Application of POM-QM for Windows and Multiple ARC Network Model for Scheduling in a Single-Stage, Multi-Item Compatible Process


Published: 2023-09-20

Page: 523-538

Bokkasam Sasidhar *

College of Business Administration, King Saud University, Riyadh, Kingdom of Saudi Arabia.

*Author to whom correspondence should be addressed.


The importance of scheduling in the industrial world is growing rapidly. Customer-driven production scheduling is the need of the hour in the present business environment. The criteria generally considered are to maximize production volume while keeping in view the customers’ requirements and not to lose focus on maximizing profits as well as maximizing machine utilization. In order to retain major customers, the customers are categorized as either priority customers or normal customers. The production planning takes into account the customers’ orders, keeping in mind the nature of customers, viz., priority or normal. A production environment involving a given set of machines in a single-stage, multi-item compatible process is considered. The problem of scheduling in such an environment, with the objective of maximizing capacity utilization has been formulated [1] as a maximal flow problem in a Multiple Arc Network (MAN). The model generates an optimal production schedule with the goal of maximizing capacity utilization, ensuring that customer-wise delivery schedules are met while keeping customer priorities in mind. Implementation of the MAN System modeling has been built using POM-QM software for Windows V5. The application of the software is demonstrated using two examples. The QM software can be used by managers for any scaled-up operations to obtain production schedules.

Keywords: Scheduling, maximal flow problem, multiple arc network model, optimization, POM-QM software

How to Cite

Sasidhar , B. (2023). Application of POM-QM for Windows and Multiple ARC Network Model for Scheduling in a Single-Stage, Multi-Item Compatible Process. Asian Journal of Advances in Research, 6(1), 523–538. Retrieved from


Download data is not yet available.


Bokkasam Sasidhar. Multiple arc network model for scheduling in a single-stage, multi-item compatible process. International Review of Management and Business Research. 2016;5(3): 1223-1231.

Pinto JM, Grossmann IE. Assignment & sequencing models for the scheduling of process systems. Ann. Oper. Res. 1998:81:433–466.

Kallrath J. Planning and scheduling in the process industry. OR Spectrum. 2002; 24:219–250.

Floudas CA, Lin X. Continuous-time versus discrete-time approaches for scheduling of chemical processes: A review. Comput. Chem. Eng. 2004;28:2109–2129.

Burkard RE, Hatzl J. Review extensions and computational comparison of milp formulations for scheduling of batch processes. Comput. Chem. Eng. 2005; 29:1752–1769.

Mendez CA, Cerda J, Grossmann IE, Harjunkoski I, Fahl M. State of-the-art review of optimization methods for short-term scheduling of batch processes. Comput. Chem. Eng. 2006;30:913–946.

Pan M, Li X, Qian Y. Continuous-time approaches for short-term scheduling of network batch processes: Small-scale and medium-scale problems. Chem. Eng. Res. Des. 2009;87:1037–1058.

Gunter Schmidt. A decision support system for production scheduling. Journal of Decision Systems. 1992;1(2-3):243-260.

Gunter Schmidt. Modelling Production Scheduling Systems. International Journal of Production Economics. 1996;46-47: 109-118.

Achmad Azhar Cholil, Sugiyono Madelan. Optimization of production plastic sacks using POM-QM application for windows (Case Study: PT Rajawali Tanjungsari Engineering). International Research Journal of Innovations in Engineering and Technology. (IRJIET). 2022;6(8):15-24.

Matheus Supriyanto Rumetna, Tirsa Ninia Lina, Titin Puspita Sari, Piton Mugu, Adrianus Assem, Richard Sianturi. Optimasi jumlah produksi roti menggunakan program linear dan softwarePOM-QM. Computer Based Information System Journal. 2021;9(1): 42-49.

Imam Purwanto, Makmun. Implementation system of simplex method for optimization profit. International Journal Science Technology (IJST). 2023;2(2):53-60.

Gloria Fenny Delavina Simanjuntak, Hani Saffanah Putri, Intan Putri Maharani Sinaga, Nastiti Adibestari, Ardhy Lazuardy. Aggregate planning to minimize cost of production of abc company with forecasting and master production schedule approach. Proceedings of the 5th European International Conference on Industrial Engineering and Operations Management Rome, Italy. 2022; 1173-1183.

Junaiddin A, Syaifuddin DT, Montundu Y, Zaid S. The application of linear programming into production schedule at electrical panel company. International Journal of Membrane Science and Technology. 2023;10(3):354-371.

Effendi M, Tunjang H, Hidayat DR. Analysis of aggregate planning to streamline production cost in the mahakam ice crystal home industry in the city of palangka raya. Jurnal Manajemen Sains dan Organisasi. 2023;4(1):1-12.

Mijinyawa M, Modibbo UM, Fimba K. Optimal production scheduling for a manufacturing company: A case of Adama Beverages Ltd.(FARO), Adamawa State Nigeria. International Journal of Advanced Research in Science and Engineering (IJARSE). 2019;8(6):21-30.

Cipta H, Widyasari R. Goal programming model in tackling the optimal building material for production planning. Journal of Industrial Engineering and Management. 2023;1(1):1-6.

Jong CH, Medina N, Fakhriyah N, Hidayat C, Hamali S. Using goal programming method for optimization of production planning. In 2018 International Conference on Information Management and Technology (ICIMTech). 2018;155-159. IEEE.

Kurniawan S, Raphaeli SS. Optimizing production process through production planning and inventory management in motorcycle chains manufacturer. ComTech: Computer, Mathematics and Engineering Applications. 2018;9(2):43-50.

Handayani S. Optimization of organic rice production using linear programming analysis in lampung province. Asia Pacific Journal of Management and Education (APJME). 2022;5(3):37-47.

Ford LR, Fulkerson DR. Flows in networks. Princeton University Press. Princeton, NJ; 2010.

Sasidhar B, Achary KK. A multiple arc network model of production planning in a steel mill. International Journal of Production Economics. 1991;22:195-202.

Ibrahim A. Aljasser, Bokkasam Sasidhar. Scheduling in a single-stage, multi-item compatible process using multiple arc network model and excel solver. International Review of Management and Business Research. 2018;7(1):23-31.

Howard J Weiss. POM - QM FOR WINDOWS software for decision sciences: Quantitative methods, production and operations management. Pearson Education; 2016.

Gede Marendra I, Made Aryata I, IrmawanAfgani. POM QM for windows training for industrial engineering students at the university of serang raya (UNSERA) in solving linear programming problems in everyday life and the world of work. Jurnal Pengabdian MasyarakatBestari(JPMB). 2023;2(2):125-138.