j.cor.2018.01.001.

[14] Pham D.T., Ashraf Afify., Ebubekir K., Manufacturing cell formation using the Bees Algorithm, IPROMS Innovative Production Machines and Systems Virtual Conference, Cardiff, UK.

[15] Ripon, K., Chakrabortty, R., Daryl L. Essa M., Multi-mode resource constrained project scheduling under resource disruptions, Computers and Chemical Engineering, 2016, 88,P.13–29.Doi: 10.1016/j.compchemeng.2016.01.004.

[16] Safa, M., Panahian, H., Application of HS Meta-heuristic Algorithm in Designing a Mathematical Model for Forecasting P/E in the Panel Data Approach, Advances in mathematical finance& applications, 2018, 3(1), P. 17-31. Doi:10.22034/amfa.2018.539132.

[17] Schott, J. R., Fault Tolerant Design Using Single and Multi-criteria Genetic Algorithm Optimization (No.AFIT/CI/CIA-95-039).AIR FORCE INST OF TECH WRIGHTPATTERSON AFB OH,1995 Doi:hdl.handle.net/1721.1/11582.

[18] Shahriar, A., Karapetyan, D., Kheiri, A., Ender Özcan, Andrew J. Parkes.,Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem, Information Sciences, 2016, 373, P.476–498. Doi.org/10.1016/j.ins.2016.09.010.

[19] Tao, S., Changzhi Wu, Zhaohan Sheng, Xiangyu Wang., Stochastic Project Scheduling with Hierarchical Alternatives, Applied Mathematical Modelling, 2017, 6(5), P.70-75. Doi.org/10.1016/j.apm.2017.09.015

[20] Sonda, E., Fortemps, P., Taïcir Loukil., Multi-objective algorithms to multi-mode resource-constrained projects under mode change disruption, Computers & Industrial Engineering, 2017, 106, P.161–173. Doi.org/10.1016/j.cie.2017.01.029.

[21] Stefan Kreter, Julia Rieck, Jürgen Zimmermann, Models and solution procedures for the resource constrained project scheduling problem with general temporal constraints and calendars, European Journal of Operational Research,2016, 251, P.387–403. Doi.org/10.1016/j.ejor.2015.11.021.

[22] Izadikhah, M., Saeidifar, A., Roostaee, R., Extending TOPSIS in fuzzy environment by using the nearest weighted interval approximation of fuzzy numbers, Journal of Intelligent & Fuzzy Systems, 2014, 27 (6), P.2725-2736, Doi: 10.3233/IFS-131109.

[23] Taghizadeh Yazdi MR, Ghafouri S, Presenting a mathematical model for problem of balancing time-cost-environmental effects and solving it using particle and particle-flaring meta-heuristic Algorithms. Industrial management vision journal,2016, 6(24), P.97-121. (In Persian).

[24] Walter J.G., Bi-Objective Multi-Mode Project Scheduling Under Risk Aversion, European Journal of Operational Research, 2015, 246, P.421–434. Doi.org/10.1016/j.ejor.2015.05.004.

[25] Zamanian, M., Sadeh, E., Amini Sabegh, Z., A Fuzzy Goal-Programming Model for Optimization of Sustainable Supply Chain by Focusing on the Environmental and Economic Costs and Revenue: A Case Study., Advances in mathematical finance & applications, 2019, 4(1), P.103-123.10.22034/amfa.2019.578990.1134.

[26] Zhu G., Bard J.F., Yu G., A branch-and-cut procedure for the multi-mode resource-constrained project-scheduling problem, Informs J. Comput, 2006, 18(3), P.377–90. Doi.org/10.1287/ijoc.1040.0121.