An Approximate Dynamic Programming Approach to Urban Freight Distribution with Batch Arrivals

We study an extension of the delivery dispatching problem (DDP) with time windows, applied on LTL orders arriving at an urban consolidation center. Order properties (e.g., destination, size, dispatch window) may be highly varying, and directly distributing an incoming order batch may yield high costs. Instead, the hub operator may wait to consolidate with future arrivals. A consolidation policy is required to decide which orders to ship and which orders to hold. We model the dispatching problem as a Markov decision problem. Dynamic Programming (DP) is applied to solve toy-sized instances to optimality. For larger instances, we propose an Approximate Dynamic Programming (ADP) approach. Through numerical experiments, we show that ADP closely approximates the optimal values for small instances, and outperforms two myopic benchmark policies for larger instances. We contribute to literature by (i) formulating a DDP with dispatch windows and (ii) proposing an approach to solve this DDP

Anticipatory freight selection in intermodal long-haul round-trips

We consider the planning problem faced by Logistic Service Providers (LSPs) transporting freights periodically, using long-haul round-trips. In each round-trip, freights are delivered and picked up at different locations within one region. Freights have time-windows and become known gradually over time. Using probabilistic knowledge about future freights, the LSP’s objective is to minimize costs over a multi-period horizon. We propose a look-ahead planning method using Approximate Dynamic Programming. Experiments show that our approach reduces costs up to 25.5% compared to a single-period optimization approach. We provide managerial insights for several intermodal long-haul round-trips settings and provide directions for further research

Long-haul transportation of perishable products with transshipment and asset management issues

In this paper, we present an optimization model for a transportation planning problem with multiple transportation modes, highly perishable products, demand and supply dynamics, and management of the reusable transport units (RTIs). Such a problem arises in the European horticultural chain, for example. As a result of geographic dispersion of production and market, a reliable transportation solutions ensures long-term success in the European market. The model is an extension to the network flow problem. We integrate dynamic allocation, flow, and repositioning of the RTIs in order to find the trade-off between quality requirements and operational considerations and costs. We also present detailed computational results and analysis. The study in this article has been supported by the Dinalog R&D project DaVinc3i with the reference number 2010.2.034R

A metaheuristic for the multimodal network flow problem with product quality preservation and empty repositioning

We study a transportation planning problem with multiple transportation modes, perishable products, and management of Reusable Transport Items (RTIs). This problem is inspired by the European horticultural chain. We present a Mixed Integer Programming (MIP) optimization model which is an extension of the Fixed-charge Capacitated Multicommodity Network Flow Problem (FCMNFP). The MIP integrates dynamic allocation, flow, and repositioning of the RTIs in order to find the trade-off between product freshness requirements, and operational circumstances and costs. We furthermore propose an Adaptive Large Neighborhood Search (ALNS) algorithm with new neighborhoods, and intensification and diversification strategies. We then provide detailed computational analysis on its properties, compare its results with a state-of-the-art MIP solver, and provide practical insights

Cooperative Relations Among Intermodal hubs and Transport Providers at Freight Networks Using an MPC Approach

Trabalho apresentado em International Conference on Computational Logistics (ICCL’15), 2015,Delft, HolandaFreight networks are more exposed to unforeseen events leading to delays compromising the delivery of cargo on time. Cooperation among different parties present at freight networks are required to accommodate the occurrence of delays. Cargo assignment to the available transport capacity at the terminal is addressed using a Model Predictive approach in this paper, taking into consideration the final destination and the remaining time until due time of cargo. A cooperative framework for transport providers and intermodal hubs is proposed in this paper. The cooperation is based on information exchange regarding the amount of cargo at risk of not reaching the destination on time. The terminal searches for a faster connection at the terminal to allocate the cargo at risk such that the final destination is reached on time. The proposed heuristic is a step towards sustainable and synchromodal transportation networks. Simulation experiments illustrate the validity of these statements.info:eu-repo/semantics/publishedVersio

Carrier selection for multi-commodity flow optimization in cooperative environments

Part 18: Optimization in Collaborative NetworksInternational audienceFreight transportation decisions are critical economic and environmental factors in the design and management of networked manufacturing systems at global scale. Multimodal transportation options in combination with cooperative models between transport operators and together with manufacturers can contribute to define more economically and environmentally sustainable operations. This work addresses the problem of the selection of carriers in an international production and distribution network. The aim is to minimize costs and environmental impacts of freight transport. A cooperative decision-making setting between carriers in response to transportation demand of manufacturers is adopted. An integrated optimization-simulation approach is proposed to model the process of defining the optimal combination of transportation services in a multimodal transport network. Experiments show that collaboration based on shared modal capacity between carriers can produce transport cost reduction and service level improvements

Learning-Based Co-planning for Improved Container, Barge and Truck Routing

When barges are scheduled before the demand for container transport is known, the scheduled departures may match poorly with the realised demands’ due dates and with the truck utilization. Synchromodal transport enables simultaneous planning of container, truck and barge routes at the operational level. Often these decisions are taken by multiple stakeholders who wants cooperation, but are reluctant to share information. We propose a novel co-planning framework, called departure learning, where a barge operator learns what departure times perform better based on indications from the other operator. The framework is suitable for real time implementation and thus handles uncertainties by replanning. Simulated experiment results show that co-planning has a big impact on vehicle utilization and that departure learning is a promising tool for co-planning.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic