Offering time windows to receivers of last-mile delivery is becoming a distinguishing factor. However, we see that in practice carriers have to create routes for their vehicles based on destination information, that is just being revealed when a parcel arrives in the depot. The parcel has to be assigned directly to a vehicle, making this a Dynamic Assignment Vehicle Routing Problem. Incorporating time windows is hard in this case. In this paper an approach is presented to solve this problem including Time Windows. A comparison is made with a real observation and with a solution method for the base problem
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The two-dimensional vehicle routing problem (2L-VRP) is a realistic extension of the classical vehicle routing problem where customers’ demands are composed by sets of non-stackable items. Examples of such problems can be found in many real-life applications, e.g. furniture or industrial machinery transportation. Often, these real-life instances have to deal with uncertainty in many aspects of the problem, such as variable traveling times due to traffic conditions or customers availability. We present a hybrid simheuristic algorithm that combines biased-randomized routing and packing heuristics within a multi-start framework. Monte Carlo simulation is used to deal with uncertainty at different stages of the search process. With the goal of minimizing total expected cost, we use this methodology to solve a set of stochastic instances of the 2L-VRP with unrestricted oriented loading. Our results show that accounting for systems variability during the algorithm search yields more robust solutions with lower expected costs.
In this paper we present a modification to the Dynamic Assignment Vehicle Routing Problem. This problem arises in parcel to vehicle assignment where the destination of the parcels is not known up to the assignment of the parcel to a delivering route. The assignment has to be done immediately without the possibility of re-assignment afterwards. We extend the original problem with a generalisation of the definition of capacity, with an unknown workload, unknown number of parcels per day, and a generalisation of the objective function. This new problem is defined and various methods are proposed to come to an efficient solution method.
Logistics companies struggle to keep their supply chain cost-effective, reliable and sustainable, due to changing demand, increasing competition and growing service requirements. To remain competitive, processes must be efficient with low costs. Of the entire supply chain, the first and last mile logistics may be the most difficult aspect due to low volumes, high waiting and shipping times and complex schedules. These inefficiencies account for up to 40% of total transport costs. Connected Automated Transport (CAT) is a technological development that allows for safer, more efficient and cleaner transport, especially for the first- and last-mile. The Connected Automated Driving Roadmap (ERTRAC) states that CAT can revolutionize the way fleets operate. The CATALYST Project (NWO) already shows the advantages of CAT. SAVED builds on several projects and transforms the challenges and solutions that were identified on a strategic level to a tactical and operational (company) level. Despite the high-tech readiness of CAT, commercial acceptance is lacking due to issues regarding profitable integration into existing logistics processes and infrastructures. In-depth research on automated hub-to-hub freight transport is needed, focusing on ideal vehicle characteristics, logistic control of the vehicles (planning, routing, positioning, battery management), control modes (central, decentralized, hybrid), communication modes (vehicle-to-vehicle, vehicle-to-infrastructure) and automation of loading and unloading, followed by the translation of this knowledge into valid business models. Therefore, SAVED focuses on the following question: “How can automated and collaborative hub-to-hub transport be designed, and what is the impact in terms of People, Planet and Profit (PPP) on the logistics value chain of industrial estates of different sizes, layouts and different traffic situations (mixed/unmixed infrastructure)?“ SAVED results in knowledge of the applicability of CAT and the impact on the logistics value chain of various industrial estates, illustrated by two case studies.
