Optimization of Joint Economic Lot Size Model for Vendor-Buyer with Exponential Quality Degradation and Transportation by Chimp Optimization Algorithm

Freight transportation plays a critical role in improving company performance in the modern manufacturing industry. To reduce costs, companies must take advantage of the use of large vehicles. It caused fewer deliveries, but inventory costs and degradation quality are high. One of the joint economic lot size (JELS) problems in supply chain is Integrated Single-Vendor Single-Buyer Inventory Problem (I-SVSB-IP). This study developed the I-SVSB-IP model that considers raw materials' exponential quality degradation and transportation costs. The objective function of this research was to maximize the Joint Total Profit (JTP). Three decision variables used were inventory cycle time (T), raw material ordering frequency (m), and frequency of delivery of finished products to buyers (n).)is study proposed a sophisticated Chimp Optimization Algorithm (ChOA) procedure to solve the I-SVSB-IP problem. A case study on the food industry in Indonesia was presented to optimize the I-SVSB-IP. The results showed that the ChOA procedure had produced an optimal solution compared to the state-of-the-art algorithm. This study also demonstrated a sensitivity analysis of decision and transportation variables to cost, revenue, and JTP. The results show that increasing transport frequency of ordering raw materials (m) and finished products to buyers (n) enhances the total cost and reduces joint total profit. In addition, increasing the rate of quality degradation of raw materials reduces JTP.

Automated summary

Freight transportation plays a critical role in modern manufacturing industry by reducing costs through the use of large vehicles. This study explores the Joint Economic Lot Size problem in supply chain, developing a model that considers raw material quality degradation and transportation costs, and proposes a Chimp Optimization Algorithm to solve it.