Multi-item inventory policy with time-dependent pricing and rework cost


  • Laila Nafisah Department of Industrial Engineering, UPN “Veteran” Yogyakarta
  • Nabilla Clara Devi Maharani Department of Industrial Engineering, UPN “Veteran” Yogyakarta
  • Yuli Dwi Astanti Department of Industrial Engineering, UPN “Veteran” Yogyakarta
  • Muhammad Shodiq Abdul Khannan Department of Industrial Engineering, UPN “Veteran” Yogyakarta



Inventory model, Time dependent pricing, Multi item, Rework cost


The price of broiler chickens at the consumer level varies daily. The price can be very low or otherwise. The price has resulted from the imbalance between the availability of chicken from suppliers and the market demand. As a result, demand will also fluctuate because it is influenced by consumer purchasing power. When the price of live chickens is low, the carcass company will usually buy in large quantities and expect to sell them at a higher price. The problem arises when the chicken overstock company will risk product damage due to product buildup in the refrigerated warehouse, so rework is necessary. In this paper, we will be developed a multi-item inventory model that considers material prices that vary to time, probabilistic demand, and rework costs. The aim is to determine the right policy for controlling frozen chicken products' inventory to minimize losses and total inventory costs.  This model can evaluate the best time to order broiler chickens, how much to order, how long the interval between orders, and the optimal number of orders, resulting in minimum total inventory cost per period.  The model solution is carried out with an optimization approach based on the parameters that affect the model. A numerical example is given at the end of this paper for model validation and illustrates the model solving algorithm.


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How to Cite

Nafisah, L., Maharani, N. C. D., Astanti, Y. D., & Khannan, M. S. A. (2021). Multi-item inventory policy with time-dependent pricing and rework cost. International Journal of Industrial Optimization, 2(2), 99–112.