Conference Agenda

To ensure a predictable and transparent delivery planning, companies define delivery profiles that provide a fixed delivery frequency for each supplier and facilitate planning for partners in the supply chain. The final order quan-tities are consolidated within a fixed timeframe before each delivery date, al-lowing for realization of bundling effects. Ideally, an integrated approach should be taken, where the transportation costs of each supplier are mini-mized, and the delivery days among all suppliers are coordinated to prevent overload at the receiving end. This paper presents a two-stage approach for such an integrated determination of delivery days. In the first stage, an ex-tension of a lot-sizing model is performed to incorporate fixed delivery fre-quencies. The first-stage model calculates cost-optimized supplier-specific delivery frequencies. In the second stage, a model is defined to orchestrate the delivery dates of all suppliers within their optimal delivery frequencies, ensuring a balanced delivery at the receiving end. Both models are solved us-ing an open-source solver. The optimal delivery days of the proposed ap-proach are evaluated compared to the currently implemented delivery pro-files through an industry case study. The results are analyzed in terms of monetary savings and optimization time to demonstrate the effectiveness of our approach.

The realm of logistics optimization is constantly evolving to reflect the intricacies of real-world scenarios. This research delves into the progression from the traditional Vehicle Routing Problem (VRP), focused solely on delivery routes, to the Pickup and Delivery Problem (PDP). The PDP extends this framework by incorporating both pickup and delivery tasks, mirroring the complexities of modern logistics, particularly in sectors like e-commerce where both collecting and delivering goods are crucial steps in the fulfillment process. PDPs hold immense significance for numerous industries. Efficient management of pick-up and delivery operations directly impacts cost reduction and customer satisfaction. This study takes a deep dive into a multi-objective optimization approach for PDPs, acknowledging the added complexities introduced by factors like alternative pick-up locations and time window constraints. Unlike traditional approaches, our methodology aims to optimize for multiple objectives simultaneously. By minimizing both delivery lateness and overall transportation costs, we strive to create a more efficient and adaptable logistics framework. Through this investigation, we aim to develop innovative strategies that enhance the effectiveness of logistics operations. Our findings hold the potential to contribute to advancements in supply chain management and transportation optimization, ultimately leading to a more streamlined and cost-effective logistics landscape.