Sustainable Distribution Of Metal Printing Powder With Customer’s Preferred Time Windows And A Capacitated Heterogeneous Fleet

With recent advancements in manufacturing technology and the social economy, demand for additive manufacturing (AM) based raw materials is increasing. From a distributer’s perspective, this increases the demand for raw materials such as metal printing powder and calls for robust logistics that can deliver the product efficiently while maintaining product quality. This, in turn, can lead to higher energy consumption and costs due to dehumidification and high carbon emissions. However, the existence of various environmental and social challenges has led to distributers adopting more sustainable supply chain designs. Since sustainability and pollution have become an important focus for industry, strategies that help reduce fuel consumption and carbon emissions to realize green distribution are desired. Hence, when designing logistics networks, distributers must consider various aspects of the product and distribution such as: 1. Overall operations and transportation costs associated with AM raw materials. 2. Greenhouse gas emissions due to transportation of AM raw materials. 3. Delivery time constraints (e.g., time windows) provided by/to the customer.

We propose a multi-objective model to help deal with the above challenges, including a sustainable vehicle routing algorithm. To minimize the distribution costs for metal printing powder and to help achieve the goal of sustainable logistics simultaneously, a genetic algorithm-based vehicle routing algorithm is proposed. The proposed model can provide a reliable last-mile delivery plan for metal powder distributers with multiple types of delivery vehicles (heterogeneous fleet). To take the aspect of sustainability into consideration, dehumidification and emission costs are introduced. The algorithm aims to minimize the total costs incurred from distribution, dehumidification, fuel consumption, carbon emissions, and overtime. From a sustainability viewpoint, the results of the study provide a robust method for AM raw material distributers to arrange last-mile delivery that is socially responsible.

Advisor: Dr. Paul Griffin, Professor, Department of Industrial and Manufacturing Engineering