A Hybrid Differential Evolution Algorithm Supported By Simulated Annealing For Integrated Inventory And Delivery Problem With Trade Credit

It is an important way to gain a competitive advantage for the contemporary enterprise to participate in global sourcing. Due to the constraint of the products, technology, regulatory and other aspects, contemporary enterprises have to select the suppliers with advanced technology, reasonable price and fast service in the process of global sourcing around the world. But in practice, every global sourcing consumes enormous fixed costs. In this case, the joint replenishment policy shows a huge advantage in terms of reducing costs. In this thesis, a new and practical joint replenishment and delivery model is developed and effective intelligent algorithms are designed to solve this problem.In practice, suppliers often provide retailers with forward financing in order to increase demand or decrease inventories and use trade credit as a greater source of capital. This thesis firstly proposes a practical joint multi-item replenishment and delivery problem by considering the trade credit and design effective intelligent algorithms to solve this problem and obtain the optimal replenishment policy. Then, a practical joint multi-item replenishment and delivery problem is proposed by considering the trade credit.We find the replenishment and delivery frequencies of items to minimize total system cost. Three intelligent algorithms, namely, HDE-SA, differential evolution algorithm(DE) and genetic algorithm(GA), are designed to solve this problem. Results of contrastive numeric examples show that the HDE-SA outperforms GA and DE, which have been proven to be effective algorithms. The effectiveness of HDE-SA is further verified by randomly generated problems. The findings show that HDE-SA is more stable and robust in handling this complex problem. A sensitivity analysis is conducted to discuss the impact of the changing parameters, and useful management implications are provided.