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

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

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

Synchromodal Transport Planning at a Logistics Service Provider

In this chapter, we consider synchromodal planning of transport orders with the objective to minimize costs, delays, and CO2 emissions. Synchromodal planning is a form of multimodal planning in which the best possible combination of transport modes is selected for every transport order. The underlying problem is known as the multi-objective k-shortest path problem, in which we search for the k-shortest paths through a multimodal network, taking into account time-windows of orders, schedules for trains and barges, and closing times of hubs. We present a synchromodal planning algorithm that is implemented at a 4PL service provider located in the Netherlands. We illustrate our approach using simulation with order and network data from this logistics service provider. On the corridor from the Netherlands to Italy, an average cost reduction of 10.1 % and a CO2 reduction of 14.2 % can be achieved with synchromodal planning