Multi-period Integer Programming Model Research Articles

Dynamic Optimization of Emergency Logistics for Major Epidemic Considering Demand Urgency

In recent years, epidemic disasters broke through frequently around the world, posing a huge threat to economic and social development, as well as human health. A fair and accurate distribution of emergency supplies during an epidemic is vital for improving emergency rescue efficiency and reducing economic losses. However, traditional emergency material allocation models often focus on meeting the amount of materials requested, and ignore the differences in the importance of different emergency materials and the subjective urgency demand of the disaster victims. As a result, it is difficult for the system to fairly and reasonably match different scarce materials to the corresponding areas of greatest need. Consequently, this paper proposes a material shortage adjustment coefficient based on the entropy weight method, which includes indicators such as material consumption rate, material reproduction rate, durability, degree of danger to life, and degree of irreplaceability, to enlarge and narrow the actual shortage of material supply according to the demand urgency. Due to the fact that emergency materials are not dispatched in one go during epidemic periods, a multi-period integer programming model was established to minimize the adjusted total material shortage based on the above function. Taking the cases of Wuhan and Shanghai during the lockdown and static management period, the quantitative analysis based on material distribution reflected that the model established in this paper was effective in different scenarios where there were significant differences in the quantity and structure of material demand. At the same time, the model could significantly adjust the shortage of emergency materials with higher importance and improve the satisfaction rate.

Redeploying excess inventories with lateral and reverse transshipments

Service parts management has the potential to generate high profits for companies that deliver superior service parts services in the after-sale market. However, a big challenge in managing service parts operations is to meet the high expectations of service levels and to reduce excess inventories caused by fluctuating demand and a complex service parts logistics network structure. By expanding the conventional inventory management that passively focuses on the forward and lateral flows of service parts deployment, we propose a crucial but overlooked practice of inventory redeployment as an integral part of the operations that allow the proactive management of lateral and reverse flows of service parts. We formulate the service parts inventory problem with the application of an excess inventory redeployment strategy in a multi-echelon service network as a multi-period integer programming model. This optimisation model is evaluated using a case study of an international company’s service parts operations and demonstrates a higher cost-saving potential. Our novel, integrated approach confers the advantage of redeploying excess inventories in a closed-loop service parts logistics network with a higher cost-saving potential that could not have been achieved in a conventional approach.

Supply-chain network configuration for product recovery

The current growth in consumption results in resource reduction, increasing waste production and, ultimately, environmental deterioration. Both government and consumer concerns regarding these issues have been driving reduction efforts in waste and natural resource-usage. These environmental issues, in addition to economic opportunities, result in the concept of product cycles that oppose the traditional ‘one-way’ economy. One approach is to create product recovery networks in which used products are collected, reprocessed and later redistributed to the customer. Product recovery networks differ from typical forward-only networks. Therefore, specific work considering their issues is necessary. In this work, we study a closed-loop supply chain in which manufacturers produce new products and remanufacture used products. The decisions to be made are: which warehouses and collection centres should be open, which warehouses should have sorting capabilities and how much material should be transported between each pair of sites. The multi-period integer programming model uses the present worth method to jointly analyse investment and operational costs. A sensitivity analysis of the model is performed, and conclusions are made regarding model behaviour and performance.