An Application of Mixed-Integer Linear Programming Method in Production Planning of Pipe Industry

Document Type : Research Paper

Authors

1 Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran

2 Department of Industrial Engineering, Hacettepe University, Ankara, Turkey

10.22070/jqepo.2022.15940.1227

Abstract

Different types of pipes are used in various industries, and linear programming of pipe production is an effective subject in this industry. Especially, efficient planning in operational level leads to reduce total cost and improves competitiveness. In this paper, the mixed integer linear programming (MILP) approach is applied for operational planning of pipe production in different periods. To provide, moreover, a mathematical model for the manufacturing line, the model which is described in this study can choose the best supplier among a variety of suppliers. This model also optimizes the amount of raw materials acquired from suppliers as well as the amount of final product manufacturing, reducing final product and raw material inventory level in the factory. The final product inventory level, raw material inventory level, manufacturing capacity, supplier and warehouse capacity for keeping the final product and raw materials are also taken into account. The model is applied in a case study in GRP pipe production plants in Turkey.

Keywords

References

Beheshti-Fakher, H., Nourelfath, M., & Gendreau, M. (2016). Joint planning of production and maintenance in a single machine deteriorating system. IFAC-PapersOnLine, 49(12), 745-750.
Billal, M. M., & Hossain, M. (2020). Multi-Objective Optimization for Multi-Product Multi-Period Four Echelon Supply Chain Problems Under Uncertainty. Journal of Optimization in Industrial Engineering, 13(1), 1-17.
Dogan, M. E., & Grossmann, I. E. (2006). A decomposition method for the simultaneous planning and scheduling of single-stage continuous multiproduct plants. Industrial & engineering chemistry research, 45(1), 299-315.
Dong, S., & Medeiros, D. J. (2012). Minimising schedule cost via simulation optimisation: an application in pipe manufacturing. International journal of production research, 50(3), 831-841.
Feylizadeh, M. R., Karimi, N., & Li, D. F. (2018). Multi-stage production planning using fuzzy multi-objective programming with consideration of maintenance. Journal of Intelligent & Fuzzy Systems, 34(4), 2753-2769.
Gao, X., Xie, Y., Wang, S., Wu, M., Wang, Y., Tan, C., ... & Chen, T. (2020). Offshore oil production planning optimization: An MINLP model considering well operation and flow assurance. Computers & Chemical Engineering, 133, 106674.
Ghaderi, H., Gitinavard, H., & Mehralizadeh, M. (2020). An Intuitionistic Fuzzy DEA Cross-Efficiency Methodology with an Application to Production Group Decision-Making Problems. Journal of Quality Engineering and Production Optimization, 5(2), 69-86.
Ghosh, D., & Mondal, S. (2017). An integrated production-distribution planning with transshipment between warehouses. International Journal of Advanced Operations Management, 9(1), 23-36.
Gitinavard, H., Ghodsypour, S., & Akbarpour Shirazi, M. (2019). A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty. Scientia Iranica, 26(5), 2952-2970.
Gupta, S., Haq, A., Ali, I., & Sarkar, B. (2021). Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment. Complex & Intelligent Systems, 7(4), 2119-2139.
Iglesias-Escudero, M., Villanueva-Balsera, J., Ortega-Fernandez, F., & Rodriguez-Montequín, V. (2019). Planning and Scheduling with Uncertainty in the Steel Sector: A Review. Applied Sciences, 9(13), 2692.
Kabak, M., Javadi, H., Aktas, A., & Ecer, B. (2022). GIS-Based Multi-Criteria Decision Making for Site Selection: An Application of GRP Pipe Production Plant. Journal of Multiple-Valued Logic & Soft Computing, 38.
Keshmiry Zadeh, K., Harsej, F., Sadeghpour, M., & Molani Aghdam, M. (2021). A multi-objective multi-echelon closed-loop supply chain with disruption in the centers. Journal of Quality Engineering and Production Optimization, 6(2), 31-58.
Khadem, M., Toloie Eshlaghy, A., & Fathi Hafshejani, K. (2021). Decentralized Multi-Commodity and Multi-Period Mathematical Model for Disaster Relief Goods Location and Distribution using HACO-VNS Hybrid Algorithm. Journal of Quality Engineering and Production Optimization, 6(2), 157-180.
Khan, M. F., Haq, A., Ahmed, A., & Ali, I. (2021). Multiobjective multi-product production planning problem using intuitionistic and neutrosophic fuzzy programming. IEEE Access, 9, 37466-37486.
Kruijff, J. T., Hurkens, C. A., & de Kok, T. G. (2018). Integer programming models for mid-term production planning for high-tech low-volume supply chains. European Journal of Operational Research, 269(3), 984-997.
Lee, I. (2001). Artificial intelligence search methods for multi-machine two-stage scheduling with due date penalty, inventory, and machining costs. Computers & Operations Research, 28(9), 835-852.
Lei, D., Yuan, Y., Cai, J., & Bai, D. (2020). An imperialist competitive algorithm with memory for distributed unrelated parallel machines scheduling. International Journal of Production Research, 58(2), 597-614.
Li, M., Yang, F., Uzsoy, R., & Xu, J. (2016). A meta model-based Monte Carlo simulation approach for responsive production planning of manufacturing systems. Journal of a Manufacturing Systems, 38, 114-133.
Mehdizadeh, E., Niaki, S. T. A., & Hemati, M. (2018). A bi-objective aggregate production planning problem with learning effect and machine deterioration: Modeling and solution.Computers & Operations research, 91, 21-36.
Mosadegh, H., Khakbazan, E., Salmasnia, A., & Mokhtari, H. (2017). A fuzzy multi-objective goal programming model for solving an aggregate production planning problem with uncertainty. International Journal of Information and Decision Sciences, 9(2), 97-115.
Nyström, R. H., Harjunkoski, I., & Kroll, A. (2006). Production optimization for continuously operated processes with optimal operation and scheduling of multiple units. Computers & chemical engineering, 30(3), 392-406.
Ramezanian, R., Rahmani, D., & Barzinpour, F. (2012). An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search. Expert Systems with Applications, 39(1), 1256-1263.
Rosa, V. R., Camponogara, E., & Ferreira Filho, V. J. M. (2018). Design optimization of oilfield subsea infrastructures with manifold placement and pipeline layout. Computers & Chemical Engineering, 108, 163-178.
Sharifzadegan, M., Sohrabi, T., & Jafarnejad Chaghoshi, A. (2021). Hybrid optimization of production scheduling and maintenance using mathematical programming and NSGA-II meta-heuristic method. Journal of Quality Engineering and Production Optimization, 6(2), 79-96.
Sohn, M. S., Choi, J., Kang, H., & Choi, I. C. (2017). Multiobjective production planning at LG display. Interfaces, 47(4), 279-291.
Solgi, E., Gitinavard, H., & Tavakkoli-Moghaddam, R. (2021). Sustainable High-Tech Brick Production with Energy-Oriented Consumption: An Integrated Possibilistic Approach Based on Criteria Interdependencies. Sustainability, 14(1), 202.
Vakili, R., Akbarpour Shirazi, M., & Gitinavard, H. (2021). Multi-echelon green open-location-routing problem: A robust-based stochastic optimization approach. Scientia Iranica, 28(2), 985-1000.
Zhang, H., Liang, Y., Ma, J., Qian, C., & Yan, X. (2017). An MILP method for optimal offshore oilfield gathering system. Ocean Engineering, 141, 25-34. Korea, 2009, pp.23-29.
Journal of Quality Engineering and Production Optimization
Volume 7, Issue 1
May 2022
Pages 13-24

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