ارائه مدل زمانبندی استوار پروژه با منابع محدود و حل آن با استفاده از الگوریتم فرا ابتکاری بهینه سازی انبوه ذرات (مطالعه موردی: پالایشگاه میعانات گازی بندر عباس)
محورهای موضوعی : مدیریت صنعتیMohammadhusein Nabizadeh 1 , Huseinali Hasanpoor 2 , Roozbeh Azizmohammadi 3 , Navid Hashtroodi 4
1 - University of Imam Hussein (AS)
2 - University of Imam Hussein (AS)
3 - Department of Industrial Engineering, Payam Noor University, Tehran, Iran
4 - M.A in Industrial Engineering
کلید واژه: پایداری, Flexibility, Project Scheduling, انعطاف پذیری, particle swarm optimization algorithm, زمانبندی پروژه, الگوریتم بهینه سازی انبوه ذرات, Robustness,
چکیده مقاله :
انجام فعالیت های پروژه مطابق برنامه زمان بندی یکی از مسائل مورد توجه دست اندرکاران پروژه ها به ویژه مدیران پروژه ها می باشد. همچنین ماهیت بسیار دشوار این مسئله، علت دیگری برای توجه زیاد محققین به آن می‌باشد. بنابراین تکنیک ها و روش‌های خاصی برای حل این مسائل مطرح شده‌اند. از اینرو توجه بیشتر به پایداری زمانبندی پروژه برای مدیران پروژه موضوعیت دارد. در این مقاله برای یک مسئله واقعی زمانبندی پروژه پالایشگاهی ابتدا مدل زمانبندی پایدار ارائه‌شده و به دلیل اینکه زمانبندی پروژه با محدودیت منابع از جمله مسائل NP-Hard است، الگوریتم فرا ابتکاری بهینه سازی انبوه ذرات برای حل این مسئله پیشنهاد شده است. به منظور اعتبارسنجی مدل نیز 4 مسئله با ابعاد کوچک انتخاب و جواب‌های به دست آمده از الگوریتم‌های پیشنهادی با جواب دقیق به دست آمده حاصل از نرم‌افزار Lingo مقایسه گردیده است. نتایج به دست آمده نشان می دهد الگوریتم پیشنهادی کارا و همگرا به جواب بهینه می‌باشند.
One of the issues considered by the projects responsible especially project managers is the execution of project activities according to time schedule. The very difficult nature of that issue is also another reason for the researchers to take much note of it. Therefore, there are especial techniques and methods to solve those issues. Also, project managers pay much attention to the stability of the time schedule as it is important for them. This paper is provided with a real project time schedule for a refinery by using stable time schedule. Particle swarm optimization algorithm is suggested to resolve the problem since the project time schedule has resources limitation including NP- Hard. In order to accesses the validation of the model, 4 issues with small scales has been selected and the results from the suggested algorithm was compared with the accurate result obtained from lingo software. These results indicate that the suggested algorithm is effective and convergent with the optimized result.
1- Damghani, k., Tavakoli moghadam, R., & Tabari, M. (2011). Solving the problems of Scheduling projects with constraint resources by improved ant algorithm, Industrial Engineering, 45 (1), 59 – 69.
2- Aliahmadi A., Jafari M., Mortaji T., Nozari H. (2013). Improve prediction of Project completion time Based Earned Value Management System, Journal of Tomorrow management, 37, 5-19.
3- Basaghzade I., Hejazi S., Amirmosa E. (2010). Development of project scheduling model with objectives of completion time and rebostness of scheduling, Industrial Engineering, 44 (1), 13 -24.
4- Wang C., Bingqi J., Li G., Zhe T.,Jide N., Siwei L. (2016). Robust scheduling of building energy system under uncertainty, Journal of Applied Energy, 167, 366-376.
5- Ke H., Wang L., Huang H. (2015). An uncertain model for RCPSP with solution robustness focusing on logistics project schedule, International Journal of e-Navigation and Maritime Economy, 3, 71-83.
6- Angela H. and Chiuh-Cheng C. (2008). A Memetic Algorithm for Maximizing Net Present Value in Resource Constrained Project Scheduling Problem, IEEE Congress on Evolutionary Computation, 2041-2049.
7- Wei1 S. (2013), Project Scheduling Under Resource Constraints: A Recent Survey, International Journal of Engineering Research & Technology (IJERT), 2, 1-20.
8- Herroelen W., and Demeulemeester E., and De Reyck, B. (1999). A Classification Scheme for Project Scheduling (Project Scheduling-Recent Models, Algorithms and Application), Boston-Kluwer Academic.
9- Vanhoucke M., Debles D. (2011). The impact of various activity assumption on the lead time and resource utilization of resource-constrained project, Computer & Industrial Engineering, 54, 140-154.
10- AL-Fawzan M.A. and Haouari M. (2004). A Bi-Objective Model for Robust Resource-Constrained Project Scheduling, International Journal of Production Economics, 18, 1-13.
11- Seifi M. and Tavakkoli-Moghaddam R. (2008). A new bi-objective model for a multi-mode resource constrained project scheduling problem with discount cash flows and four payment models, IJE Transactions A: Basics, 21, 347-355.
12-Pinedo M. (2001). Scheduling: Theory, Algorithms, and Systems, Prentice Hall, 2nd edition, Englewood Cliffs.
13- Tavagho M., Makuee A. (2012). Provide a method for determining the buffer size on the critical chain scheduling (Case Study: Company Soliran), Journal of Industrial Management (IM), 17, 1-22.
14- Goldratt E.M. (1997). Critical Chain, The North River Press Publishing Corporation, Great Barrington.
15- Herroelen W. and Leus R. (2001). On the merits and pitfalls of critical chain scheduling, Journal of Operations Management, 19, (5), 559–577.
16- Artigue C., Billaut J.C. and Esswein C. (2005). Maximization of solution flexibility for robust shop scheduling, European Journal of Operational Research, 165, (2), 314–328.
17-Aloulou M.A., Portmann M.C. and Vignier A. (2002). Predictive-reactive scheduling for the single machine problem, Proceedings of the 8th International Workshop on Project Management and Scheduling, 39–42.
18- Roy B. (2002). Robustesse de quoi et vis-à-vis de quoi mais aussi robustesse pourquoi en aide à la d ´cision? , Newsletter of the European Working group "Multicriteria Aid for Decisions", 3, (6), 1–6.
19- Tabrizi B., Ghaderi S.F. (2016). A robust bi-objective model for concurrent planning of project scheduling and material procurement, Computers & Industrial Engineering, In Press, Accepted Manuscript.
20- Sadeghi A., Safi A., Barzinpur F. (2012). Project scheduling problem solving with resource constraints multimode (MRCPSP) By Bees algorithm, Journal of Industrial Management (IM), 15, 2-4.
21-Zhang H., Li H., Huang F. (2005). Particle swarm optimization-based schemes for resource constraint project schedule Elsevier Automation in Construction, 14, 393-404.
22- Khaji M., Shoghaee R. (2014). Robust approach to supply network design with an emphasis on environmental uncertainty, Journal of Tomorrow management, 37, 171-188.
23- Bhaskar, T., Manabendra N., Asim K. (2011). A heuristic method for RCPSP with fuzzy activity time, European Journal of Operational Research, 208, 57-66.
24- Issi H., Bazgan C. (2007). Min-max and min-max regret versions of some combinatorial optimization problems: a survey, Annales du Lamsade Paris, 1–32.
25- Devonder S.V., Ballestin F., Demeulemeester E., Herroelen, W. (2010). Heuristic procedures for reactive project scheduling, Computer & Industrial Engineering, 52, 11-28.
26- Ke H., Liu B. (2010). Project scheduling problem with mixed uncertainty of randomness and fuzziness, European Journal of operational Research, 83, 135-147.
_||_
1- Damghani, k., Tavakoli moghadam, R., & Tabari, M. (2011). Solving the problems of Scheduling projects with constraint resources by improved ant algorithm, Industrial Engineering, 45 (1), 59 – 69.
2- Aliahmadi A., Jafari M., Mortaji T., Nozari H. (2013). Improve prediction of Project completion time Based Earned Value Management System, Journal of Tomorrow management, 37, 5-19.
3- Basaghzade I., Hejazi S., Amirmosa E. (2010). Development of project scheduling model with objectives of completion time and rebostness of scheduling, Industrial Engineering, 44 (1), 13 -24.
4- Wang C., Bingqi J., Li G., Zhe T.,Jide N., Siwei L. (2016). Robust scheduling of building energy system under uncertainty, Journal of Applied Energy, 167, 366-376.
5- Ke H., Wang L., Huang H. (2015). An uncertain model for RCPSP with solution robustness focusing on logistics project schedule, International Journal of e-Navigation and Maritime Economy, 3, 71-83.
6- Angela H. and Chiuh-Cheng C. (2008). A Memetic Algorithm for Maximizing Net Present Value in Resource Constrained Project Scheduling Problem, IEEE Congress on Evolutionary Computation, 2041-2049.
7- Wei1 S. (2013), Project Scheduling Under Resource Constraints: A Recent Survey, International Journal of Engineering Research & Technology (IJERT), 2, 1-20.
8- Herroelen W., and Demeulemeester E., and De Reyck, B. (1999). A Classification Scheme for Project Scheduling (Project Scheduling-Recent Models, Algorithms and Application), Boston-Kluwer Academic.
9- Vanhoucke M., Debles D. (2011). The impact of various activity assumption on the lead time and resource utilization of resource-constrained project, Computer & Industrial Engineering, 54, 140-154.
10- AL-Fawzan M.A. and Haouari M. (2004). A Bi-Objective Model for Robust Resource-Constrained Project Scheduling, International Journal of Production Economics, 18, 1-13.
11- Seifi M. and Tavakkoli-Moghaddam R. (2008). A new bi-objective model for a multi-mode resource constrained project scheduling problem with discount cash flows and four payment models, IJE Transactions A: Basics, 21, 347-355.
12-Pinedo M. (2001). Scheduling: Theory, Algorithms, and Systems, Prentice Hall, 2nd edition, Englewood Cliffs.
13- Tavagho M., Makuee A. (2012). Provide a method for determining the buffer size on the critical chain scheduling (Case Study: Company Soliran), Journal of Industrial Management (IM), 17, 1-22.
14- Goldratt E.M. (1997). Critical Chain, The North River Press Publishing Corporation, Great Barrington.
15- Herroelen W. and Leus R. (2001). On the merits and pitfalls of critical chain scheduling, Journal of Operations Management, 19, (5), 559–577.
16- Artigue C., Billaut J.C. and Esswein C. (2005). Maximization of solution flexibility for robust shop scheduling, European Journal of Operational Research, 165, (2), 314–328.
17-Aloulou M.A., Portmann M.C. and Vignier A. (2002). Predictive-reactive scheduling for the single machine problem, Proceedings of the 8th International Workshop on Project Management and Scheduling, 39–42.
18- Roy B. (2002). Robustesse de quoi et vis-à-vis de quoi mais aussi robustesse pourquoi en aide à la d ´cision? , Newsletter of the European Working group "Multicriteria Aid for Decisions", 3, (6), 1–6.
19- Tabrizi B., Ghaderi S.F. (2016). A robust bi-objective model for concurrent planning of project scheduling and material procurement, Computers & Industrial Engineering, In Press, Accepted Manuscript.
20- Sadeghi A., Safi A., Barzinpur F. (2012). Project scheduling problem solving with resource constraints multimode (MRCPSP) By Bees algorithm, Journal of Industrial Management (IM), 15, 2-4.
21-Zhang H., Li H., Huang F. (2005). Particle swarm optimization-based schemes for resource constraint project schedule Elsevier Automation in Construction, 14, 393-404.
22- Khaji M., Shoghaee R. (2014). Robust approach to supply network design with an emphasis on environmental uncertainty, Journal of Tomorrow management, 37, 171-188.
23- Bhaskar, T., Manabendra N., Asim K. (2011). A heuristic method for RCPSP with fuzzy activity time, European Journal of Operational Research, 208, 57-66.
24- Issi H., Bazgan C. (2007). Min-max and min-max regret versions of some combinatorial optimization problems: a survey, Annales du Lamsade Paris, 1–32.
25- Devonder S.V., Ballestin F., Demeulemeester E., Herroelen, W. (2010). Heuristic procedures for reactive project scheduling, Computer & Industrial Engineering, 52, 11-28.
26- Ke H., Liu B. (2010). Project scheduling problem with mixed uncertainty of randomness and fuzziness, European Journal of operational Research, 83, 135-147.