Genetic Algorithm for Optimal Vendor Payment Schedule of Transportation Company

Authors

  • Farhana Johar Jabatan Sains Matematik, Fakulti Sains, Universiti Teknologi Malaysia, 81300 Johor Bahru, Johor, Malaysia
  • Ng Houy Fen Jabatan Sains Matematik, Fakulti Sains, Universiti Teknologi Malaysia, 81300 Johor Bahru, Johor, Malaysia
  • Syarifah Zyurina Nordin Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.11113/mjfas.v18n4.2451

Keywords:

Payment scheduling problem, net present value, transportation, genetic algorithm, cash flow analysis

Abstract

Nowadays, businesses in any sector are under pressure to do more with less. Company cannot afford to pay more and squander opportunities to free up their company’s cash, i.e. working capital. Good working capital gives greater availability to the cash trapped on your balance sheet which is beneficial to fund growth, reduce costs, enhance service levels and seize new investment opportunities. There are numerous ways to free up working capital, and one of the strategies is through account payable. Account payable are amounts due to vendor or supplier for goods or services received that have not yet been paid for. Thus, this paper focuses on the proper vendor payment schedule as one of the approaches to sustain the liquidity of business. Optimizing the vendor payment schedule could be observed through their Net Present Value (NPV). NPV is the difference between the present value of cash inflows and outflows. Therefore, our aim is to optimize the vendor payment schedule by maximizing their NPV. Genetic Algorithm (GA) is implemented in determining the optimal vendor payment schedule. Two GA parameters, which are generation number and population size, have been analyzed and optimized in order to meet the maximum NPV. The results show that the GA is efficient in maximizing the NPV of vendor payment schedule.

References

Thiago A., Luca S. and Maria V. (2021). “Working Capital Management and Profitability: Evidence from an Emergent Economy”. International Journal of Advances in Management and Economics, 10 (01), pp.32-39.

Sierpińska-Sawicz, A. (2021). “Liquidity measurement problems in mining companies”. Inżynieria Mineralna, pp.105-111,

Rahman, A. and Mangalagangothri, M. (2022). “Relationship Between Working Capital Management And Profitability Of Indian Automobile Manufacturers”. Journal Of Management & Entrepreneurship (ISSN : 2229-5348), Vol. 16, No.1 (II), pp.12-20.

Ahmed, A.Y. and Mwangi, L.W. (2022). “Working Capital Management and Financial Performance of Small and Medium Enterprises in Garissa County, Kenya”. International Journal of Current Aspects in Finance, Banking and Accounting, 4(1), pp.56-71.

Hutchison, P.D., Farris, M.T. and Anders, S.B. (2007). “Cash-to-cash analysis and management”. CPA journal, 77(8), p.42.

Tarun T., Sampath D., Senthil M., and Chandresh M. (2018). “Prediction of Invoice Payment Status in Account Payable Business Process”. International Conference on Service-Oriented Computing, pp.165-180.

Ellen M. and Bret W (2012). “Concepts in Enterprise Resource Planning,” Course Technology Cengage Learning, 4th Edition.

Tamer F. A. and Maged M. D. A. (2007). “Genetic Algorithm Approach to the Integrated Inventory-Distribution Problem”. International Journal of Production Research. 44(21): 4445-4464.

Liu S.S. and Kuo-Chuan S. (2009). “A Framework of Critical Resource Chain for Project Schedule Analysis”, Construction Management and Economics, 27(9), pp. 857-869.

Ismail M. B., Ahmed H. I. and Ahmed N. A. E. (2016). “Profitability Optimization of Construction Project Using Genetic Algorithm Cash Flow Model”. American Journal of Civil Engineering and Architecture, 4(1), pp. 17-27.

Mehdy M.G., Naser S.G. and Reza G.Y. (2017). “Techniques for Cash Management in Scheduling Manufacturing Operations”, Journal of Industrial Engineering International, 13(1), pp. 265–273.

Sulla, A., Slepchenko, D., Kuzmicheva, I. and Zaostrovskikh, E. (2021). “Financial Logistics and Its Application in Cash Flow Management. In VIII International Scientific and Practical Conference Current problems of social and labour relations (ISPC-CPSLR 2020), Atlantis Press, pp. 726-731.

Kantar, L. (2022). “Finance and Cost Management in the Process of Logistics 4.0”. In Logistics 4.0 and Future of Supply Chains. Springer, Singapore, pp. 215-234.

Prem K. G. and D S Hira. (1992). Operations Research. 7th ed. New Delhi: S. Chand Publishing.

Klimek, M. and Łebkowski, P. (2013). “Robustness of schedules for project scheduling problem with cash flow optimization”. Bulletin of the Polish Academy of Sciences: Technical Sciences, pp.1005-1015.

Chaharsooghi, S.K., Seyfi Sariqaya, M. and Rahimnezhad, F. (2019). “Optimization of project cash flow under uncertainty by genetic algorithm”. International Journal of Industrial Engineering and Management Science, 6(1), pp.24-37.

M. Vanhoucke. (2009). “A Genetic Algorithm for Net Present Value Maximization for Resource Constrained Projects”. In Cotta, Carlos, Cowling and Peter I. Evolutionary Computation in Combinatorial Optimization. Germany: Springer. 5482: 13-24.

R. Glenn Hubbard, Anne M. G., Philip L. and Anthony P. O'B. (2014). “Microeconomics”. 3rd ed. Australia: Pearson Australia.

S. Keoki Sears, Glenn A. S., Richard H. C., Jerald L. R. and Robert O. S. (2015). Construction Project Management. 6th ed. United States: John Wiley & Sons, Ltd.

F.J. Plewa, JR., G.T. Friedlob (1995). “Understanding Cash Flow”, John Wiley & Sons, Ltd.

IntroBooks (2017). “Cash Flow Analysis”. Independently Published.

Angelo C. (2018). “Analytical Corporate Finance”. 2nd ed. Switzerland: Springer Nature Switzerland.

Alexander, J. (2018). Financial Planning & Analysis and Business Performance Management. United States: John Wiley & Sons, Ltd.

Jiang W., Faisal B.A., Jinn-Tsair T. and Leopoldo E. C. (2016). “Inventory Models for Deteriorating Items With Maximum Lifetime under Downstream Partial Trade Credits to Credit-Risk Customers by Discounted Cash-Flow Analysis”. International Journal of Production Economics, 171(1), pp. 105-115.

Harvard Business Review Home. “A Refresher on Net Present Value”. Brighton, Massachusetts. 2014.

J. Weglarz (2012). “Project Scheduling: Recent Models, Algorithms and Applications”. Berlin, Germany: Springer Science & Business Media.

A. H. Russell (1970). “Cash Flows in Networks”, Management Science, 16(5), pp. 357-373.

Grinold, R.C. (1972). “The Payment Scheduling”, Naval Research, Logistics Quarterly, 19(1), pp. 123-136.

A. H. Russell (1986). “A Comparison of Heuristics for Scheduling Projects with Cash Flows and Resource Restrictions”, Management Science, 32(10), pp. 1291-1300.

Smith-Daniels, D.E., N.J. Aquilano (1987). “Using A Late Start Resource Constrained Project Schedule to Improve Project Net Present Value”, Decision Sciences, 18, pp. 617-630, 1987.

Baroum, S.M., J.H. Patterson (1999). “An Exact Solution Procedure for Maximizing the Net Present Value of a Project”, in Weglarz, J. (Ed.), Handbook on Recent Advances in Project Scheduling, Kluwer Academic Publishers, 107-134.

Y.Y Xiao and A. Konak (2017). “A Genetic Algorithm With Exact Dynamic Programming For the Green Vehicle Routing & Scheduling Problem”. Journal of Cleaner Production, 167, pp. 1450-1463.

Senthilkumar M., Nallakaruppan K., Chandra S. T. and Prasanna S. A. (2014). “Modified and Efficient Genetic Algorithm to Address a Travelling Salesman Problem”. International Journal of Applied Engineering Research, 9(10), pp. 1279-1288.

Katoch, S., Chauhan, S.S. and Kumar, V., (2021). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80(5), pp.8091-8126.

Chaharsooghi, S.K., Seyfi Sariqaya, M. and Rahimnezhad, F. (2019). “Optimization of project cash flow under uncertainty by genetic algorithm”. International Journal of Industrial Engineering and Management Science, 6(1), pp.24-37.

Asadujjaman, M., Rahman, H.F., Chakrabortty, R.K. and Ryan, M.J. (2021). “An immune genetic algorithm for solving NPV-based resource constrained project scheduling problem”. IEEE Access, 9, pp.26177-26195.

Asadujjaman, M., Rahman, H.F., Chakrabortty, R.K. and Ryan, M.J. (2022) “Multi-operator immune genetic algorithm for project scheduling with discounted cash flows”. Expert Systems with Applications, p.116589.

Downloads

Published

06-10-2022