Markdown Pricing of Perishable Products with Demand Dependence on Inventory Level and Selling Price

Subject Areas : Industrial Management

Ameneh Jeihouni ¹ , Hossein Safari ² , Ameneh Jeihouni ³ , Mohammad Reza Sadeghi Moghadam ⁴ , Farzad Bahrami ⁵

1 - PhD candidate of Industrial Management, Kish Campus, University of Tehran, Tehran
2 - Professor of Department of Industrial Management, Faculty of Management, University of Tehran, Tehran
3 - Associate Professor, Faculty of Industrial Engineering, Sharif University of Technology, Tehran
4 - Associate Professor, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran
5 - Assistant Professor, Department of Industrial Management, Faculty of Administrative and Economic Sciences, Arak University, Arak

Received: 2021-01-25 Accepted : 2021-05-18 Published : 2021-06-25

Keywords: Genetic Algorithm. Simulated Annealing Algorithm, :Perishable Product, Markdown Pricing,

Abstract :

In this article,A markdown pricing strategy has been used to achieve the maximum profit from the sale of perishable products. Because perishable products after a period of time and corruption is formed, so they are not attractive to the customers and the demand for these products decreases. Demand can be increased with a markdown pricing strategy. Also, as sales increase, waste will decrease and so, wastage cost will diminish .Therefore, total profit will increase. The problem is that if the discount is not provided at the right time and in the optimal amount, we will not reach the goal of achieving maximum profit. So, in this article, we seek to determine the optimal discount time and the optimal discount amount with the aim of maximizing the total profit of the firm. In this article, the demand function depends on two factors: the selling price and the displayed stocks, final inventory level is non-zero and the deterioration rate is fixed. After solving the model from the exact approach and numerical example, the example was solved using Genetic Algorithm, Simulated Annealing Algorithm and the results were compared, then sensitivity analysis of the key system parameters is carried out.

References:

1. Ahmadi, E., Masel, D. T., Hostetler, S., Maihami, R., Ghalehkhondabi, I. (2020). A centralized stochastic inventory control model for perishable products considering age-dependent purchase price and lead time, TOP, 28: 231-269.


2. Bai, R., Kendall, G., (2008). A Model for Fresh Produce Shelf Space Allocation and Inventory Management with Freshness Condition Dependent Demand, Informs Journal on Computing, 20(1): 78-85.


3. Bakker, M., Riezebos, J., Teunter, R. (2012). Review of inventory systems with deterioration since 2001,European Journal of Operational Research, 221(2): 275-284.


4. Bandalouski, A.M., Kovalov, M.Y., Pesch, E., Tarim, S.A. (2018).An Overview of Revenue Management and Dynamic Pricing  Models in hotel business, RAIRO Operations Research,52:119-143.


5. Bandalouski, A.M. (2015).Revenue Management Models for hotel business, Doctoral Thesis, Fakultät III-Wirtschaftswissenschaften, Wirtschaftsinformatik und Wirtschaftsrecht, https://dspace.ub.uni-siegen.de/handle/ubsi/923.


6. Bhunia, A.K., Shaikh, A.A. (2014). A deterministic inventory model for deteriorating items with selling price dependent demand and three-parameter Weibull distributed deterioration, International Journal of Industrial Engineering Computations, 5(3):497-510.


7. Bitran, G., Caldentey, R. (2003). An Overview of Pricing Models for Revenue Management, Journal of Manufacturing & Service Operations Management, 5(3):203-229.


8. Chen, S.C., Min, J., Teng, J. T., Li, F. (2016). Inventory and shelf-space optimization for fresh produce with expiration date under freshness-and-stock-dependent demand rate,
   Journal of the Operational Research Society, 67(6): 884-896.


9. Cohen, M.A. (1977). Joint pricing and ordering policy for exponentially decaying inventories with known demand, Naval Research Logistics Quarterly, 24 (2): 257-268.


10. Covert, R.P., Philip, G.C. (1973). An EOQ model for items with Weibull distribution deterioration, AIIE Transactions, 5: 323-326.


11. Duan, Y., Liu, J. (2018). Optimal dynamic pricing for perishable foods with quality and quantity deteriorating simultaneously under reference price effects, International Journal of Systems Science: Operations & Logistics, 2330-2682.


12. Dye,C.Y.,(2020) Optimal joint dynamic pricing, advertising and inventory control model for perishable items with psychic stock effect, European Journal of Operational Research, 283(2): 576-587.


13. Feng, L., Chan, Y. L., Barron, L.E.C. (2017). Pricing  and lot-sizing policies for perishable goods when the demand depends on selling price, displayed stocks ,and expiration date, International Journal of  Production Economic: Manufacturing System, Strategy & Design, 185:11-20.


14. Feng, L., Zhang, J., Tang, W. (2015). Dynamic joint pricing and production policy for perishable products, Journal of International Transactions in Operational Research, 25(6): 2031-2051.


15. Ghare, P.M., Schrader, G.F. (1963). A model for exponentially Decaying Inventory, Journal of Industrial Engineering, 14:238-243.


16. Hsieh, T.P., Dye, C.Y. (2017). Optimal dynamic pricing for deteriorating items with reference price effects when inventories stimulate demand, European Journal of Operational Research, 262(1): 136-150.


17. Jia, J., Hu, Q. (2011). Dynamic ordering and pricing for a perishable goods supply chain, Computers & Industrial Engineering Journal, (60): 302–309.


18. Jolai,F., Davoodi, S.M.R., Mohaghar,A., Mehregan, M.R. (2015), Developing an Optimization Algorithm for Multi-product and Multi-level Inventory Systems with Random Parameters, Journal of Industrial Management Islamic Azad University, Sanandaj Branch.9(30):71-84.


19. Kaya, O., Polat, A.L. (2017). Coordinated pricing and inventory decisions for perishable products, OR Spectrum 39: 589-606.


20. Khakzar, M., Zabihi, F. (2019). Pricing and Determining the Optimal Discount of Perishable Goods to Speed up Demand Rate, Production and Operations Management, 9(17)179-193.


21. Leventhal, B., Breur, T., (2012), Intelligent markdown pricing,Journal of Direct, Data and Digital Marketing Practice13, 207 – 220.


22. Li, R., Teng, J.T. (2018). Pricing and lot-sizing decisions for perishable goods when demand depends on selling price, reference price, product freshness, and displayed stocks, European Journal of Operational Research ,270, 1099-1108.


23. Li, R., Lan, H., Mawhinney, J.R., A. (2010). Review on Deteriorating Inventory Study, Journal of Service Science and Management, 3:117-129.


24. Lou, Z., Hou, F., Lou, X. (2020), Optimal Dual-Channel Dynamic Pricing of Perishable Items under Different Attenuation Coefficients of Demands, Journal of Systems Science and Systems Engineering, 0(0):1-15.
    
    
    
    1. Maihami, R., and Nakhai K., I. (2012). Joint pricing and inventory control for non-instantaneous deteriorating items with partial backlogging and time and price dependent demand”, International Journal of Production Economics, 136(1): 116–122.
    
    
    2. Nahmias, S., (1982). Perishable Inventory Theory: A Review, Operations Research, 30(4): 680-708.
    
    
    3. Nahmias, S. (2011). Perishable Inventory Systems, Santa Clara University, California, Springer.
    
    
    4. Philip, G.C. (1974). A generalized EOQ model for items with Weibull distribution deterioration, AIIE Transaction, (6):159-162.
    
    
    5. Qin, Y., Wang, J., Wei, C. (2014). Joint pricing and inventory control for fresh produce and foods with quality and physical quantity deteriorating simultaneously, Int. J. Production Economics, 152: 42-48.
    
    
    6. Raafat, F. (1991).Survey of Literature on Continuously Deteriorating Inventory Models, Palgrave Macmillan Journals on behalf of the Operational Research Society, 42(1). 27-37.
    
    
    7. Rabbani, M., Pourmohammad Zia, N., Rafiei, H. (2016). Joint optimal dynamic pricing and replenishment policies for items with simultaneous quality and physical quantity deterioration, Applied Mathematics and Computation, 287-288 (2016) 149–160.
    
    
    8. Rajan, A., Steinberg, R.  Steinberg, R. (1992), Dynamic Pricing and Ordering Decisions by a Monopolist, INFORMS, 38(2): 240 - 262.
    
    
    9. Sadeghi Moghadam, M.R., Nakhai, K. I., Karbasian, B. (2019). Joint pricing and inventory control modelling for obsolescent products: a case study of the telecom industry, International Journal of Applied Decision Sciences, 12(4): 375-401.
    
    
    10. Tashakkor, N., Mirmohammadi, S.M., Iranpoor, M. (2018). Joint optimization of dynamic pricing and replenishment cycle considering variable non-instantaneous deterioration and stock-dependent demand, Computers & Industrial Engineering, 123:232-241.
    
    
    11. Tat, R., Esmaeili, M., Taleizadeh, A. (2015). Developing EOQ Model with Instantaneous Deteriorating Items for A Vendor-Managed Inventory (VMI) System, Journal of Industrial and Systems Engineering, 8:(1), 85-102.
    
    
    12. Tat, R., Taleizadeh, A. Esmaeili, M. (2013). Developing Economic Order Quantity Model for Non-instantaneous Deteriorating Items in Vendor Managed Inventory (VMI) System, International Journal of Systems Science: 44 (45), 1-11.
    
    
    13. Akbarzade, M., Taleizadeh, A. (2016). Esmaeili, M., Developing Economic Production Quantity Model with Scrap Rework and Backordering under Vendor Managed Inventory policy, International Journal of Advanced Logistics, 5 :(4), 125-140.
    
    
    14. Akbarzadeh, M. Esmaeili, M., Taleizadeh, A. (2014). EPQ model with scrap and backordering under Vendor managed inventory policy, Journal of Industrial and Systems Engineering: 8(1), 21-42.
    
    
    15. Van Zyl, G.J.J. (1964). Inventory control for perishable commodities. Unpublished Ph.D. dissertation, university of North Carolina, Chapel Hill, N.C.
    
    
    16. Veinott, A.F. Jr. (1960). Optimal ordering issuing and disposal of inventory with known demand. Unpublished doctoral dissertation, Colombia University, New York, NY.
    
    
    17. Wee, H.M. (1999). Deteriorating inventory model with quantity discount, pricing and partial backordering, International Journal of Production Economics, 59(1-3):511–518.
    
    
    18. Wee, H. M., Law, S .T. (1999). Economic production lot size for deteriorating items taking account of the time-value of money, Journal of Computers & Operations Research, (26):545–558.
    
    
    19. Wu, J., Chang, C.T., Cheng, M.C., Teng, J.T., Al-khateeb, F.B. (2016).Inventory management for fresh produce when the time-varying demand depends on product freshness, stock level and expiration date. International Journal of Systems Science: Operations & Logistics, 3,138-147.
    
    
    20. Yavari,M.,Zaker,H.,Mozneb Emamzadeh,E.S. (2019). Joint Dynamic Pricing and Inventory Control for Perishable Products Taking into Account Partial Backlogging and Inflation, International Journal of Applied and Computational Mathematics,5(1).
    
    
    21. Zhang, J., Wang, Y., Lu, L., Tang, W. (2015). Optimal dynamic pricing and replenishment policy for non -instantaneous deterioration items with inventory-level-dependent demand, International Journal of Production Economic, (170A):136-145.